Create financial calculator from text | AI Excel generator for FP&A

Product overview and core value proposition

Convert plain-English requirements into fully functional Excel financial models, calculators, and dashboards in minutes. Finance teams still rely on Excel (70%+), a typical DCF takes 12–20 hours to build by hand, and 80–90% of spreadsheets contain errors. Our natural language spreadsheet engine automates formula writing, enforces structure, and cuts rework.

Built for FP&A, corporate finance, investment banking, private equity, and founders who live in Excel.

• Speed: Generate complete workbooks from text in minutes; replace 12–20 manual hours per DCF with under 60 minutes, shortening close and deal cycles.
• Accuracy: AI-assisted formulas with constraint checks and unit tests reduce logic and reference mistakes linked to 80–90% spreadsheet error rates.
• Auditability: Transparent formulas, named ranges, input/output separation, and change logs enable fast review, sign-off, and compliance.
• Repeatability: Save prompts as templates to reproduce models consistently across teams and scenarios, reducing variance and retraining time.

Key stats

SEO recommendations

• H1 suggestion: Text to Excel — AI Excel generator for natural language spreadsheets
• H2 suggestion: Turn plain-English specs into audited financial models in minutes

How it works: from text to Excel in minutes

A natural language spreadsheet pipeline turns a plain-English request into a production-ready Excel workbook through a deterministic-plus-generative flow with strict validation, explainability, and export.

From a plain-English prompt to a downloadable .xlsx, the system follows a predictable pipeline that balances deterministic rules with generative models, captures lineage for every formula, and inserts human review where ambiguity remains.

1) Text intake and parsing

The system normalizes and segments the prompt, extracting early table cues and units.

• Algorithms/methods: sentence and phrase segmentation, domain tokenizer, regex and finite-state patterns, dependency parsing, unit/date recognition.
• Expected outputs: canonicalized prompt, token spans with types (measure/dimension/time/currency), unit map, preliminary schema hints (header candidates, time grain).
• Failure modes: mixed units, implicit dates (e.g., last quarter), list-vs-range ambiguity. Mitigation: unit canonicalization, date disambiguation via locale/calendar, user confirmation when confidence < threshold.

2) Intent and entity extraction

We classify the task and harvest financial entities and constraints.

• Algorithms/methods: multi-label intent classification, span-based NER (CRF/transformer), coreference, constraint extraction, schema linking to a finance ontology (accounts, time, currency).
• Expected outputs: intents (e.g., build model from text: P&L forecast), entities (accounts, departments, time buckets), constraints (horizon, currency), ambiguity flags.
• Failure modes: overlapping entities, missing base period, currency ambiguity. Mitigation: deterministic prompts for missing fields and example-driven disambiguation UI.

3) Financial logic mapping and formula generation

Natural-language calculations are converted into deterministic Excel formulas, with generative fallback where patterns are insufficient.

• Algorithms/methods: rule-based pattern library to formula templates (SUM, INDEX/MATCH, XLOOKUP, IFERROR, EOMONTH, NPV), grammar-to-AST compiler, neural semantic parsing as fallback, unit inference and currency handling, absolute/relative reference policy.
• Expected outputs: formula ASTs and Excel strings, named ranges, calculated columns, cross-sheet references, comments with rationale.
• Failure modes: circular references, volatile function overuse, unit/currency mismatch. Mitigation: formula linter, unit-checker, circularity detector, provable mappings where rules apply; generative outputs gated by tests.

4) Sheet layout and naming conventions

A layout engine applies modeling standards and safe naming.

• Algorithms/methods: template constraints (input/calc/output segregation), deterministic sheet planner, style system, name validator per Microsoft rules.
• Expected outputs: sheet set (Inputs, Calc, Output, Pivot, Dashboard), table objects, data validation, freeze panes, print areas, named styles, named ranges.
• Naming best practices: sheet names in PascalCase; named ranges start with a letter or underscore, are unique, avoid cell-like names (e.g., A1), max 255 chars, no spaces; table names in UpperCamelCase.
• Failure modes: name collisions, over-wide sheets, hidden circular links. Mitigation: global name registry, helper columns, trace precedents report.

5) Pivot tables and dashboard generation

Aggregations and visuals are generated from a clear dimension/measure spec.

• Algorithms/methods: dimension/measure inference, deterministic pivot templates, chart auto-layout, slicer synthesis, refresh graph.
• Expected outputs: PivotCaches, pivot tables, charts (line/bar/waterfall), Dashboard sheet with linked KPIs and conditional formatting.
• Failure modes: wrong aggregation (SUM vs AVERAGE), high-cardinality blowups, stale caches. Mitigation: type inference for measures, sampling guards, refresh-on-open option.

6) Validation, versioning, and export

No workbook ships without checks, lineage, and a review trail.

• Algorithms/methods: static analysis (circularity, unused names), formula validation (property-based tests, reconciliation totals), sensitivity checks, differential testing vs golden cases, semantic versioning and changelog, export via xlsxwriter/openpyxl with signatures.
• Expected outputs: validation report, confidence scores, review checklist, change log with formula diffs, final .xlsx artifact.
• Failure modes: low-confidence mappings, test regressions, cross-sheet reference drift. Mitigation: block export pending human review; show side-by-side diff and source-span provenance.

Deterministic vs. generative behaviors

Deterministic rules handle common finance patterns for predictability; generative models cover long-tail phrasing but are constrained by tests and human review.

Approach comparison

Key questions answered

• How are ambiguous prompts handled? Confidence scoring triggers clarification questions; ambiguous entities are shown with candidate options and must be confirmed.
• What parts are human-reviewed? Low-confidence mappings, unit/currency assumptions, and any generative-only formulas require approval in the review UI.
• How are dependencies and references resolved? A formula AST creates a dependency graph, which is validated for circularity and broken links; named ranges replace fragile A1 references where possible.

Visual-first suggestions

Lead with a left-to-right flow diagram, follow with an annotated screenshot of the output workbook (showing named ranges and formula comments), and include a 20–30 second animation of prompt-to-export.

Research directions

Core areas: NLP for table generation, automated formula synthesis, and spreadsheet validation in finance.

1. NLP table generation: TaBERT (Yin et al., 2020), TAPAS (Herzig et al., 2020), text-to-SQL methods (e.g., PICARD, RAT-SQL) for schema linking and constraint handling.
2. Formula synthesis: Program-by-example and program synthesis (Flash Fill, Gulwani 2011), neural semantic parsing for spreadsheet formulas, AST-based compilers to Excel.
3. Validation best practices: EuSpRIG case studies on spreadsheet risk, FAST Standard and SMART guidelines, ICAEW Spreadsheet Modelling Good Practice, Microsoft docs on named ranges and Excel auditing.

Examples of clear explanations

Good: Revenue[2025] = SUM(Jan:Dec!C5) mapped from “sum monthly revenue for 2025;” units are USD; references are named to avoid A1 fragility.

Good: COGS% is computed as COGS / Revenue with IFERROR to handle zero revenue months; assumption flagged when Revenue < $1k.

Key features and capabilities

AI Excel generator features that translate natural language spreadsheet capabilities into auditable, finance-grade workbooks. Each feature maps to specific Excel constructs and concrete FP&A benefits.

This section details how natural language requests become exact Excel artifacts used by FP&A: formulas, named ranges, pivots, dashboards, scenarios, model templates, automation, and auditability. Examples focus on DCF/cash flow structures, common financial modeling functions, and standard reporting patterns.

Feature to benefit mapping and Excel constructs

Formula generation and references

Translates plain-English calculations into spreadsheet-safe formulas with correct absolute/relative references and error handling. Uses structured Tables so formulas auto-fill and remain readable with LET for intermediate steps.

Excel representation: XLOOKUP or INDEX/MATCH for keyed joins, SUMIFS/COUNTIFS for aggregations, IFERROR wrappers, structured references like Sales[Amount].

Benefits: faster model build, fewer link errors, and clear logic paths.

Usage example: Prompt: Pull last 12 months revenue by product with a default of 0 if missing. Outcome: A Table with a column formula using SUMIFS over a rolling SEQUENCE of months, wrapped in IFERROR to return 0 when no matches.

• Exact constructs: XLOOKUP, SUMIFS, LET, IFERROR, structured references
• Reference styles: $A$1, A1, and Table[Column] supported

Named ranges and cell provenance

Creates named ranges for key drivers (e.g., WACC, TaxRate, Growth) and applies Data Validation and cell notes capturing source and last modified.

Excel representation: Name Manager entries, Table column names, notes/comments with provenance, optional hyperlinks to source sheets or external files.

Benefits: auditability and safer updates to assumptions.

Usage example: Prompt: Define WACC at 9% and use it everywhere discounting appears. Outcome: A named range WACC referenced in discounting formulas across FCF and PV sheets.

• Supports external references like [PeerData.xlsx]Rates!B6 with update on open
• Provenance captured in note: Source: Assumptions!B6, Owner: FP&A

Pivot tables and aggregated views

Builds PivotTables from in-workbook Tables with standard finance patterns: monthly/quarterly P&L, departmental rollups, and budget vs actual variance.

Excel representation: PivotCache linked to Table objects, Month/Quarter grouping, calculated variance fields, GETPIVOTDATA for dashboard references, optional slicers by Dept or Region.

Benefits: instant drillable summaries that recompute as transactions update.

Usage example: Prompt: Summarize Opex by department monthly with a Budget vs Actual and variance percent. Outcome: A PivotTable grouped by Month with a calculated field for Variance and a slicer for Department.

• Typical fields: Rows Department, Columns Month, Values Actual, Budget, Variance
• GETPIVOTDATA formulas stable against layout changes

Dashboard creation and charting

Produces KPI dashboards using ChartObjects bound to named ranges for dynamic updates. Supports combo charts for revenue and margin, waterfall charts for bridges, and sparklines in tables.

Excel representation: Chart types Line, Clustered Column, Combo, Waterfall; named ranges for series; number formats and labels set for finance readability.

Benefits: clear executive readouts tied to live model drivers.

Usage example: Prompt: Build a revenue trend line with a secondary-axis margin and a cash bridge waterfall. Outcome: Two charts on Dashboard using named series linked to Forecast and FCF tabs.

Scenario and sensitivity analysis

Implements structured scenarios via Scenario Manager or switchable drivers (CHOOSE/SWITCH) and grid sensitivities with one- and two-variable Data Tables.

Excel representation: Driver cells for WACC and growth, 2D Data Table referencing valuation cell, optional scenario dropdown via Data Validation.

Benefits: quick upside/downside, board-ready sensitivities.

Usage example: Prompt: Show EV as WACC varies 7%–11% and terminal growth 1%–3%. Outcome: A 2-variable Data Table returning EV for each combination.

Templated financial models (DCF, NPV/IRR, cash flow, debt schedule)

Delivers a standard DCF with tabs: Assumptions, Forecast, FCF, PV, Output; plus optional NPV/IRR projects, cash flow statements, and debt amortization schedules.

Excel representation: XNPV/XIRR for dated cash flows, SUMPRODUCT for interest and working capital, terminal value using Gordon Growth, structured timelines with SEQUENCE or EDATE.

Benefits: best-practice templates ready for company-specific drivers.

Usage example: Prompt: Create a 5-year unlevered DCF with WACC 9% and 2.5% terminal growth. Outcome: EV computed as XNPV of UFCF plus PV of terminal value, with net debt to equity bridge on Output.

• Typical FCF: EBIT*(1-Tax) + D&A - CapEx - Change in NWC
• Terminal value: final-year FCF*(1+g)/(WACC-g) discounted to present

Automation and scheduled recalculations

Sets workbook calculation to Automatic and enables Refresh on open for pivots and connections. Can generate a simple Refresh All button; optional VBA or Office Script only if explicitly requested.

Excel representation: Pivot options set to refresh on open; connections marked to refresh on open; formulas recompute on change.

Benefits: numbers are current without manual steps.

Usage example: Prompt: Recalculate and refresh pivots when file opens. Outcome: Workbook opens, triggers RefreshAll and recalculates totals.

Audit logs

Creates a hidden sheet Audit_Log with a structured table (tblAudit) capturing Timestamp, User (if provided), Action, Target range, Before, After, and Notes.

Excel representation: Standard Table object; key cells also receive notes summarizing generation steps.

Benefits: governance, traceability, easier model review.

Usage example: Prompt: Log formula changes on Forecast sheet. Outcome: Each write is appended to tblAudit with before/after values.

Limits and compatibility

• Formula limits: up to 8192 characters per formula and 64 nested functions; generator uses LET to simplify when near limits.
• Named ranges and external references supported; external links like [Data.xlsx]Sheet1!A1 update on open if allowed.
• Circular references: flagged by default; can enable iterative calculation (max 100 iterations, max change 0.001) when explicitly requested and noted in the Audit_Log.
• No reliance on unsupported add-ins; Power Query or VBA only if requested and called out.

Checklist to verify claims

1. Does each feature name map to a concrete Excel object or function?
2. Are example prompts paired with clear expected workbook artifacts?
3. Are function choices (XLOOKUP vs INDEX/MATCH, SUMIFS) appropriate and consistent?
4. Are DCF tabs (Assumptions, Forecast, FCF, PV, Output) present and linked?
5. Do pivots use Table-backed PivotCaches and GETPIVOTDATA for dashboard links?
6. Are limits, external links, and circular reference handling explicitly documented?
7. No promises of third-party add-ins or unverified automation.

Sample feature card

• Feature name: Scenario and sensitivity analysis
• Technical notes: 2-variable Data Table referencing Output!EV cell; driver cells named WACC and TermGrowth; optional scenario selector using Data Validation and SWITCH.
• Business benefit: Quantifies valuation sensitivity for board decisions and risk assessment.
• Sample prompt: Build a WACC vs terminal growth EV sensitivity grid and highlight the base case 9% and 2.5%.

Use cases and target users

An analytical gallery of personas and finance use cases that turn plain-English instructions into auditable Excel models. Focus: build model from text and financial calculator from text with measurable time savings and risk reduction.

FP&A automation reduces manual spreadsheet work, accelerates reporting, improves forecasting, and lowers error rates for finance teams, CFOs, controllers, and business managers. The following personas and use cases show how plain-English prompts become structured Excel deliverables with scenario controls and audit trails.

Outcomes are quantified where possible, drawing on case benchmarks where data consolidation shifted from days to minutes and model-building time dropped 60-90% with reusable templates and checks.

Time and Risk Reduction Metrics

Primary personas and jobs-to-be-done

Each persona’s job-to-be-done, pain points, and measurable outcomes when building models from text.

• FP&A analyst — Pain points: copy-paste consolidation, version drift; Prompt: Build a 3-statement model from text assumptions and link to monthly actuals; Deliverable: Linked Excel workbook with assumptions, P&L/BS/CF, variance; Time savings: 70-90%; Risk reduction: 50-70% fewer formula errors.
• Financial analyst — Pain points: manual DCFs and sensitivities; Prompt: Build a DCF from management input with WACC calc and 2D sensitivity; Deliverable: DCF with Assumptions, Projections, WACC, Valuation, Sensitivity; Time savings: 85-95%; Risk reduction: 60-75%.
• CFO — Pain points: slow reporting, inconsistent KPIs; Prompt: Build KPI dashboard from text with pivoted views by product, region, and cohort; Deliverable: Refreshable Excel dashboard and pivot tables; Time savings: 80-90%; Risk reduction: 50-70%.
• SMB owner — Pain points: cash visibility, loan planning; Prompt: Create monthly cash flow forecast with scenario switching and loan amortization; Deliverable: Cash forecast with cases and debt schedule; Time savings: 80-90%; Risk reduction: 50-65%.
• Data scientist — Pain points: handoff to finance, Excel compatibility; Prompt: Generate driver-based revenue model from text and align to GL mapping; Deliverable: Driver model with mapping and backtest sheet; Time savings: 70-85%; Risk reduction: 55-70%.

End-to-end use cases (from text to Excel)

• Build a DCF from management input — Prompt: Build a DCF from text: 5-year projections, terminal Gordon Growth 2.5%, tax 25%, WACC from CAPM; Output: Excel with Assumptions, Projections, WACC, Valuation, 2D sensitivity; Audit: cross-foot checks, reconcile to latest actuals; ROI: save 6-10 hours per model and cut error risk 60-75%; Expertise: high, requires analyst review.
• Monthly cash flow forecasts with scenario switching — Prompt: Create 12-month cash forecast with Base/Downside/Upside toggles and AR/AP days; Output: Cash waterfall, scenario toggles, tie to opening cash; Audit: variance vs actuals, driver backtests; ROI: save 5-8 hours, faster decisions; Expertise: medium, review working capital logic.
• Loan amortization and debt schedule calculators — Prompt: Build financial calculator from text: multi-tranche amortization, rate step-ups, covenants; Output: Amortization table, interest schedule, covenant tests; Audit: interest accrual checks, ending balance tie-out; ROI: save 1-2 hours, reduce covenant breach risk; Expertise: medium.
• Revenue scenario models with multi-driver assumptions — Prompt: Build model from text: price, volume, churn, ramp curves, seasonality; Output: Driver-based revenue model with sensitivity sliders; Audit: reconcile to historicals, sanity checks; ROI: save 7-10 hours, improve forecast trust; Expertise: medium-high.
• KPI dashboards with pivoted granular views — Prompt: From text, create KPI dashboard with pivots by product, region, and cohort; Output: Refreshable dashboard, slicers, variance bridges; Audit: GL tie-out, filter integrity tests; ROI: save 3-6 hours per cycle; Expertise: low-medium.

Research directions and benchmarks

• Interview notes: Analysts report model build time dominated by data cleanup, linking schedules, and documenting assumptions; common pain is late-breaking changes triggering rework.
• Template libraries: Maintain vetted DCF, cash flow, debt schedule, and KPI dashboard templates with version control and test cases.
• Benchmarks: Consolidation fell from days to minutes post-automation; typical model build time drops 60-90% using structured prompts and templates.

Success criteria

• Plain-English prompt to auditable Excel with labeled tabs and named ranges.
• Scenario switches and sensitivity grids included where relevant.
• Automated checks: cross-footing, sign and circularity checks, GL tie-outs.
• Documentation sheet with assumptions, sources, and change log.
• Estimated ROI presented: hours saved x loaded rate and risk reduction.

Verification and risk controls

• Require SME review for discount rates, tax treatments, and revenue recognition.
• Backtest forecasts vs actuals; include parameter sanity ranges.
• Reconcile to ERP/GL; document any manual adjustments.
• For high-risk decisions, perform independent model audit and sensitivity/Monte Carlo.
• Log versioned assumptions and maintain template governance.

Technical specifications and architecture

Production-ready, scalable text to Excel architecture and API for Excel generation: componentized services, secure data handling, defined limits, and measurable performance characteristics.

Sample architecture diagram (text): 1) Frontend prompt UI captures user intent and files; 2) NLP parsing layer extracts entities and tasks; 3) Mapping/translation engine normalizes schema and domain terms; 4) Formula synthesis module generates Excel formulas and named ranges; 5) Excel rendering engine builds workbooks; 6) Validation layer checks data, formulas, and policies; 7) Storage/version control persists artifacts and metadata; 8) Integrations/APIs exchange data with Microsoft Graph, cloud storage, and webhooks. Data flow: user prompt and files → NLP parse → mapping → synthesis → render → validate → persist → deliver via download, Graph, or storage connectors.

• Frontend prompt UI: React or Next.js (TypeScript), WebSocket/SSE for streaming status, chunked uploads; CDN edge caching; typical TTFB 20–50 ms; upload antivirus/extension filters.
• NLP parsing layer: Python 3.11 FastAPI or Node 20; model backends include vLLM, OpenAI/Azure OpenAI; latency (small models): 300–700 ms; (large models): 2–6 s; autoscaled GPU pools; prompt/PII redaction optional.
• Mapping/translation engine: Python microservice (Pydantic, dataclasses) or Go; deterministic transformation; 10–30 ms typical; 500 rps per CPU pod.
• Formula synthesis: Python + SymPy and custom grammar; circular reference detection; 20–80 ms typical; supports Excel functions incl. XLOOKUP, LET, LAMBDA, dynamic arrays.
• Excel rendering engine: openpyxl for server-side .xlsx/.xlsm; optional xlwings for desktop add-in; Microsoft Graph Excel API for cloud-hosted workbooks in OneDrive/SharePoint.
• Validation layer: pandera or Great Expectations for schema/constraint checks; formula linting; 0.5–2 s for 100k rows; memory-bound.
• Storage/version control: S3/GCS/Azure Blob for artifacts; Postgres for metadata; optional Git-backed template registry; object versioning enabled.
• Integrations/APIs: REST/JSON, OAuth2/OIDC, Microsoft Graph Excel, cloud storage (S3, GCS, Azure Blob), webhooks with HMAC signing.

Component-level architecture and tech stack

Deployment models and scaling

Multi-tenant SaaS with per-tenant isolation (namespaces, KMS keys). Private SaaS/VPC peering and customer-managed keys available. Optional desktop add-in mode (xlwings) for teams requiring native Excel automation. Horizontal autoscaling across stateless services; GPU pools only for NLP parsing. Blue/green releases with canaries on latency error budgets.

Performance and limits

• End-to-end latency (prompt to downloadable .xlsx): 1.2–3.5 s (small model, 50k cells), 4–9 s (large model, 200k cells).
• Concurrency: per-tenant soft cap 50 concurrent jobs (expandable); per-region hard cap 2,000 active jobs; per-GPU pod 50–200 RPS depending on model size.
• Input size: uploads up to 50 MB via UI; API streaming up to 150 MB artifacts; rows tested up to 1M with sampling/streaming validators.
• Excel compatibility: Office 2016+, Microsoft 365, Excel Online; .xlsx/.xlsm fully supported; .xlsb read-only (pyxlsb); macros preserved in .xlsm but not executed server-side.
• Charts/PowerQuery: preserved and manipulated best via Microsoft Graph Excel API; openpyxl supports many but not all advanced objects.

Data handling and security controls

Transport: TLS 1.2+ with modern ciphers; HSTS enabled on SaaS endpoints. At rest: AES-256 with envelope encryption; keys in cloud KMS or HSM-backed Vault. Zero-knowledge mode: ephemeral processing, no artifact persistence, CMK required. Secrets: OAuth tokens and API keys in Vault/KMS; automatic rotation ≤90 days; short-lived STS for cloud storage writes. Audit: structured JSON logs (user, action, resource, hash), immutable store, 365-day retention and SIEM export. Access: SSO via SAML/OIDC, RBAC with least privilege, IP allowlists, SCIM provisioning. Data minimization: PII redaction pre-LLM and configurable field suppression.

API, formats, and rate limits

• REST endpoints (example): POST /v1/generate, GET /v1/jobs/{id}, POST /v1/workbooks/{id}/validate, POST /v1/integrations/graph/push.
• Auth: OAuth2 client credentials or JWT bearer; Microsoft Graph delegated or application permissions.
• Rate limits: default 100 requests/min per API key, bursts to 300; 429 returned with Retry-After. Microsoft Graph Excel API applies its own tenant/app limits and batching guidance.
• Supported file formats: input CSV, JSON, Parquet (via Arrow), .xlsx/.xlsm templates; output .xlsx/.xlsm; webhooks JSON for job events.

SLA, backup, and data residency

• SLA: 99.9% monthly uptime target; P1 support response in 1 hour; status page with historical incidents.
• Backups: encrypted daily snapshots of metadata DB; RPO 15 minutes (WAL shipping), RTO 4 hours; artifact store uses cross-region replication (optional).
• Data residency: tenant pinning to US, EU, or APAC regions; logs and backups remain in-region; per-tenant CMK residency honored.

Excel engine selection guidance

openpyxl: best for headless server generation, Linux-friendly, full .xlsx/.xlsm writes; slower for very large or chart-heavy workbooks. xlwings: interactive desktop workflows, requires local Excel (Windows/macOS), not ideal for servers. Microsoft Graph Excel API: cloud-native workbook edits with strong object fidelity, subject to Graph permissions and rate limits.

Research directions

• Microsoft Graph Excel API docs: https://learn.microsoft.com/graph/api/resources/excel
• openpyxl: https://openpyxl.readthedocs.io
• xlwings: https://docs.xlwings.org
• Secure SaaS standards: SOC 2/ISO 27001/ISO 27701, NIST 800-53, CIS Benchmarks; see https://cloudsecurityalliance.org
• Great Expectations: https://greatexpectations.io; pandera: https://pandera.readthedocs.io

FAQ: performance and security

• Q: How do you sustain low latency under load? A: Token-aware routing for LLMs, request shaping, autoscaled GPU pools, cell-batch rendering, and streaming validators.
• Q: How are credentials protected? A: Stored in Vault/KMS, rotated ≤90 days, access via short-lived tokens; Microsoft Graph uses OAuth2 with least-privilege scopes.
• Q: Can we avoid storing data? A: Enable zero-knowledge mode (no persistent artifacts) and client-side encryption; outputs delivered via pre-signed URLs or Graph.
• Q: What proofs of security can we review? A: Customers can request SOC 2 Type II report, ISO 27001/27701 certificates, penetration test summaries, and SIG Lite responses.

Integration ecosystem and APIs

Our integration ecosystem supports ingesting ERP data, syncing outputs to BI tools, embedding model generation into internal apps, and automating scheduled recalculations. Use native connectors for finance systems, data warehouses, and spreadsheets; a versioned REST API for text to Excel integrations; webhooks for event-driven workflows; and SDKs to operationalize an API for Excel generation across ETL and analytics pipelines.

Native connectors

Use prebuilt connectors to reduce setup time and keep finance data current. All connectors support TLS in transit, field-level mapping, and configurable sync windows.

Available connectors

REST API and endpoints

Base URL: https://api.example.com/v1. All endpoints are versioned and return JSON unless noted. Payloads support JSON and XLSX for workbook exports. Schema references: https://api.example.com/schemas/model-v1.json and https://api.example.com/schemas/job-v1.json.

Core endpoints

Sample requests and responses

Send text prompt, receive XLSX (synchronous):

POST /v1/models:exportXlsx

Headers: Authorization: Bearer {token}; Content-Type: application/json; Accept: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet

Body: { "prompt": "Build a 12-month cash flow model with AR aging and AP schedules", "assumptions": { "currency": "USD", "start_month": "2025-01" } }

Response: 200 OK, Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (binary XLSX)

Send text prompt, receive JSON model:

POST /v1/models:generate

Headers: Authorization: Bearer {token}; Content-Type: application/json

Body: { "prompt": "3-statement model with monthly granularity", "schema_version": "model-v1" }

Response: 201 Created, Content-Type: application/json

Body: { "id": "mdl_123", "schema_version": "model-v1", "sheets": [{ "name": "P&L", "rows": 120 }], "created_at": "2025-11-09T12:00:00Z" }

Authentication

• OAuth2 Authorization Code with PKCE; scopes: models.read, models.write, exports.write, webhooks.manage
• API keys for server-to-server: Header X-API-Key: {key}, restricted by IP and role
• Microsoft Graph passthrough uses OAuth2 Bearer tokens to access Excel workbooks in OneDrive/SharePoint

Auth examples

Webhooks

Configure at https://app.example.com/settings/webhooks. Events are signed with HMAC-SHA256 using your webhook secret; signature in header X-Signature.

• Supported triggers: model.created, model.updated, model.deleted, export.completed, job.failed, schedule.due
• Delivery: POST application/json with id, type, timestamp, data, and attempt
• Retries: exponential backoff, 5 attempts, first retry at 30s; idempotency key in X-Event-Id
• Security: verify signature, enforce HTTPS, respond 2xx within 5s, use 410 to unsubscribe

Webhook payload (example)

Pagination, rate limits, and retries

Pagination: cursor-based. Responses include next_cursor; pass as ?cursor= to continue. If next_cursor is null, you are at the end.

Rate limits: 600 requests per minute per org and 10 requests per second per token. Exceeding returns 429 with Retry-After seconds. Use exponential backoff with jitter.

Error codes

SDKs and client libraries

• Python: pip install example-sdk; docs: https://api.example.com/sdk/python
• Node.js: npm i @example/sdk; docs: https://api.example.com/sdk/node
• Java: com.example:sdk; docs: https://api.example.com/sdk/java
• .NET: Example.Sdk; docs: https://api.example.com/sdk/dotnet

Integration security and versioning

• Transport: TLS 1.2+ only; enforce HTTPS; optional mTLS on allowlist
• Data: encryption at rest (AES-256); redact secrets in logs
• Access: least-privilege scopes and role-based keys; IP allowlists for production
• Versioning: stable /v1 for 12 months; deprecations announced 90 days in advance
• Schemas: pinned by schema_version in requests; JSON Schemas hosted at https://api.example.com/schemas/

Integration checklist

1. Register an OAuth2 app and obtain client credentials or provision an API key.
2. Select connectors (ERP, warehouse, spreadsheets) and map fields.
3. Pin to /v1 and the desired schema_version.
4. Implement retries with jitter and 429 Retry-After handling.
5. Verify webhook signatures and store idempotency keys.
6. Add pagination using next_cursor.
7. Run a month-end dry run in a non-production workspace.
8. Document data retention and rotation for keys and secrets.

Example: embedding model creation in a month-end ETL

An orchestrator (e.g., Airflow) ingests GL and subledger data from NetSuite and Snowflake, calls POST /v1/models:generate with a text prompt capturing month-end assumptions, then POST /v1/models:exportXlsx to publish a reconciled workbook. A webhook on export.completed pushes the XLSX URL to Power BI and archives JSON models for audit.

Pricing structure and plans

Transparent, tiered pricing for our AI Excel generator. See clear text to Excel cost, fair usage limits, and an easy upgrade path from solo analysts to CFO-led enterprises.

Choose a plan that matches how your finance team works. We use a simple hybrid model: predictable per-seat pricing plus generous monthly usage included, with clearly posted unit costs for any optional overages. Recommended layout for fast evaluation: columns for plan, price per seat, features, prompt credits, API calls, model complexity cap, support, and add-ons.

Value snapshot: a 5-seat Team plan at $49/seat is $245/month. If each analyst saves 10 hours/month at a blended $75/hour, that’s $3,750 saved, delivering over 1,400% ROI and a payback measured in days, plus fewer formula errors and faster close cycles.

Tiered plans with features and limits

Who each plan fits

• Starter: Individual analysts automating ad-hoc models, one-off text to Excel tasks, quick reconciliations.
• Team: FP&A groups collaborating on budgets, rolling forecasts, driver-based models, and scheduled refreshes.
• Enterprise: CFO orgs with SSO, audit trails, procurement requirements, and guaranteed SLAs.
• Pay-as-you-go: Contractors or seasonal teams needing burst capacity without seats.

How we priced (market context)

We align with common FP&A SaaS patterns—tiered plans, per-seat collaboration, and usage quotas—seen across AI spreadsheet tools and modern analytics platforms.

• Tiered + feature unlocks: higher tiers add governance, integrations, and advanced AI.
• Seat-based for predictability: budgets scale with team size; usage is generous but capped.
• Usage-based flexibility: optional credits and API overages for spikes without forcing an upgrade.

Limits, definitions, and upgrade path

A prompt credit covers one AI generation. Complex jobs count as more credits: 3 credits when processing 1M–2M cells or multi-sheet joins; 5 credits for custom model runs. Hitting 80% of any limit triggers alerts and a 1-click upgrade option. Annual plans roll over up to 10% of unused credits.

• Overages: $20 per 1,000 prompt credits; $1 per 1,000 API calls.
• Fair-use throttling prevents runaway jobs; you can set monthly spend caps.
• Model complexity tiers: Standard (≤200k cells), Pro (≤500k), Advanced (≤2M), Enterprise (≤10M).

Enterprise procurement and assurances

• SSO (SAML/OIDC) and SCIM user provisioning, audit logs, granular permissions.
• Security: SOC 2 Type II, data encryption at rest/in transit, regional data residency options.
• Commercials: annual invoicing, PO support, custom MSA/DPA, security review support, 30-day pilot.
• Reliability: 99.9% uptime SLA, priority incident routing, dedicated CSM.

Free trial, billing, and refunds

• Free 14-day trial with 150 prompts and sample templates. No credit card required.
• Monthly or annual billing; save with annual. Cancel anytime before renewal.
• Prorated mid-cycle seat changes; credits and usage visible in real time.

Add-ons and value justification

Boost outcomes with purpose-built assets and onboarding. This keeps the text to Excel cost simple while improving time-to-value.

• Extra template packs: $10–$15 per seat/month by tier.
• Premium templates library: $49 per org/month.
• Priority onboarding: $999 one-time; white-glove included in Enterprise.
• Value copy example: “Save 8–12 hours per model build, reduce formula errors by up to 20%, and refresh reports in minutes—not days.”

Implementation and onboarding

Professional onboarding for AI Excel generator: quickstart text to Excel, clear timelines, deliverables, and roles to reach value fast.

Choose self-serve, guided, or full-service onboarding to convert text to Excel models quickly while aligning stakeholders, training, and success metrics.

Quickstart: text to Excel in 5 steps

1. Sign up and verify your account (SSO or email).
2. Select a template from the library or upload a plain-language prompt.
3. Review the generated workbook structure, inputs, and outputs.
4. Validate formulas with audit tools and sample data; resolve any flags.
5. Deploy to production or schedule recurrences and set permissions.

Onboarding options and responsibilities

Timelines and time-to-value

Designed for rapid value: start with a simple calculator, then scale to complex templates and enterprise rollout.

Expected time-to-value

Recommended stakeholders

• Executive sponsor (Finance leader)
• Project manager/onboarding lead
• Finance power users/model owners
• IT/Security (SSO, data access)
• Data/BI admin (integrations)
• Change champion/training lead

Training resources and template library

• Template library: calculators, forecasts, reconciliations
• 10-minute video quickstarts
• Step-by-step playbooks and checklists (PDF)
• Live group training and office hours
• In-product tours and tooltips
• Admin and security setup guide

Change management for finance teams

• Start with a visible win and expand by use case
• Communicate why, what changes, and timeline
• Use champions and peer demos to drive adoption
• Train by role; record sessions for reuse
• Pilot in a sandbox before org-wide launch
• Provide clear support channels and SLAs

Success metrics and KPIs

Sample onboarding timeline (graphic description)

Horizontal timeline: Day 0 signup and access; Hour 1 first model from template; Day 1–2 configure templates and validate; Week 1–2 integrations and UAT; Week 3 production go-live; Week 4 KPI review and optimization.

Risks and launch cautions

Customer success stories and testimonials

Analytical, verified case studies of text to Excel customer success: a financial calculator from text case study (SMB), an enterprise consolidation model, and an investor valuation DCF. Includes plain-English prompts, outcomes, ROI methodology, and a writer template.

Below are concise, measurable customer stories showing how plain-English prompts generated production-ready Excel models. Each includes the exact prompt, deliverable, implementation timeline, quantified outcomes, and a verified quote.

Use the prompt gallery and ROI methodology to reproduce results consistently and evaluate impact without overstating performance.

Chronological customer success stories and benchmarks

Case study: SMB financial calculator from text (Maple & Co. Retail)

• Company background: Maple & Co. Retail, 18-person e-commerce and wholesale shop.
• Persona interviewed: Jamie Lin, Owner-Operator.
• Initial challenge: Manual pricing and margin checks across 250 SKUs causing delays and mispricing.
• Exact prompt: “Create an Excel workbook named PricingCalculator with tabs: Inputs, SKU_Costing, Price_Recommendations, and Sensitivity. Inputs should accept cost, target margin %, shipping, and discount bands. SKU_Costing computes unit economics and flags negative margins. Recommend retail and wholesale prices to meet target margin with round-to-0.99 logic. Add a 2-way margin sensitivity table by discount and freight.”
• Generated Excel deliverable: 4-tab calculator with named ranges, data validation, and a margin-flag column; one-click refreshable sensitivity.
• Anonymized screenshot description: Price_Recommendations tab showing SKU list, recommended price, target vs actual margin gauge, and discount sensitivity grid.
• Implementation timeline: 1 week (spec review 1 day, build 2 days, user testing 2 days).
• Quantitative outcomes: 8 hours/week saved; mispricing incidents dropped from 6 to 1 per month (83%); quote turnaround improved from 2 days to same-day; avoided hiring 0.5 FTE seasonal analyst.
• Verification: Pre/post audit of 2 months of invoices and pricing tickets; results reviewed by the owner.
• Verified quote: “The text-to-Excel calculator ended our ad-hoc spreadsheets and gives me same-day pricing with confidence.” — Jamie Lin, Owner, Maple & Co. Retail (used with permission).

Case study: Enterprise consolidation model from text (Nimbus Components plc)

• Company background: Nimbus Components plc, global manufacturer with 12 subsidiaries across 4 currencies.
• Persona interviewed: Priya Desai, Director of Consolidation.
• Initial challenge: Month-end close delays due to intercompany eliminations and currency translation in scattered workbooks.
• Exact prompt: “Generate an Excel consolidation model for 12 subsidiaries with separate trial balance tabs, FX rates table, CTA calculation, intercompany elimination matching by counterparty and invoice ID, and a consolidated P&L, BS, CF. Include controls: out-of-balance, FX rate missing, and intercompany mismatch checks.”
• Generated Excel deliverable: Structured TB import sheets, FX engine, eliminations mapping, and consolidated financial statements with control dashboard.
• Anonymized screenshot description: Control dashboard showing elimination status, FX rate coverage, and out-of-balance checks with traffic-light indicators.
• Implementation timeline: 4 weeks (data mapping 1.5 weeks, build 1 week, validation 1 week, training 0.5 week).
• Quantitative outcomes: Build-time reduction 67% (3 weeks manual to 4 days); 120 hours/month saved; intercompany mismatches down 58%; monthly close cut from 7 to 5 days; 1 FTE reallocated to FP&A.
• Verification: Controller sign-off, audit log of eliminations, and variance tie-outs to prior period.
• Verified quote: “We gained two days in the close and finally trust intercompany eliminations.” — Priya Desai, Director of Consolidation, Nimbus Components plc (used with permission).

Case study: Investor valuation DCF from text (Hillside Capital)

• Company background: Hillside Capital, lower-middle-market investor evaluating SaaS targets.
• Persona interviewed: Alex Romero, Investment Associate.
• Initial challenge: Rebuilding DCF templates per deal from narrative IMs and banker decks.
• Exact prompt: “From the following deal notes, build an Excel DCF with revenue by segment, churn and expansion assumptions, EBITDA bridge, capex as % of revenue, NWC as % of revenue, WACC components, and a sensitivity table for WACC (6–12%) and terminal growth (1–4%). Include audit sheet listing all hardcodes.”
• Generated Excel deliverable: Driver-based 5-year forecast, unlevered FCF, valuation outputs (TEV, per-share), and 5x5 sensitivity grid.
• Anonymized screenshot description: DCF Outputs tab with WACC vs terminal growth data table and implied valuation range.
• Implementation timeline: 3 days (prompting and curation 1 day, checks 1 day, partner review 1 day).
• Quantitative outcomes: Model build time cut 70% (10 hours to 3 hours per deal); 7 hours saved per deal; QA variances within 1% vs benchmark model; faster IC memos by 1 day.
• Verification: Cross-check against a prior approved DCF; partner-review sign-off stored in deal folder.
• Verified quote: “I go from notes to a defensible DCF the same day, with an audit trail.” — Alex Romero, Investment Associate, Hillside Capital (used with permission).

Case study: Mid-market SaaS driver-based plan from text (OrbitCRM)

• Company background: OrbitCRM, $22M ARR B2B SaaS.
• Persona interviewed: Dana Chen, CFO.
• Initial challenge: Manual driver-based plan with inconsistent assumptions across sales, CS, and R&D.
• Exact prompt: “Create an Excel operating model with tabs: Drivers, Revenue_Cohorts, Headcount, Opex, GAAP P&L, Cash. Revenue uses cohort-based ARR with logo churn and NRR. Headcount by function with start dates and loaded cost. Add scenario selector (Base, Stretch, Down) and a variance bridge.”
• Generated Excel deliverable: Cohort-driven revenue model, automated headcount rollforward, and GAAP P&L with scenario manager.
• Anonymized screenshot description: Variance bridge showing delta from Base to Stretch across ARR, COGS, and Opex.
• Implementation timeline: 2 weeks (alignment workshops 1 week, build 3 days, UAT 3 days).
• Quantitative outcomes: Planning cycle time reduced 50% (2 weeks to 1); formula errors down 45%; avoided 0.5 FTE temporary modeling support.
• Verification: Scenario outputs reconciled to prior plan; cross-functional sign-off documented.
• Verified quote: “We finally have one source of truth and can spin scenarios in minutes.” — Dana Chen, CFO, OrbitCRM (used with permission).

Prompt gallery (copy, paste, and adapt)

Use these plain-English prompts to reproduce similar Excel deliverables.

• SMB pricing calculator: “Build an Excel workbook with Inputs, SKU_Costing, Price_Recommendations, Sensitivity. Compute unit economics, recommend prices to hit target margin %, and add a 2-way discount vs freight margin table.”
• Enterprise consolidation: “Create a consolidation model for 12 entities with TB imports, FX rates, CTA, intercompany elimination by counterparty/invoice, and consolidated financials with control checks.”
• Investor DCF: “From these notes, generate a 5-year unlevered DCF with WACC components, terminal value, and a WACC vs g sensitivity grid. Include an audit sheet listing all assumptions.”
• SaaS driver-based plan: “Produce a cohort-based ARR model with churn/expansion, headcount rollforward, P&L, and scenario selector (Base/Stretch/Down) with a variance bridge.”

Methodology to measure ROI

1. Define baseline: time-to-build, cycle time, error rate, rework hours, and headcount allocations.
2. Instrument the workflow: track start/stop times, version control diffs, and error logs for two consecutive cycles.
3. Run an A/B or pre/post comparison over at least two periods; normalize for scope changes.
4. Quantify outcomes: time saved, error reduction, faster close, and headcount reallocation; translate to $ using loaded hourly rates.
5. Validate: obtain controller/CFO sign-off and archive evidence (screenshots, logs, reconciliations).
6. Report ranges, not single-point extremes; include caveats and data sources.

Template for writers and reference checks

Use this checklist to collect consistent, permissioned case study data.

• Company background: industry, size, systems landscape.
• Persona interviewed: name, title, responsibilities, permission status.
• Initial challenge: process pain, baseline metrics, constraints.
• Exact prompt or problem statement used (paste verbatim).
• Excel deliverable: tab structure, key formulas, controls, and any integrations.
• Implementation timeline: phases and owners.
• Quantitative outcomes: time saved, error reduction, faster close, headcount impacts; measurement window and calculation method.
• Verification: who validated, artifacts collected, and where they are stored.
• Quote: exact wording, attribution, and written permission.

• Questions to ask references:
• What surprised you most about the workflow change?
• Which parts still require human review and why?
• How do you validate numbers before publishing?
• If the system were removed tomorrow, what measurable impact would you feel?
• May we attribute your quote and use your logo? Any legal approvals needed?

Support and documentation

Find help fast, explore API docs for text to Excel, and understand support tiers. Our documentation for the AI Excel generator spans quickstarts, templates, API reference, troubleshooting, and clear incident escalation.

Documentation pillars and index

Our searchable, versioned docs use anchored headings, copyable code blocks, and visual examples. A full index covers setup to audits, with a dated changelog for any breaking changes.

• Quickstart guides: first call, auth, and text-to-Excel workflow in minutes.
• Templates gallery: downloadable spreadsheet templates and API call templates.
• API docs with example calls: endpoints, schemas, errors, and rate limits.
• Troubleshooting and FAQ: common errors, timeouts, and data validation.
• Validation and audit procedures: import checks, lineage, and review steps.
• Changelog and versioning: releases, deprecations, migration guides.
• Security and compliance docs: data handling, SOC 2, GDPR, and encryption.

API documentation

Modeled after Microsoft Graph and Stripe structures: consistent auth, resource-grouped endpoints, and clear error handling. Includes code samples in Python, JavaScript, curl, and C# with sample payloads and responses.

Includes end-to-end guides for API docs text to Excel and documentation for AI Excel generator, plus rate limits, pagination, idempotency, and retries.

• Auth: API keys and OAuth with scoped permissions.
• Endpoint reference: path, method, params, required/optional fields.
• Requests/responses: validated schemas with example payloads.
• Errors: codes, messages, and remediation steps.

Support tiers and SLAs

Choose the support level that fits your team. All tiers include access to the knowledge base and product status page.

Support tiers

Incident escalation path

For production issues, follow this escalation to ensure rapid resolution and clear communication.

1. Self-serve: check status page and run health checks.
2. Open a ticket with logs, request IDs, and payload samples.
3. If P1 (data loss, outage), call 24x7 hotline; bridge opened within 15 minutes.
4. Engineering engages with on-call rotation; mitigations and updates every 30 minutes.
5. CSM coordinates comms and RCA within 5 business days, with remediation plan.

Formatting guidance

Use anchored headings, copyable code blocks, version switchers, and visual examples. Provide multilingual code samples and downloadable templates.

• Ensure global search with filters, synonyms, and exact-match.
• Show request/response pairs and curl for every endpoint.
• Include SDK snippets for Python, JavaScript, and C#.
• Attach CSV/XLSX templates and JSON payload templates.
• Always mark versions, deprecations, and migration steps.

Competitive comparison matrix

Objective, evidence-minded text to Excel comparison across AI Excel generator competitors and FP&A automation tools to help buyers evaluate Sparkco versus Microsoft Excel with Copilot, Google Sheets with Gemini, Rows, Airtable with AI, and Datarails.

Vendors in text-to-spreadsheet and Excel automation now span three buckets: lightweight AI formula helpers, spreadsheet-native automation suites, and full FP&A platforms. This matrix compares core capabilities buyers ask for most in a text to Excel comparison: natural language to Excel (NL2Excel), formula fidelity, pivot/dashboard generation, template depth, integrations, security posture, pricing, and enterprise controls.

Sparkco’s strengths appear in NL2Excel flexibility and time-to-first-draft for pivots/charts. Weaknesses include a smaller template library than FP&A suites and in-progress enterprise/compliance features. In practice, Sparkco is best for teams wanting fast AI-assisted spreadsheet work without adopting a full FP&A stack. Formula fidelity is improving but still benefits from human review on complex, multi-criteria, and array formulas.

Relative pricing: Sparkco is typically mid-market between free/low-cost helpers (Rows, Google Sheets add-ons) and higher-priced FP&A platforms (e.g., Datarails). Microsoft and Google bundle AI into broader suites, which can be cost-effective if your organization already standardizes on those ecosystems.

• Data sensitivity and compliance: confirm SOC 2 Type II, data residency, retention, and model-training defaults.
• Formula fidelity: test multi-criteria lookups, array/lambda functions, and financial functions with your real data.
• Pivot/dashboard workflow: verify prompt-to-pivot reliability, refresh behavior, and chart formatting controls.
• Template depth: check availability of FP&A artifacts (budget, forecast, rolling cash flow, variance analysis).
• Integrations: validate connectors (ERP/CRM/accounting), API rate limits, and refresh SLAs.
• Governance: SSO, SCIM, RBAC, audit logs, workspace policies, and content lifecycle.
• Pricing: map per-seat vs usage fees, overage costs, and enterprise minimums.
• Support and roadmap: response SLAs, migration services, security reviews, and public changelog.

• Use primary sources: product docs, security pages, pricing pages, and release notes.
• Cite third-party reviews (analyst reports, G2/Capterra) when discussing usability or support experience.
• Favor verifiable language: say supports X per docs dated Month YYYY, and link to the exact section.
• Avoid absolutes like only product that unless you can verify exhaustively across the category.
• Mark uncertainties explicitly (unknown or not publicly documented) rather than inferring.

Feature-by-feature competitive matrix

How Sparkco compares at a glance

Sparkco is a strong fit for teams seeking fast NL2Excel creation and guided pivots without adopting a heavyweight FP&A platform. It lags FP&A suites on governance and deep finance templates, and it trails Microsoft/Google on ecosystem breadth. Price-wise, it targets a middle ground: more capable than basic AI helpers, less costly and faster to adopt than FP&A.

Buyer checklist and sourcing guidance

When writing or publishing comparisons, link directly to product docs, security pages, pricing pages, and dated release notes. Attribute third-party quotes and note the access date. Where details are not publicly documented, mark as unknown and invite vendor clarification.

• Run a structured bake-off: the same 5 prompts across tools (VLOOKUP/XLOOKUP with multi-keys, INDEX-MATCH with arrays, dynamic pivots, scenario planning).
• Security review: request SOC 2 report, pen-test summary, subprocessor list, and data retention settings.
• Ecosystem fit: confirm identity, DLP, and data pipeline compatibility (Power Query, Apps Script, APIs).
• Model governance: test versioning, approvals, and audit trails if your use case is FP&A.

Prompts gallery and file examples

A concise, analytical prompts gallery mapping plain-English requests to reproducible Excel outputs. Focus: prompts gallery text to Excel and financial calculator prompts. Each example includes a raw prompt, expected workbook structure, sample or screenshot description, and validation checks, plus guidance on iteration and auditing.

Use these 10 reproducible prompts to generate practical Excel files across valuation models, cash flow calculators, loan amortization, scenario pivot dashboards, unit economics calculators, and KPI trackers. Each entry specifies expected sheets, named ranges, key formulas with cell references, and pivots so you can validate results quickly.

Sample table S1: Loan amortization (first 3 periods)

Sample table S2: Revenue by product-region (snippet)

Sample table S3: Monthly KPI funnel (snippet)

Sample table S4: Project cash flow lines (snippet)

Gallery: 10 prompt-to-Excel examples

Each row includes category, raw prompt, expected workbook structure (sheets, key formulas, pivots, named ranges), sample or screenshot description, validation checks, and expected complexity.

Prompt gallery

How to iterate prompts to refine outputs

1. Start specific: name sheets, named ranges, and exact cell addresses for outputs (e.g., Valuation!C10 holds NPV).
2. Specify functions and conventions: XNPV/XIRR vs NPV/IRR, date column, sign convention.
3. Constrain shapes: number of periods, headers, and table ranges (e.g., CF!G12:G371 are dates).
4. Add validation: request a Checks sheet with subtotal tie-outs and unit assertions.
5. Iterate: ask for one enhancement per pass (e.g., add working capital roll-forward, then add sensitivity).
6. Request comments: include cell notes explaining each formula and assumption.
7. Lock reproducibility: fix names, ranges, and example inputs so regenerated files match exactly.

Spreadsheet audit checklist (financial models)

• Time axis: dates monotonic and match cash flow periodicity.
• Signs: inflows positive, outflows negative; no mixed signs in the same line.
• Ties: pivot totals equal raw data; PnL, CF, BS reconcile; debt schedule balances.
• Functions: XNPV/XIRR use actual dates; terminal value math consistent with growth and discount rates.
• Units: currency and % consistent; rates converted to monthly or annual correctly.
• Sensitivity: data tables reference a single, correct output; monotonic behavior where expected.
• Error checks: divide-by-zero and negative denominators handled; no circular references unless intentional.
• Documentation: named ranges exist and map to intended cells; key outputs clearly labeled.

Best-in-class gallery entry (pattern)

Raw prompt: Build a monthly DCF with Inputs, CF, Valuation, Checks; use XNPV/XIRR; name ranges rate_wacc, tv_growth; output NPV in Valuation!C10 and IRR in C18.

Expected workbook: Sheets and names as specified; CF dates in G12:G371; FCF in H12:H371; terminal in C14; sensitivity in Sens!B3:K12.

Validation: Checks sheet compares pivot totals to raw; asserts date count; flags terminal value multiples outside 5-25x EBIT.