In-sheet plain-language spreadsheet analysis workspace

For

Analysts and finance teams working in spreadsheets who need evidence-backed analysis and reporting

Solves

Spreadsheet work is split across several AI add-ons and chat tools, so formulas, cleaning, charts and reports are produced without a reviewable trail.

Delivers

Reviewed formulas, cleaned tables, charts and reports linked to source cells

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce tool switching and manual spreadsheet work while keeping a reviewable record of every change.

1. Assist inside the sheet without switching tools.
2. Generate formulas from plain-language requests.
3. Explain existing and generated formulas in simple terms.
4. Clean and standardize messy spreadsheet data.
5. Build charts and visualizations from selected ranges.
6. Generate formatted sheets, scenario tabs and comparison tables.
7. Explain proposed edits and require accept or revert before applying.
8. Keep a visible edit audit trail for inspection.
9. Import CSV and XLSX and extract tables from PDFs.
10. Summarize key insights and generate reports.
11. Provide customizable workflow automation templates.
12. Support real-time collaboration on shared projects.
13. Generate and explain VBA and Apps Script code.
14. Construct regular expressions for validation and pattern matching.
15. Provide searchable formula and shortcut directories.
16. Handle locale separators, date formats and array formulas.
17. Offer custom functions for extract, summarize and categorize.
18. Extract structured data from dropped PDFs inside the workbook.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed analysis package with source references and unresolved questions.

Everything these tools do, in one app

• In-sheet AI assistance Lets users interact with AI directly inside the spreadsheet without switching to another tool.Found in ChatGPT for Google Sheets, Pane, Melder - AI for Excel
• Natural language formula generation Converts plain-language requests into working spreadsheet formulas.Found in ChatGPT for Google Sheets, ManyExcel, Excel Formula Bot and 2 more
• Formula explanation Explains existing or generated formulas in simple terms so users can understand them.Found in ChatGPT for Google Sheets, Excel Formula Bot, Sheetsbase and 1 more
• Data cleaning and transformation Cleans and standardizes messy spreadsheet data automatically.Found in ChatGPT for Google Sheets, ManyExcel, Pane
• Chart and visualization creation Builds charts and visualizations from spreadsheet data.Found in Pane, AI Assist, Melder - AI for Excel
• Model and scenario building Generates formatted sheets, scenario tabs, and comparison tables from plain instructions.Found in ChatGPT for Google Sheets
• Permission-before-change workflow Explains proposed edits and lets users accept or revert changes before they are applied.Found in ChatGPT for Google Sheets
• Edit audit trail Provides a visible history of changes so users can inspect and adjust what the AI did.Found in ChatGPT for Google Sheets, Pane
• File import and PDF extraction Imports CSV, XLSX, and extracts tables from PDFs to bring data into the spreadsheet.Found in Pane, Excel Formula Bot, Melder - AI for Excel
• Report creation Summarizes key insights from data and generates reports.Found in ManyExcel
• Workflow automation templates Provides customizable templates to automate repetitive workflows.Found in AI Assist
• Real-time collaboration Enables team members to work together on projects in real time.Found in AI Assist
• Code generation for VBA and Apps Script Creates and explains VBA and Apps Script code for spreadsheet automation.Found in Formulas HQ
• Regular expression generator Constructs regular expressions for data validation and pattern matching.Found in Formulas HQ
• Formula and shortcut directories Provides searchable libraries of common formulas and keyboard shortcuts for quick lookup.Found in Sheetsbase
• Edge case handling Addresses tricky cases such as locale separators, date formats, and array formulas.Found in Sheetsbase
• AI-powered spreadsheet functions Offers custom functions like =M.EXTRACT, =M.GEN, and =M.CATEGORIZE for extracting, summarizing, and categorizing data.Found in Melder - AI for Excel
• Document processing in Excel Allows dropping PDFs into Excel to extract structured data without manual effort.Found in Melder - AI for Excel

What goes in, what comes out

The workflow

1. In
   Start with

   Spreadsheet data, plain-language instructions and imported files

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect spreadsheet data

4. 3

   Plain-language instructions and imported files

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed formulas, cleaned tables, charts and reports linked to source cells

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed workbook format and approved function set; final financial interpretation and sign-off remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data intake and mapping, Editable analysis workspace, Review and report delivery. Use a workbook list for projects, a large central grid with an in-sheet assistant panel, and a right-hand panel for proposed edits, sources and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant cell or chart. Make the task-specific outcome reviewed formulas, cleaned tables, charts and reports linked to source cells visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, workbook versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned spreadsheets, authorized file sources and permitted reporting destinations. Cloud file storage, spreadsheet import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: assist inside the sheet without switching tools; generate formulas from plain-language requests. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will analysts and finance teams working in spreadsheets who need evidence-backed analysis and reporting use it to solve "spreadsheet work is split across several AI add-ons and chat tools, so formulas, cleaning, charts and reports are produced without a reviewable trail"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted analysis outputs per analyst hour and corrections after review.
4. Measure, then decide. Track accepted analysis outputs per analyst hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed workbook format and approved function set; final financial interpretation and sign-off remain human. Implement one approved input format, a bounded representative case set and the first two task modules: assist inside the sheet without switching tools; generate formulas from plain-language requests. Support the remaining modules with operator review: explain formulas, clean data, build charts, generate scenario tabs, require accept or revert, keep an audit trail, import files, summarize insights and generate reports. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed formulas, cleaned tables, charts and reports linked to source cells. Retain the explicit scope boundary: One fixed workbook format and approved function set; final financial interpretation and sign-off remain human.

What the build depends on. File upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity reporting requires specialist analytical QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed workbook format and approved function set; final financial interpretation and sign-off remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined workbook package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist reporting separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed analysis package. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce tool switching and manual spreadsheet work while keeping a reviewable record of every change. Demonstrate a concrete reviewed analysis package using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Analysts and finance teams working in spreadsheets professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample analysis package from a small authorized input set, with a transparent calculation of accepted analysis outputs per analyst hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five analysts and finance teams working in spreadsheets and inspect a recent example of spreadsheet work split across several AI add-ons and chat tools without a reviewable trail.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted analysis outputs per analyst hour and corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted analysis outputs per analyst hour and corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted analysis outputs per analyst hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed formulas, cleaned tables, charts and reports linked to source cells. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved formulas, cleaning rules, report templates and review examples, together with reliable delivery for a narrow analytical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for analysts and finance teams working in spreadsheets. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ChatGPT for Google Sheets, ManyExcel, Pane, AI Assist, Excel Formula Bot, Sheetsbase, Formulas HQ, Bricks and Melder - AI for Excel. Compare this product with the buyer's present method on accepted analysis outputs per analyst hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, file processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed formulas, cleaned tables, charts and reports linked to source cells. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source data, cell-level attribution, calculation accuracy and usage permissions. Named owners approve substantive changes and report scope. One fixed workbook format and approved function set; final financial interpretation and sign-off remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.