Spreadsheet AI formula and analysis console

For

Marketing and operations teams that run recurring reporting and content work in Google Sheets

Solves

AI work is split across several paid spreadsheet add-ons, so formulas, prompts, cached results and review history live in different tools and cannot be audited in one place.

Delivers

Reviewed AI outputs written back to named cells with source references

Built in

about 4 weeks of creation time, MVP in 4 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 the number of rented add-ons and keep spreadsheet AI work in one owned, reviewable console.

1. Connect to Google Sheets and read selected ranges.
2. Provide custom AI formula functions callable from cells.
3. Generate written content such as paragraphs, lists and ideas.
4. Summarize long text into shorter summaries.
5. Classify rows such as reviews or inquiries.
6. Extract emails, phone numbers and addresses from text.
7. Translate text between languages.
8. Answer questions from sheet data and reference documents.
9. Analyze ranges and return structured results.
10. Generate spreadsheet formulas from plain-English descriptions.
11. Create images from plain-English prompts.
12. Store user-provided memory and context from text or URLs.
13. Cache responses and throttle API calls per sheet.
14. Run updates on demand or on a schedule.
15. Author and run Apps Script for custom transformations.
16. Build charts and presentation-ready visuals.
17. Run multi-step agentic tasks such as web research and script-driven edits.
18. Draft, format and deliver finished documents, reports or slides.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before write-back.
21. Export a versioned reviewed AI output set with source references and unresolved questions.

Everything these tools do, in one app

• Google Sheets integration Lets users access AI capabilities directly inside Google Sheets without leaving the spreadsheet.Found in GPT For Sheets, Claude For Sheets, Ajelix AI Agent for Work and 1 more
• Custom formula functions Provides spreadsheet formulas that call AI to perform tasks from within cells.Found in GPT For Sheets, Claude For Sheets, SheetAI
• Content generation Generates written content such as paragraphs, lists, and ideas.Found in GPT For Sheets, Claude For Sheets, Ajelix AI Agent for Work and 1 more
• Text summarization Condenses lengthy texts into shorter summaries.Found in GPT For Sheets, Claude For Sheets
• Data classification Categorizes or classifies data such as reviews or inquiries.Found in GPT For Sheets, Claude For Sheets
• Information extraction Extracts specific details like emails, phone numbers, and addresses from text.Found in Claude For Sheets, SheetAI
• Language translation Translates text between languages.Found in Claude For Sheets
• Question answering Answers questions based on data or documents.Found in Claude For Sheets, SheetAI
• Data analysis Analyzes data to provide insights or structured results.Found in Ajelix AI Agent for Work, SheetAI
• Formula generation Automatically creates spreadsheet formulas to simplify calculations.Found in SheetAI
• Image generation Creates images from plain English prompts.Found in SheetAI
• AI memory and context Trains the AI with user data via text or URLs for more accurate, tailored outputs.Found in SheetAI
• Caching and throttling Caches responses within the sheet and manages API call limits to optimize performance.Found in Claude For Sheets
• On-demand and scheduled updates Allows users to recalculate on demand or schedule background updates.Found in Claude For Sheets
• Apps Script authoring Writes and runs Google Apps Script to automate custom workflows and transformations.Found in Ajelix AI Agent for Work
• Visualization and slides Builds charts and presentation-ready visuals and slides.Found in Ajelix AI Agent for Work
• Multi-step agentic tasks Performs multi-step tasks like web research and script-driven edits without leaving the workspace.Found in Ajelix AI Agent for Work
• Production-ready deliverables Drafts, formats, and delivers finished documents, reports, or slides for immediate use.Found in Ajelix AI Agent for Work

What goes in, what comes out

The workflow

1. In
   Start with

   Sheet ranges, prompts, reference documents and usage limits

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect sheet ranges

4. 3

   Prompts

5. 4

   Reference documents and usage limits

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed AI outputs written back to named cells with source references

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 connected spreadsheet workspace and one approved model set; final data interpretation and write-back decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace connection and scope, Formula and prompt workbench, Review and write-back queue. Use a sheet-range picker, a central prompt and formula editor, and a right-hand panel for references, cached results, usage limits and comments. Let users compare generated and prior cell values side by side. Display draft, changes requested and approved states. Provide a shared review link with comments anchored to the relevant cell range. Make the task-specific outcome reviewed AI outputs written back to named cells with source references visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, sheet and range permissions, prompt versions, cached responses, approval states, usage allowances, API call limits, write-back 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

Google Sheets, Google Apps Script, Google Drive, approved model providers and presentation or document 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

   4 days

   One buyer segment, one recurring use case; first modules: connect to Google Sheets and read selected ranges; provide custom AI formula functions callable from cells. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 marketing and operations teams that run recurring reporting and content work in Google Sheets use it to solve "AI work is split across several paid spreadsheet add-ons, so formulas, prompts, cached results and review history live in different tools and cannot be audited in one place"?
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 AI-written cells per reviewer hour and corrections after write-back.
4. Measure, then decide. Track accepted AI-written cells per reviewer hour and corrections after write-back; 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 connected spreadsheet workspace and one approved model set; final data interpretation and write-back decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect to Google Sheets and read selected ranges; provide custom AI formula functions callable from cells. Support the remaining modules with operator review: generate content, summarize, classify, extract, translate, answer questions, analyze ranges, generate formulas, create images, store memory, cache and throttle, schedule updates, author Apps Script, build visuals, run multi-step tasks and draft deliverables. 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 AI outputs written back to named cells with source references. Retain the explicit scope boundary: One connected spreadsheet workspace and one approved model set; final data interpretation and write-back decisions remain human.

What the build depends on. Sheet connection and range preview, asynchronous AI jobs, editable version history, reviewer access and tested export formats. High-fidelity spreadsheet work requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One connected spreadsheet workspace and one approved model set; final data interpretation and write-back decisions 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 spreadsheet workflow. Offer a monthly production allowance after repeat demand. Quote complex multi-step agentic or document production separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed AI output set written back to named cells with source references. 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 the number of rented add-ons and keep spreadsheet AI work in one owned, reviewable console. Demonstrate a concrete reviewed AI output set written back to named cells with source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and operations teams that run recurring reporting and content work in Google Sheets professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample AI output set written back to named cells with source references from a small authorized input set, with a transparent calculation of accepted AI-written cells per reviewer hour and corrections after write-back and no promised savings.

The first 30 days

1. Week 1: interview five marketing and operations teams that run recurring reporting and content work in Google Sheets and inspect a recent example of AI work split across several paid spreadsheet add-ons.
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 AI-written cells per reviewer hour and corrections after write-back, 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 AI-written cells per reviewer hour and corrections after write-back. 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 AI-written cells per reviewer hour and corrections after write-back; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed AI outputs written back to named cells with source references. 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 prompts, sheet configurations and review examples, together with reliable delivery for a narrow spreadsheet workflow. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and operations teams that run recurring reporting and content work in Google Sheets. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

GPT For Sheets, Claude For Sheets, Ajelix AI Agent for Work and SheetAI. Compare this product with the buyer's present method on accepted AI-written cells per reviewer hour and corrections after write-back. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, image processing, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed AI outputs written back to named cells with source references. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, data accuracy and usage permissions. Named owners approve substantive changes and write-back scope. One connected spreadsheet workspace and one approved model set; final data interpretation and write-back decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.