Evidence-backed data question and reporting workspace

For

Analysts and operations leads who answer recurring data questions for their teams

Solves

Data questions arrive faster than analysts can answer them, and answers arrive without traceable sources or review.

Delivers

Reviewed plain-language answers, charts and reusable playbooks linked to source queries

Built in

about 5 weeks of creation time, MVP in 5 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 repeated manual data pulls while keeping every answer traceable and reviewed.

1. Ask questions about connected data in plain language.
2. Generate charts, graphs and dashboards from the result.
3. Connect spreadsheets, databases, cloud storage and other sources.
4. Clean and preprocess datasets before analysis.
5. Export results in multiple formats and share them.
6. Answer questions inside Slack with charts and summaries.
7. Enforce permission-aware access with traceable answers.
8. Apply role-based access control and enterprise security settings.
9. Expose an API for programmatic access.
10. Switch to a notebook with SQL, Python and visualizations.
11. Keep local files in browser storage so they do not leave the device.
12. Generate and run SQL against connected databases.
13. Structure recurring decisions into reusable playbooks with consistent metrics.
14. Label and categorize data for analysis.
15. Track donation and fundraising questions for nonprofit teams.
16. Build conversational agents with a drag-and-drop builder.
17. Deploy those agents across messaging and social platforms.
18. Monitor usage and engagement in an analytics dashboard.

Everything these tools do, in one app

• Natural Language Querying Ask questions about your data in plain English and get answers without writing code.Found in DataGPT, Basejump AI, Alkemi and 6 more
• Interactive Visualizations Generate charts, graphs, and dashboards to visually explore data trends and patterns.Found in DataGPT, Basejump AI, Alkemi and 5 more
• Data Source Integration Connect to spreadsheets, databases, cloud storage, and other data sources for analysis.Found in DataGPT, Basejump AI, Alkemi and 4 more
• Automated Data Cleaning Automatically clean and preprocess datasets to prepare them for analysis.Found in DataGPT, Kater
• Export and Sharing Export results in multiple formats and share insights with others.Found in DataGPT, Basejump AI, Alkemi and 2 more
• Slack Integration Ask questions and receive answers, charts, and summaries directly within Slack.Found in Alkemi, Julius Slack Agent
• Permission-Aware Access Ensure users only see data they are authorized to access, with traceable answers.Found in Alkemi, Kater
• Enterprise Security Provide enterprise-grade security features like HIPAA compliance and role-based access control.Found in Basejump AI, Kater
• API Access Integrate with other systems via API for programmatic access.Found in Basejump AI
• Notebook Mode Switch to a notebook interface with SQL, Python, and visualizations for advanced analysis.Found in Livedocs
• Local Data Privacy Keep data in local browser storage so files do not leave your device.Found in Upsolve AI for CSVs
• SQL Generation Automatically generate and run SQL queries against connected databases.Found in Julius Slack Agent
• Dynamic Playbooks Structure business decision flows into reusable playbooks for consistent metrics.Found in Kater
• Intelligent Data Curation Automatically label and categorize data to optimize for AI analysis.Found in Kater
• Nonprofit Focus Tailored for analyzing donation data and fundraising insights for nonprofits.Found in Overflow AI
• Chatbot Builder Drag-and-drop builder to create conversational agents and chatbots.Found in Sequel
• Multi-Platform Support Deploy chatbots across social media, messaging apps, and other platforms.Found in Sequel
• Analytics Dashboard Monitor performance and user engagement with an analytics dashboard.Found in Sequel

What goes in, what comes out

The workflow

1. In
   Start with

   Connected spreadsheets, databases and cloud storage

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected spreadsheets

4. 3

   Databases and cloud storage

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed plain-language answers, charts and reusable playbooks linked to source queries

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 source set and one permission model; final metric definitions and publication decisions remain analytical. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data connection and permissions, Question and answer workspace, Reviewed report and delivery. Use a left-hand list of saved questions and playbooks, a large central answer canvas with chart and table views, and a right-hand panel for sources, query text, permissions and comments. Let users compare answer versions side by side. Display draft, changes requested and approved states. Provide a share link with comments anchored to the relevant chart or number. Make the task-specific outcome reviewed plain-language answers, charts and reusable playbooks linked to source queries visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, source credentials, permission groups, saved questions, approval states, usage allowances, query limits, export history and a rights record for supplied data. 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, databases and cloud storage. Cloud data warehouses, BI destinations, Slack and messaging platforms. 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

   5 days

   One buyer segment, one recurring use case; first modules: ask questions about connected data in plain language; generate charts, graphs and dashboards from the result. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 operations leads who answer recurring data questions for their teams use it to solve "data questions arrive faster than analysts can answer them, and answers arrive without traceable sources or review"?
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 answers per analyst hour and corrections after publication.
4. Measure, then decide. Track accepted answers per analyst hour and corrections after publication; 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 source set and one permission model; final metric definitions and publication decisions remain analytical. Implement one approved input format, a bounded representative case set and the first two task modules: ask questions about connected data in plain language; generate charts, graphs and dashboards from the result. Support the third module with operator review: connect spreadsheets, databases, cloud storage and other sources. 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 plain-language answers, charts and reusable playbooks linked to source queries. Retain the explicit scope boundary: One connected source set and one permission model; final metric definitions and publication decisions remain analytical.

What the build depends on. Source connection and preview, asynchronous query 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 connected source set and one permission model; final metric definitions and publication decisions remain analytical.

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 data workspace. Offer a monthly analysis allowance after repeat demand. Quote complex warehouse, security or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed plain-language answers, charts and reusable playbooks linked to source queries. 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 repeated manual data pulls while keeping every answer traceable and reviewed. Demonstrate a concrete reviewed plain-language answers, charts and reusable playbooks linked to source queries using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Analysts and operations leads who answer recurring data questions for their teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed plain-language answers, charts and reusable playbooks linked to source queries from a small authorized input set, with a transparent calculation of accepted answers per analyst hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five analysts and operations leads who answer recurring data questions for their teams and inspect a recent example of data questions arrive faster than analysts can answer them, and answers arrive without traceable sources or review.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted answers per analyst hour and corrections after publication, 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 answers per analyst hour and corrections after publication. 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 answers per analyst hour and corrections after publication; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed plain-language answers, charts and reusable playbooks linked to source queries. 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 metric definitions, source mappings 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 operations leads who answer recurring data questions for their teams. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

DataGPT, Basejump AI, Alkemi, Kater, Overflow AI, Livedocs, Julius Slack Agent, Upsolve AI for CSVs, Sequel and STRING. Compare this product with the buyer's present method on accepted answers per analyst hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Query compute, storage, reviewer hours, client revision rounds and licensed source connectors. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed plain-language answers, charts and reusable playbooks linked to source queries. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, metric definitions, permission boundaries and usage permissions. Data owners approve substantive changes and publication scope. One connected source set and one permission model; final metric definitions and publication decisions remain analytical. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.