Evidence-backed data analysis and reporting workspace

For

Analysts and operators in marketing, sales and operations teams who need answers from data without SQL or spreadsheet expertise

Solves

Raw data sits in spreadsheets, databases and web pages, and turning it into answers, charts and shareable reports requires SQL or spreadsheet skills the team does not have.

Delivers

Reviewed answers, charts and scheduled reports with source references

Built in

about 5 weeks of creation time, MVP in 6 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 time from raw data to a shared, source-linked report while keeping every number traceable.

1. Connect databases, warehouses and cloud services.
2. Upload and analyze CSV files directly.
3. Clean and map schemas before analysis.
4. Ask questions in plain English and get data answers.
5. Generate charts and tables automatically from results.
6. Link every answer back to the captured source data.
7. Capture live web page content as seen in the browser.
8. Run JavaScript on uploaded data for custom processing.
9. Compare algorithms and pick the best model for the data.
10. Run regression, classification and time series forecasts.
11. Arrange and refine visualizations in drag-and-drop dashboards.
12. Write narrative summaries that highlight key insights.
13. Refresh analyses and reports when underlying data updates.
14. Distribute reports via Slack, email and Google Slides.
15. Apply fine-grained permissions for teammates, clients or investors.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewed report with source references and unresolved questions.

Everything these tools do, in one app

• Natural language querying Lets users ask questions in plain English and get data answers without writing SQL or formulas.Found in Querri 2.0, HyperArc, Aureo.io and 3 more
• Automatic chart generation Turns data into charts or tables automatically so results are easy to interpret.Found in Querri 2.0, BayesLab, Capalyze and 2 more
• Interactive dashboards Provides drag-and-drop dashboards for arranging and refining visualizations without code.Found in Querri 2.0, HyperArc, Dashboards by Equals
• Report export and sharing Exports or shares finished reports and visuals with teammates or stakeholders.Found in Querri 2.0, BayesLab, Capalyze and 1 more
• Data source connectors Connects to databases, warehouses, and cloud services to bring data in.Found in Querri 2.0, HyperArc, BayesLab and 1 more
• Automated data cleaning Prepares raw files by cleaning and handling schemas before analysis.Found in BayesLab, Aureo.io
• Scheduled report refresh Regenerates analyses and reports automatically when underlying data updates.Found in BayesLab, Dashboards by Equals
• AI-generated summaries Writes narrative summaries that highlight key insights and context.Found in BayesLab, Dashboards by Equals
• Live web scraping Captures live web page content as seen in the browser and imports it for analysis.Found in Capalyze
• Source-linked answers Links generated answers back to the captured source data to reduce hallucination risk.Found in Capalyze
• CSV file analysis Analyzes uploaded CSV files directly inside the tool.Found in The Analysis tool in Claude.ai
• Script-based data processing Runs JavaScript on uploaded data for customized processing and deeper analysis.Found in The Analysis tool in Claude.ai
• Metabase integration Works as a Chrome extension layered on top of Metabase for querying and visualizing its data.Found in Bilbo
• Spreadsheet-to-dashboard conversion Instantly converts spreadsheet data into dynamic dashboards with live updates.Found in Dashboards by Equals
• Automated report distribution Schedules and sends reports automatically via Slack, email, and Google Slides.Found in Dashboards by Equals
• Access controls Provides secure sharing with fine-grained permissions for teammates, clients, or investors.Found in HyperArc, Dashboards by Equals
• Algorithm comparison Automatically compares many algorithms to pick the best model for the data.Found in Aureo.io
• Predictive modeling Runs regression, classification, and time series forecasting steps on data.Found in Aureo.io, BayesLab

What goes in, what comes out

The workflow

1. In
   Start with

   Connected databases, warehouses and cloud services, uploaded CSV files, captured web pages and approved source list

2. 1

   Confirm the buyer's problem and scope

3. 2

   Connect databases

4. 3

   Warehouses

5. 4

   Cloud services

6. 5

   Uploaded files and captured web pages

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed answers, charts and scheduled reports 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 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 fixed data schema and approved source list; final interpretation and business decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data sources and connections, Ask and explore, Dashboard builder, Report and distribution. Use a left rail for sources and saved questions, a central canvas for charts and tables, and a right panel for source links, assumptions and comments. Let users compare chart versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or number. Make the task-specific outcome reviewed answers, charts and scheduled reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source credentials, dataset versions, client comments, approval states, usage allowances, refresh schedules, distribution lists, 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 databases, warehouses, cloud services, uploaded CSV files and permitted web sources. Cloud data storage, BI tool import/export and distribution 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

   6 days

   One buyer segment, one recurring use case; first modules: connect databases, warehouses and cloud services; upload and analyze CSV files directly. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 operators in marketing, sales and operations teams who need answers from data without SQL or spreadsheet expertise use it to solve "raw data sits in spreadsheets, databases and web pages, and turning it into answers, charts and shareable reports requires SQL or spreadsheet skills the team does not have"?
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: Time from data request to accepted report and corrections after stakeholder review.
4. Measure, then decide. Track time from data request to accepted report and corrections after stakeholder 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 data schema and approved source list; final interpretation and business decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect databases, warehouses and cloud services; upload and analyze CSV files directly. Support the third module with operator review: clean and map schemas before analysis. 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 answers, charts and scheduled reports. Retain the explicit scope boundary: One fixed data schema and approved source list; final interpretation and business decisions remain human.

What the build depends on. Data upload and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity analytics requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed data schema and approved source list; final interpretation and business 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 data package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist modeling separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed report 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 time from raw data to a shared, source-linked report while keeping every number traceable. Demonstrate a concrete reviewed report with source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Analysts and operators in marketing, sales and operations teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample report with source references from a small authorized input set, with a transparent calculation of time from data request to accepted report and corrections after stakeholder review and no promised savings.

The first 30 days

1. Week 1: interview five analysts and operators in marketing, sales and operations teams and inspect a recent example of raw data sitting in spreadsheets, databases and web pages and turning it into answers, charts and shareable reports requires SQL or spreadsheet skills the team does not have.
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 time from data request to accepted report and corrections after stakeholder 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: Time from data request to accepted report and corrections after stakeholder 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

Time from data request to accepted report and corrections after stakeholder 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 answers, charts and scheduled reports. 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 data schemas, source mappings and review examples, together with reliable delivery for a narrow analytics niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for analysts and operators in marketing, sales and operations teams. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Morph 1.0, Querri 2.0, HyperArc, BayesLab, Spreadsite, Aureo.io, Capalyze, The Analysis tool in Claude.ai, Bilbo and Dashboards by Equals. Compare this product with the buyer's present method on time from data request to accepted report and corrections after stakeholder review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Query and model attempts, 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 answers, charts and scheduled reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data provenance, source attribution, calculation accuracy and usage permissions. Data owners approve substantive changes and distribution scope. One fixed data schema and approved source list; final interpretation and business decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.