Evidence-backed data question and reporting workspace

For

Analysts and operations leads answering recurring business questions from company data

Solves

Questions about company data require SQL, separate dashboards and manual reporting, so answers arrive late and cannot be traced.

Delivers

Reviewed, source-linked answers, dashboards and reports

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 time to a traceable answer while keeping the organization's data and definitions.

1. Ask questions in plain language and return answers without SQL.
2. Aggregate and analyze connected data to identify themes and insights.
3. Search connected sources with AI and retrieve relevant records.
4. Connect databases, CRMs and ERPs through managed connectors.
5. Query a wide range of database engines.
6. Upload or sync files from cloud storage without technical setup.
7. Build interactive dashboards without writing code.
8. Auto-generate charts and visualizations for trends.
9. Present insights in clear, accessible layouts.
10. Allow custom SQL and JavaScript for advanced cases.
11. Refine results with filters and parameters.
12. Apply organization-specific model settings and definitions.
13. Monitor underlying data and notify on changes.
14. Send real-time updates and alerts on data trends.
15. Embed dashboards and reports into sites or apps.
16. Export data in multiple formats.
17. Share prompts, outputs and histories with the team.
18. Generate narratives, proposals and documents from datasets.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before publication.
21. Export a versioned reviewed answer with source references and unresolved questions.

Everything these tools do, in one app

• Natural Language Querying Ask questions in plain language and get answers without coding or SQL.Found in InsightBase, AskEdith
• Data Analysis Aggregate and analyze data to identify themes and insights.Found in Echobase
• AI-Powered Search Use AI to search and retrieve relevant information quickly.Found in Outerbase 2.0
• Comprehensive Data Integration Connect to many data sources like databases, CRMs, and ERPs.Found in AskEdith
• Broad Database Support Connect to and query a wide range of databases.Found in InsightBase
• No-Code File Integration Upload or sync files from cloud storage without technical expertise.Found in Echobase
• Visual Dashboard Builder Create interactive dashboards without writing code.Found in InsightBase
• Dynamic Data Visualization Auto-generate charts and visualizations to present data trends.Found in AskEdith
• User-Friendly Dashboard Present data insights in clear, accessible formats.Found in Outerbase 2.0
• Custom SQL & JavaScript Write custom queries or scripts for enhanced flexibility.Found in InsightBase
• Customizable Filters Refine search results with filters and parameters.Found in Outerbase 2.0
• Customized AI Models Fine-tune large language models to meet specific business needs.Found in AskEdith
• Real-Time Data Monitoring Receive notifications when underlying data changes.Found in InsightBase
• Real-Time Updates and Alerts Stay informed on the latest data trends with real-time updates.Found in Outerbase 2.0
• Embeddable Dashboards Integrate dashboards and reports into websites or apps.Found in InsightBase
• Data Export Options Export data in multiple formats for easy integration.Found in Outerbase 2.0
• Team Collaboration Share prompts, outputs, and chat histories in real time.Found in Echobase
• Content Creation Generate new narratives, proposals, and documents from datasets.Found in Echobase

What goes in, what comes out

The workflow

1. In
   Start with

   Connected databases, uploaded files and business definitions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected databases

4. 3

   Uploaded files and business definitions

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed, source-linked answers, dashboards and reports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers and narratives 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. Final metric definitions and publication decisions remain with the data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data connections and definitions, Question and analysis workspace, Dashboard and report delivery. Use a list of saved questions and datasets, a large central answer canvas with the generated query, chart and source rows, and a right-hand panel for definitions, filters 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, source-linked answers, dashboards and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, connection credentials, dataset versions, saved questions, approval states, usage allowances, export 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, CRMs, ERPs and cloud file storage. Warehouse and BI destinations, export formats and embedding targets. 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: ask questions in plain language and return answers without SQL; aggregate and analyze connected data to identify themes and insights. 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 operations leads answering recurring business questions from company data use it to solve "questions about company data require SQL, separate dashboards and manual reporting, so answers arrive late and cannot be traced"?
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: Reviewed answers accepted per analyst hour and corrections after publication.
4. Measure, then decide. Track reviewed answers accepted 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 approved database and one file source; final metric definitions and publication decisions remain with the data owner. Implement one approved input format, a bounded representative question set and the first two task modules: ask questions in plain language and return answers without SQL; aggregate and analyze connected data to identify themes and insights. Support the third module with operator review: search connected sources with AI and retrieve relevant records. 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 question volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked answers, dashboards and reports. Retain the explicit scope boundary: One approved database and one file source; final metric definitions and publication decisions remain with the data owner.

What the build depends on. Connection setup 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 approved database and one file source; final metric definitions and publication decisions remain with the data owner.

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 scope. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist analytics separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked answer set. 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 time to a traceable answer while keeping the organization's data and definitions. Demonstrate a concrete reviewed, source-linked answer set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Analysts and operations leads professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample answer set from a small authorized input set, with a transparent calculation of reviewed answers accepted per analyst hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five analysts and operations leads answering recurring business questions from company data and inspect a recent example of questions about company data require SQL, separate dashboards and manual reporting, so answers arrive late and cannot be traced.
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 reviewed answers accepted 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: Reviewed answers accepted 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

Reviewed answers accepted 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, source-linked answers, dashboards and 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 metric definitions, query patterns 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 operations leads answering recurring business questions from company data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

InsightBase, Echobase, Outerbase 2.0 and AskEdith. Compare this product with the buyer's present method on reviewed answers accepted 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 and model usage, storage, reviewer hours, client revision rounds and connector maintenance. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked answers, dashboards and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data access boundaries, source attribution, metric definitions and usage permissions. Data owners approve substantive changes and publication scope. One approved database and one file source; final metric definitions and publication decisions remain with the data owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.