Source-linked retrieval answer console

For

IT and development teams answering questions from their own documents, databases and applications

Solves

Answers are scattered across documents, databases and applications, so teams cannot trace where a response came from or keep it current.

Delivers

Reviewer-approved grounded answers with source references

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce time spent locating and verifying answers while keeping every response linked to its source.

1. Connect external data sources.
2. Retrieve documents and combine them with language model generation.
3. Upload private datasets.
4. Build knowledge bases without code.
5. Show answers with sources and query logic.
6. Query a structured data catalog.
7. Edit SQL and build visualizations.
8. Adjust retrieval parameters and generation style.
9. Process documents and answer in real time.
10. Manage and monitor retrieval workflows in one interface.
11. Generate text such as articles, reports and marketing materials.
12. Identify patterns and generate insights from data.
13. Apply customizable templates by industry and use case.
14. Automate routine tasks and workflows.
15. Support multiple languages and content formats.
16. Expose API access for other applications.
17. Run semantic search across applications, databases and document stores.
18. Sync data manually, on schedule or on events with version tracking.
19. Run autonomous research across local documents and online sources.
20. Build knowledge graphs for structured representation.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewer-approved grounded answer set with source references and unresolved questions.

Everything these tools do, in one app

• External data integration Connects to external data sources to enrich responses with relevant information.Found in Verta RAG System, Powerdrill, Baselight AI and 2 more
• Retrieval-augmented generation Combines document retrieval with language model generation to produce context-aware answers.Found in Verta RAG System, Tilores Identity RAG, Super RAG
• Private dataset upload Allows users to upload their own data to create tailored knowledge bases.Found in Powerdrill, Baselight AI
• No-code knowledge base creation Enables building AI-powered knowledge bases without writing code.Found in Powerdrill
• Traceable answers with sources Provides answers that include the underlying data, sources, and query logic for verification.Found in Baselight AI, R2R
• Structured data catalog Offers a large catalog of structured datasets accessible at query time.Found in Baselight AI
• SQL editing and visualization Provides a studio for SQL editing and visualizations to analyze data.Found in Baselight AI
• Customizable query handling Allows adjusting retrieval parameters and generation style to suit different needs.Found in Verta RAG System, Super RAG
• Real-time processing Delivers quick responses and real-time document retrieval for efficient task completion.Found in Verta RAG System, Superpowered AI, Super RAG
• User-friendly interface Offers an intuitive interface for managing and monitoring retrieval workflows.Found in Verta RAG System, Vext, Superpowered AI and 1 more
• Content generation Generates text such as articles, reports, and marketing materials.Found in Vext, Superpowered AI, Farspeak
• Data analysis tools Identifies patterns and generates insights from data.Found in Vext, Superpowered AI
• Customizable templates Provides templates that can be tailored to different industries and use cases.Found in Vext
• Workflow automation Automates routine tasks and workflows to improve productivity.Found in Superpowered AI
• Multi-language support Supports multiple languages and content formats for broader usability.Found in Superpowered AI, Farspeak
• API access Offers API access for integration with other applications and workflows.Found in Farspeak, R2R
• Semantic search across sources Performs semantic searches across various applications, databases, and document stores.Found in Airweave
• Data syncing options Supports manual, scheduled, and event-driven data syncing with version tracking.Found in Airweave
• Autonomous research Conducts autonomous research across local documents and online sources.Found in R2R
• Knowledge graph construction Builds knowledge graphs for structured information representation.Found in R2R

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted data sources, uploaded private datasets and query settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted data sources

4. 3

   Uploaded private datasets and query settings

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved grounded answers 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 source set and permission scope; final factual and access checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connection and permissions, Retrieval and answer workspace, Review and delivery. Use a thumbnail gallery for knowledge bases, a large central answer canvas, and a right-hand panel for sources, retrieval settings and comments. Let users compare answer versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source passage. Make the task-specific outcome reviewer-approved grounded answers with source references visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client comments, approval states, usage allowances, query 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 documents, databases and applications. Cloud storage, identity providers, ticketing and messaging 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

   5 days

   One buyer segment, one recurring use case; first modules: connect external data sources; retrieve documents and combine them with language model generation. 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 IT and development teams answering questions from their own documents, databases and applications use it to solve "answers are scattered across documents, databases and applications, so teams cannot trace where a response came from or keep it current"?
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 reviewer hour and corrections after answer approval.
4. Measure, then decide. Track accepted answers per reviewer hour and corrections after answer approval; 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 source set and permission scope; final factual and access checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect external data sources; retrieve documents and combine them with language model generation. Support the third module with operator review: show answers with sources and query logic. 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 reviewer-approved grounded answers with source references. Retain the explicit scope boundary: One fixed source set and permission scope; final factual and access checks remain human.

What the build depends on. Source upload and preview, asynchronous retrieval jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed source set and permission scope; final factual and access checks 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 source package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist data work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved grounded answers 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 time spent locating and verifying answers while keeping every response linked to its source. Demonstrate a concrete reviewer-approved grounded answers with source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development teams answering questions from their own documents, databases and applications professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved grounded answers with source references from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and corrections after answer approval and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams answering questions from their own documents, databases and applications and inspect a recent example of answers scattered across documents, databases and applications, so teams cannot trace where a response came from or keep it current.
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 reviewer hour and corrections after answer approval, 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 reviewer hour and corrections after answer approval. 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 reviewer hour and corrections after answer approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved grounded answers 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 source connectors, retrieval settings and review examples, together with reliable delivery for a narrow IT niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams answering questions from their own documents, databases and applications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Verta RAG System, Powerdrill, Baselight AI, Vext, Superpowered AI, Tilores Identity RAG, Farspeak, Airweave, R2R and Super RAG, plus internal scripts and manual search. Compare this product with the buyer's present method on accepted answers per reviewer hour and corrections after answer approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Retrieval attempts, model calls, 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 reviewer-approved grounded answers with source references. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, access permissions and data rights. Named owners approve substantive changes and external use. One fixed source set and permission scope; final factual and access checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.