Source-linked answer assistant and admin console

For

Support and operations teams answering questions from connected company documents and communication tools

Solves

Answers are scattered across drives, chat and wikis, so staff search manually and reply without traceable sources.

Delivers

Source-linked answers with named-owner review

Built in

about 4 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 manual search time while keeping every answer traceable to its source.

1. Connect Google Drive, Slack, Notion and website sources.
2. Sync connected data on a schedule.
3. Extract, chunk and index PDFs, slides and pages.
4. Answer questions with semantic, keyword and hybrid retrieval.
5. Show source citations and links beside each answer.
6. Flag knowledge gaps and duplicate documentation.
7. Provide an embeddable chat interface.
8. Support multiple assistants with configurable tone.
9. Expose API and SDK access for custom apps.
10. Build task-specific AI agents.
11. Automate outreach and reporting workflows.
12. Enforce authentication and permission boundaries.
13. Separate tenant data logically.
14. Log and moderate AI interactions.
15. Report usage and answer quality in an analytics dashboard.
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 source-linked answer set with source references and unresolved questions.

Everything these tools do, in one app

• Multi-Source Integration Connects to popular platforms like Google Drive, Slack, and Notion to centralize data access.Found in Needle, Ragie, Ragie Connect and 3 more
• Instant AI Search Allows users to ask questions and receive immediate, context-aware answers from all connected data sources.Found in Needle, Ragie, Ragie Connect and 5 more
• API and SDK Access Provides APIs and SDKs for developers to integrate and build custom applications.Found in Needle, Ragie, Ragie Connect and 3 more
• Custom AI Agents Enables building AI agents tailored to specific organizational needs.Found in Needle, Query Memory, Dashworks Answer API
• Workflow Automation Automates common tasks such as sales outreach and internal reporting through customizable workflows.Found in Needle, Dashworks Answer API
• Data Synchronization Automatically syncs data from connected sources to keep information up to date.Found in Ragie, Ragie Connect, Query Memory
• Document Processing Pipeline Extracts, chunks, and indexes various document types including PDFs and PowerPoints.Found in Ragie, Query Memory
• Advanced Retrieval Methods Uses semantic, keyword, hierarchical, and hybrid search to ensure accurate results and reduce hallucinations.Found in Ragie, Ragie Connect
• Built-in Authentication Handles authentication and permission management to secure user data.Found in Ragie Connect
• Multi-Tenant Support Supports multi-tenant applications with logical data separation.Found in Ragie Connect
• Chat Interface Provides an embeddable chat interface for websites and applications.Found in Query Memory, NewRA.ai
• Source Citation Displays sources and links used to generate answers, ensuring transparency.Found in Casc, NewRA.ai
• Knowledge Gap Identification Identifies knowledge gaps and duplicate documentation to optimize content management.Found in Casc
• Analytics Dashboard Includes analytics tools to monitor usage and improve the knowledge base over time.Found in Casc
• Website Crawling Indexes website content quickly to provide accurate answers based solely on that data.Found in SiteAssist
• Customizable Personalities Supports multiple assistants with customizable personalities.Found in SiteAssist
• Real-Time Search Searches across applications in real-time to ensure answers are current and relevant.Found in Dashworks Answer API
• Moderation and Logs Includes tools to monitor and review AI interactions for quality control.Found in NewRA.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Connected documents, chat history, website content and permission settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected documents

4. 3

   Chat history and website content

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked answers with named-owner review

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers for the stated task modules. Use deterministic code for arithmetic, schema validation, permission checks and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved source set and permission model; final policy and customer-facing answers remain human-reviewed. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Connected sources and permissions, Answer workspace, Admin console and logs. Use a source list with sync status, a central question-and-answer canvas with inline citations, and a right-hand panel for review state, confidence and comments. Let users compare draft and approved answers side by side. Display draft, changes requested and approved states. Provide a client-facing embed preview with comments anchored to the relevant answer. Make the task-specific outcome source-linked answers with named-owner review visible beside its evidence, review state and value baseline.

Accounts and administration

Source ownership, sync status, tenant boundaries, assistant configurations, review states, usage allowances, retention limits, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls and explicit approval for external actions.

Integrations and data access

Google Drive, Slack, Notion, website crawlers and permitted internal sources. Cloud storage, chat platforms and publishing 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 Google Drive, Slack, Notion and website sources; sync connected data on a schedule. 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

   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 support and operations teams answering questions from connected company documents and communication tools use it to solve "answers are scattered across drives, chat and wikis, so staff search manually and reply without traceable sources"?
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: Answer acceptance rate per support hour and corrections after publication.
4. Measure, then decide. Track answer acceptance rate per support 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 source set and permission model; final policy and customer-facing answers remain human-reviewed. Implement one approved input format, a bounded representative question set and the first two task modules: connect Google Drive, Slack, Notion and website sources; sync connected data on a schedule. Support the third module with operator review: extract, chunk and index PDFs, slides and pages. Include source citations, 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 source-linked answers with named-owner review. Retain the explicit scope boundary: One approved source set and permission model; final policy and customer-facing answers remain human-reviewed.

What the build depends on. Source upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity support requires specialist content QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set and permission model; final policy and customer-facing answers remain human-reviewed.

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 set. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant or custom-agent work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked answers with named-owner review. 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 manual search time while keeping every answer traceable to its source. Demonstrate a concrete source-linked answers with named-owner review using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and operations teams answering questions from connected company documents and communication tools professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked answers with named-owner review from a small authorized input set, with a transparent calculation of answer acceptance rate per support hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams answering questions from connected company documents and communication tools and inspect a recent example of answers scattered across drives, chat and wikis, so staff search manually and reply without traceable sources.
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 answer acceptance rate per support 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: Answer acceptance rate per support 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

Answer acceptance rate per support 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 source-linked answers with named-owner review. 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 sources, permission mappings and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative question cases, reviewer corrections and verified operating constraints for support and operations teams answering questions from connected company documents and communication tools. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Needle, Ragie, Ragie Connect, Query Memory, Casc, SiteAssist, Dashworks Answer API and NewRA.ai. Compare this product with the buyer's present method on answer acceptance rate per support 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

Indexing and retrieval 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 source-linked answers with named-owner review. Track cost per accepted answer, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive answers and publication scope. One approved source set and permission model; final policy and customer-facing answers remain human-reviewed. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.