Cross-tool source-linked knowledge assistant console

For

IT, support and operations teams answering questions from information scattered across their work tools

Solves

Answers live in many connected tools, so staff search manually, miss context and repeat work.

Delivers

Source-linked answers with named-owner approval

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce search and answer time while keeping source links and permissions intact.

1. Connect tools and data sources.
2. Search across connected sources in one place.
3. Rank results with AI relevance scoring.
4. Tailor assistants to team workflows.
5. Generate automated digests and reports.
6. Process bulk queries for RFPs and questionnaires.
7. Embed assistants through APIs, MCP and Zapier.
8. Apply customizable filters to refine results.
9. Offer real-time query suggestions.
10. Apply privacy-focused search settings.
11. Run federated search without central indexing.
12. Summarize documents and automate recurring tasks.
13. Connect without duplicating source data.
14. Run autonomous agents for routine tasks.
15. Adapt results from user-provided information.
16. Support content creation and data analysis.
17. Add one-click integrations.
18. Read project context and deadlines.
19. Map entities into a knowledge graph.
20. Use bring-your-own inference models.
21. Surface real-time source updates.
22. Enforce permission-aware retrieval.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned source-linked answer set with source references and unresolved questions.

Everything these tools do, in one app

• Multi-source search Searches across multiple connected tools and data sources to find relevant information in one place.Found in Super, GoSearch, GoSearch Free and 1 more
• AI-powered search Uses AI to improve the accuracy and relevance of search results.Found in Super, GoSearch, GoSearch Free
• Customizable AI assistants Allows users to tailor AI assistants to specific workflows and team needs.Found in Super
• Automated digests Generates reports and documentation automatically.Found in Super
• Bulk query processing Handles large volumes of queries simultaneously, useful for RFPs and security questionnaires.Found in Super
• Integration via APIs Embeds assistants into existing workflows and web applications through APIs, MCP, and Zapier.Found in Super
• Customizable filters Provides user-friendly filters to refine search results.Found in GoSearch
• Real-time query suggestions Offers real-time suggestions to refine search queries as you type.Found in GoSearch
• Privacy-focused settings Includes secure search options with privacy-focused settings.Found in GoSearch
• Federated search Searches across connected applications in real-time without central data indexing.Found in GoSearch Free
• Personal AI agent Summarizes documents, analyzes content, and automates recurring tasks.Found in GoSearch Free
• No data duplication Connects directly to data sources without duplicating data, enhancing security.Found in GoSearch Free
• Autonomous AI agents Performs tasks and provides insights independently without manual intervention.Found in Knowlee AI
• Customizable AI learning Adapts based on the information provided by the user to improve results.Found in Knowlee AI
• Content creation support Assists with creating content and performing in-depth data analysis.Found in Knowlee AI
• One-click integrations Connects to third-party tools with a single click, eliminating setup barriers.Found in Directory by Claude
• Contextual understanding Enables the AI to access project details, understand deadlines, and interact within connected apps.Found in Directory by Claude
• Knowledge graph mapping Ingests and links entities across systems to make relationships between people, projects, decisions, and customers explicit.Found in Pensieve
• Bring-your-own inference Works with Anthropic, OpenAI, or Google models so teams control which model runs queries and inference.Found in Pensieve
• Real-time updates Uses subscriptions and background workers to surface new insights as source systems change.Found in Pensieve
• Permission-aware architecture Supports per-user organizations and explores source-level permission tagging with retrieval-time filtering.Found in Pensieve

What goes in, what comes out

The workflow

1. In
   Start with

   Connected tool content, team questions and permission rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected tool content

4. 3

   Team questions and permission rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked answers with named-owner approval

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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. One fixed source set and permission model; final answers and access decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Connected sources and permissions, Assistant and query console, Answer review and delivery. Use a source list with connection state, a central query and answer canvas, and a right-hand panel for filters, citations and comments. Let users compare answers side by side. Display draft, changes requested and approved states. Provide a shareable answer link with citations anchored to the source record. Make the task-specific outcome source-linked answers with named-owner approval 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 connected tools, authorized documents and permitted research sources. Cloud storage, identity providers and destination tools. 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 tools and data sources; search across connected sources in one place. 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, support and operations teams answering questions from information scattered across their work tools use it to solve "answers live in many connected tools, so staff search manually, miss context and repeat work"?
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 time per resolved question and corrections after approval.
4. Measure, then decide. Track answer time per resolved question and corrections after 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 model; final answers and access decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect tools and data sources; search across connected sources in one place. Support the remaining modules with operator review: rank results with AI relevance scoring; tailor assistants to team workflows; generate automated digests and reports; process bulk queries for RFPs and questionnaires; embed assistants through APIs, MCP and Zapier; apply customizable filters to refine results; offer real-time query suggestions; apply privacy-focused search settings; run federated search without central indexing; summarize documents and automate recurring tasks; connect without duplicating source data; run autonomous agents for routine tasks; adapt results from user-provided information; support content creation and data analysis; add one-click integrations; read project context and deadlines; map entities into a knowledge graph; use bring-your-own inference models; surface real-time source updates; enforce permission-aware retrieval. 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 source-linked answers with named-owner approval. Retain the explicit scope boundary: One fixed source set and permission model; final answers and access decisions remain human.

What the build depends on. Source connection and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed source set and permission model; final answers and access 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 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 source-linked answers with named-owner approval. 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 search and answer time while keeping source links and permissions intact. Demonstrate a concrete source-linked answers with named-owner approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT, support 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 source-linked answers with named-owner approval from a small authorized input set, with a transparent calculation of answer time per resolved question and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five IT, support and operations teams answering questions from information scattered across their work tools and inspect a recent example of answers live in many connected tools, so staff search manually, miss context and repeat work.
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 time per resolved question and corrections after 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: Answer time per resolved question and corrections after 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

Answer time per resolved question and corrections after approval; 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 approval. 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 connectors, permission mappings and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT, support and operations teams answering questions from information scattered across their work tools. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Super, GoSearch, GoSearch Free, Knowlee AI, Directory by Claude and Pensieve. Compare this product with the buyer's present method on answer time per resolved question and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, connector maintenance, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked answers with named-owner approval. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, permission boundaries and usage permissions. Named owners approve substantive answers and access scope. One fixed source set and permission model; final answers and access decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.