Private knowledge assistant and agent console

For

IT and development teams running a private AI assistant over company documents and systems

Solves

Answers are scattered across rented assistants that cannot see all internal sources or stay under the organization's control.

Delivers

Source-linked assistant and administrator console

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 the number of rented assistants while keeping answers source-linked and inside the organization's own deployment.

1. Connect permitted internal knowledge sources.
2. Retrieve relevant passages and generate cited answers.
3. Build and tailor custom AI agents for defined tasks.
4. Support cloud, on-premise and private cloud deployment.
5. Keep sensitive data under organization control.
6. Switch between approved AI models.
7. Provide an interface for non-technical users.
8. Automate routine tasks from approved insights.
9. Generate context-aware content from internal sources.
10. Integrate with productivity tools such as Slack and Google Docs.
11. Offer prompt manager, focus mode and conversation logs.
12. Provide real-time analysis and reporting.
13. Retain conversation history across sessions.
14. Support voice interaction in multiple languages.
15. Run locally on approved hardware where required.
16. Handle PDF, DOC and image file types.
17. List on major cloud marketplaces for procurement.
18. Guide AI readiness evaluation through deployment.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned source-linked assistant and administrator console with source references and unresolved questions.

Everything these tools do, in one app

• Connect to internal knowledge sources Links the assistant to company documents and systems so answers are based on internal information.Found in Le Chat Enterprise, Omnifact, Amazon Q and 5 more
• Retrieval-augmented generation Uses retrieval techniques to pull relevant internal content and generate accurate answers.Found in Omnifact, Parallel Labs, Chat with RTX
• Custom AI agent building Lets users create and tailor AI agents for specific business tasks.Found in Le Chat Enterprise, ZBrain
• Flexible deployment options Supports cloud, on-premise, or private cloud setups to match IT needs.Found in Le Chat Enterprise, Omnifact, Amazon Q and 2 more
• Secure data handling Keeps sensitive data protected and under the organization's control.Found in Le Chat Enterprise, Omnifact, Amazon Q and 3 more
• Multi-model support Allows switching between different AI models for flexibility.Found in Omnifact, ZBrain, Parallel Labs and 1 more
• User-friendly interface Provides an intuitive interface for non-technical users to manage AI assistants.Found in Omnifact, Claude for Enterprise, Platus YC F24
• Task automation Automates routine tasks and actions based on AI insights.Found in Amazon Q, Cody
• Content generation Generates coherent and context-aware content for various needs.Found in Amazon Q, Platus YC F24
• Integration with productivity tools Connects with platforms like Slack, Google Docs, and others for seamless workflow.Found in Cody, Parallel Labs, Platus YC F24
• Advanced response tools Includes features like prompt manager, focus mode, and conversation logs to refine responses.Found in Cody
• Real-time data analysis Provides real-time analysis and reporting to assist decision-making.Found in Platus YC F24
• Contextual memory Retains conversation history across sessions for continuity.Found in LyzrGPT
• Speech recognition Allows voice interaction with the chatbot in multiple languages.Found in Chat with RTX
• Local processing Runs locally on hardware for fast and secure processing without cloud reliance.Found in Chat with RTX
• File format support Handles a wide range of file types like PDF, DOC, and images.Found in Chat with RTX
• Cloud marketplace availability Available through major cloud marketplaces for easy procurement.Found in Le Chat Enterprise
• End-to-end AI enablement Guides from AI readiness evaluation to deployment.Found in ZBrain

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted internal sources, model choices, agent definitions and access rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted internal sources

4. 3

   Model choices

5. 4

   Agent definitions and access rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked assistant and administrator console

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 approved deployment target and model set; final access, retention and professional decisions remain with the organization. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connections and permissions, Assistant and agent workspace, Administrator console and audit. Use a source list with sync status, a central question-and-answer canvas with citations, and a right-hand panel for model choice, agent settings and review state. Let users compare answers across models. Display draft, changes requested and approved states. Provide a conversation log with source references and unresolved questions. Make the task-specific outcome source-linked assistant and administrator console visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, source versions, access boundaries, approval states, usage allowances, retention limits, export history and a rights record for supplied material. Add named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Organization-owned document stores, identity providers and permitted productivity tools. Cloud, on-premise and private cloud deployment 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

   5 days

   One buyer segment, one recurring use case; first modules: connect permitted internal knowledge sources; retrieve relevant passages and generate cited answers. 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 IT and development teams running a private AI assistant over company documents and systems use it to solve "answers are scattered across rented assistants that cannot see all internal sources or stay under the organization's control"?
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 support hour and corrections after review.
4. Measure, then decide. Track accepted answers per support hour and corrections after 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 approved deployment target and model set; final access, retention and professional decisions remain with the organization. Implement one approved input format, a bounded representative case set and the first two task modules: connect permitted internal knowledge sources; retrieve relevant passages and generate cited answers. Support the third module with operator review: build and tailor custom AI agents for defined tasks. 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 assistant and administrator console. Retain the explicit scope boundary: One approved deployment target and model set; final access, retention and professional decisions remain with the organization.

What the build depends on. Source upload and sync, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity deployment requires specialist IT QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved deployment target and model set; final access, retention and professional decisions remain with the organization.

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 deployment, on-premise or specialist integration separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked assistant and administrator console. 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 number of rented assistants while keeping answers source-linked and inside the organization's own deployment. Demonstrate a concrete source-linked assistant and administrator console using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development 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 assistant and administrator console from a small authorized input set, with a transparent calculation of accepted answers per support hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams running a private AI assistant over company documents and systems and inspect a recent example of answers scattered across rented assistants that cannot see all internal sources or stay under the organization's control.
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 support hour and corrections after 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: Accepted answers per support hour and corrections after 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

Accepted answers per support hour and corrections after review; 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 assistant and administrator console. 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, access rules 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 running a private AI assistant over company documents and systems. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Le Chat Enterprise, Omnifact, Amazon Q, Claude for Enterprise, Cody, ZBrain, Parallel Labs, Platus YC F24, LyzrGPT and Chat with RTX. Compare this product with the buyer's present method on accepted answers per support hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, retrieval and indexing, 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 assistant and administrator console. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, access boundaries, retention limits and usage permissions. The organization approves substantive changes and deployment scope. One approved deployment target and model set; final access, retention and professional decisions remain with the organization. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.