AI app for it and development · no coding needed
Private knowledge assistant and agent console
Reduce the number of rented assistants while keeping answers source-linked and inside the organization's own deployment.
Made for: IT and development teams running a private AI assistant over company documents and systems
What it does for you
The problem
Answers are scattered across rented assistants that cannot see all internal sources or stay under the organization's control.
What it gives you
Source-linked assistant and administrator console
What you give it
Permitted internal sourcesmodel choicesagent definitionsaccess rules
Build your own version of Le Chat Enterprise, Omnifact and more
One app with what these 10 AI tools do, yours to keep and change: Le Chat Enterprise, Omnifact, Amazon Q, Claude for Enterprise, Cody, ZBrain, Parallel Labs, Platus YC F24, LyzrGPT, Chat with RTX.
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
How it works, step by step
- Connect permitted internal knowledge sources
- Retrieve relevant passages and generate cited answers
- Build and tailor custom AI agents for defined tasks
- Support cloud, on-premise and private cloud deployment
- Keep sensitive data under organization control
- Switch between approved AI models
- Provide an interface for non-technical users
- Automate routine tasks from approved insights
- Generate context-aware content from internal sources
- Integrate with productivity tools such as Slack and Google Docs
- Offer prompt manager, focus mode and conversation logs
- Provide real-time analysis and reporting
- Retain conversation history across sessions
- Support voice interaction in multiple languages
- Run locally on approved hardware where required
- Handle PDF, DOC and image file types
- List on major cloud marketplaces for procurement
- Guide AI readiness evaluation through deployment
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned source-linked assistant and administrator console with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Private knowledge assistant and agent console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Private knowledge assistant and agent console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the number of rented assistants while keeping answers source-linked and inside the organization's own deployment. For IT and development teams running a private AI assistant over company documents and systems, convert permitted internal sources, model choices, agent definitions and access rules into a source-linked assistant and administrator console. The benefit is a testable hypothesis, measured through accepted answers per support hour and corrections after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted internal sources, model choices, agent definitions and access rules, then follow this sequence: 1. Connect permitted internal knowledge sources. 2. Retrieve relevant passages and generate cited answers. 3. Build and tailor custom AI agents for defined tasks. Resolve uncertain cases with qualified reviewers, approve source-linked assistant and administrator console, and measure accepted answers per support hour and corrections after review against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
