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AI Knowledge Base Assistant MVP

Ship a RAG-powered assistant in 1 day. Query your docs with AI, guardrails included, validation-ready.

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RAG (Retrieval-Augmented Generation) turns your documentation, knowledge base, or internal docs into an intelligent assistant. Our 1-Day MVP Sprint delivers a working RAG system with document ingestion, semantic search, and LLM-powered responses—complete with guardrails to keep answers on-topic and accurate.

Examples

What we've built.

Real MVPs shipped in this category.

Customer support assistants trained on help docs

Internal knowledge bases for employee onboarding

Technical documentation search with code snippets

Policy and compliance query systems

Product FAQ bots with source attribution

Considerations

Scoping factors.

Things that affect what fits in a day.

Document corpus size affects ingestion time
Multi-language support adds complexity
Real-time document updates need architecture planning
Fine-tuned models require additional work

Deliverables

What you get.

Typical day-one deliverables.

Hosted RAG assistant with chat interface
Document ingestion pipeline
Semantic search with embeddings
LLM integration (OpenAI, Anthropic, etc.)
Guardrails for hallucination reduction
Source citation in responses

FAQs

Common questions about rag knowledge assistant MVPs.

What document formats can you ingest?+

PDF, Markdown, HTML, and plain text are standard. Complex formats (DOCX, spreadsheets) may need additional parsing work.

How do you handle hallucinations?+

We implement retrieval-based grounding, source citations, and configurable guardrails to keep responses factual and on-topic.

Can this work with private/sensitive data?+

Yes—we can deploy to your cloud with data never leaving your infrastructure. Enterprise deployments may need hardening phase.

What LLMs do you support?+

OpenAI GPT-4, Anthropic Claude, and open-source models (Llama, Mistral). We recommend based on your use case and budget.

Can users provide feedback on answers?+

Basic thumbs up/down feedback is included. Advanced feedback loops for model improvement are hardening phase work.

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