Software & Integrations
Your documents. Your AI.
Your answers — on your terms.
A Private RAG Knowledge Base lets your team ask plain-language questions and get grounded, cited answers drawn directly from your own internal documents — policy manuals, contracts, case files, clinical protocols — without exposing any of it to a public cloud service. We build, deploy, and tune the entire system on infrastructure you control.
How It Works
What is Retrieval Augmented Generation?
RAG is the dominant architecture for giving AI reliable, verifiable answers about your specific information. Instead of asking a language model to recall facts from its training data (which can be outdated or hallucinated), RAG first retrieves the most relevant passages from your own document library, then asks the model to generate an answer grounded in what it was just shown.
The result is an AI that can answer questions like "What does our employee handbook say about remote work?" or "Which of our supplier contracts include a force majeure clause?" — accurately, with citations, and without any of your documents ever leaving your network.
We build the entire stack: the ingestion pipeline that processes your documents, the vector database that indexes them, the retrieval logic that finds the right passages, and the interface your team actually uses to ask questions.
What You Get
A full-stack knowledge system, built for your environment.
Document ingestion pipeline
We build a reliable pipeline that pulls from your sources — shared drives, email, scanners, web portals — and normalizes everything into a consistent format for indexing.
Vector store & semantic search
Your documents are chunked and embedded into a high-dimensional vector index optimized for semantic search. We select and tune the embedding model to match your content type and language.
Retrieval & generation layer
Queries are matched against the most relevant document chunks and passed — with context — to a language model that synthesizes a grounded, cited answer. No hallucinations from thin air.
User-facing interface
Your team gets a clean, intuitive chat-style interface — or an API endpoint — to ask questions and get answers. We design it to match your workflows, not the other way around.
Granular access controls
Role-based access ensures staff only see what they are authorized to see — critical for organizations with multiple departments, privilege levels, or regulatory requirements.
Private by design
The entire system runs on your infrastructure — on-premises or private cloud. Your documents never leave your environment, and no third-party AI service ever sees your data.
Who It's For
Any organization with documents they can't afford to expose.
Law Firms & Legal Departments
Instant answers from case files, contracts, and precedent libraries. Attorney-client privilege stays intact because the AI never leaves your environment.
Healthcare Providers
Clinical protocols, intake forms, and compliance documentation — searchable by AI without any PHI touching a public cloud. HIPAA-aligned by default.
Financial Services
Internal policy, regulatory guidance, and client records — queried privately with full audit trails. Built for GLBA and FINRA-sensitive environments.
Manufacturing & Operations
Equipment manuals, quality standards, and SOPs made instantly accessible to floor staff via natural language — even on isolated or air-gapped networks.
Don't see your industry? We work with non-profits, school districts, logistics companies, and more.
The Process
From your files to a working knowledge base.
Document audit
We inventory your sources, understand access patterns, and identify what's worth indexing and what isn't.
Pipeline build
We stand up the ingestion, chunking, embedding, and indexing pipeline — tested against your actual documents and edge cases.
Interface deploy
We deploy the query interface or API, configure access controls, tune retrieval quality, and validate outputs.
Handoff & training
Your team learns to use and manage the system. We document everything and provide ongoing support options.