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Turn scattered knowledge into answers.

Production-ready RAG and LLM systems — chatbots, autonomous assistants, and secure MCP-integrated workflows — that turn your enterprise's scattered knowledge into accurate, measurable outcomes.

Grounded in your dataMCP-secureCited answers
rag.assistant
grounded
Which vendor contracts renew in Q3, and which need notice?
retrieving from your knowledge
renewals.csvcontracts_2024.pdfvendor_policy.md
Grounded in your sources0 hallucinations
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Faster support resolution
0%
Fewer escalations
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Semantic search accuracy
0wks
From POC to production
How it works

Answers grounded in four steps.

Retrieval-Augmented Generation grounds every response in your own data — so assistants answer from what's true for your business, with citations.

01

Query

A question arrives from a user, app, or workflow.

02

Retrieve

Vector search pulls the most relevant chunks from your data.

03

Augment

Retrieved context is injected into the model's prompt.

04

Generate

The LLM writes a grounded answer, with citations.

What we deliver

Everything you need to ship generative AI.

From the retrieval layer to the assistant your teams actually use every day.

Production chatbots & RAG pipelines

Retrieval-augmented assistants that answer from your knowledge, with citations and guardrails.

Conversational workflows (MCP)

Secure, permissioned connectors so assistants can act inside enterprise systems, not just talk.

Contextual retrieval & semantic search

Vector search across documents and databases with up to 95% semantic accuracy.

Document ingestion & knowledge bases

Pipelines that turn PDFs, wikis, and tickets into a searchable, always-current knowledge layer.

Custom virtual assistants

Domain-tuned assistants for support, sales, and internal teams — built around your workflows.

Voice assistants

Speech-enabled assistants for hands-free and telephony use cases.

In practice

Where teams put it to work.

Customer support automation
Internal knowledge search
Document Q&A and summarization
Contract and policy lookup
Employee self-service
Research and analyst assistants
Tools & platforms

Built on the modern LLM stack.

The current best tools for retrieval, orchestration, and evaluation — matched to your data and constraints.

OpenAIAnthropic ClaudeHugging FaceMistralLangChainLlamaIndexPineconeMilvusWeaviateChromaFAISSLangSmithMCP
Common questions

Questions about Generative AI.

RAG, safety, data security, and time to launch.

  • Retrieval-Augmented Generation grounds an LLM's answers in your own data. Instead of guessing, the model retrieves the most relevant chunks from your documents and answers from them — with citations — which is what makes enterprise assistants accurate and trustworthy.

Work with us

Put your knowledge to work.

Tell us where answers live today — the wiki, the PDFs, the tickets. We'll show you the fastest path to an assistant that actually uses them.

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Engineering process
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