Axtronsols
Retrieval · Vector search · Grounding

RAG & Knowledge Systems

Make your company's knowledge instantly usable.

We turn your documents, databases and tribal knowledge into a retrieval system that gives accurate, grounded, cited answers — the reliable foundation under every serious AI feature you'll build.

Business impact

96%+
Answer accuracy
−60%
Time spent searching
Faster
Onboarding
Scales
Without headcount
The challenge

Your answers exist — nobody can find them fast enough

Knowledge is scattered across PDFs, wikis, tickets and people's heads. Staff and customers waste hours searching, and generic AI tools hallucinate because they don't know your business. RAG fixes both: fast, accurate answers grounded in your own sources.

  • Hours lost searching across scattered systems
  • AI tools that hallucinate or give generic answers
  • Onboarding that's slow because knowledge is undocumented
  • Support repeatedly answering the same questions
Why it pays off

The numbers that matter to you

We build for measurable business impact — lower cost, lighter workload, more revenue, and a fast return on what you invest.

96%+
Answer accuracy

Grounded retrieval with citations cuts hallucination and builds trust in the system.

−60%
Time spent searching

Staff find the right answer in seconds instead of digging through systems.

Faster
Onboarding

New hires get instant, accurate answers instead of interrupting senior staff.

Scales
Without headcount

Handle more questions and customers without adding support staff.

What we build

Everything you need, done right

01

Vector & hybrid search

Semantic plus keyword retrieval tuned for your content, so the right context surfaces every time.

02

Citations & grounding

Every answer links back to the source document, so users can trust and verify what they're told.

03

Continuous indexing

Pipelines that keep your knowledge base fresh as documents and data change — no stale answers.

04

Access-aware retrieval

Respect permissions so people only ever see what they're allowed to see.

How we deliver

A clear path from idea to impact

01

Audit your sources

We inventory where your knowledge lives and assess quality, structure and permissions.

02

Build the pipeline

Ingestion, chunking, embeddings and indexing designed for your content and update cadence.

03

Tune retrieval

We measure retrieval quality with real questions and tune until answers are accurate and cited.

04

Integrate & monitor

We surface it in your product or tools and monitor quality continuously in production.

Where it fits

Common use cases

  • Internal knowledge assistant for staff
  • Customer-facing help & support search
  • Contract, policy & legal document Q&A
  • Research and technical documentation search
  • Sales enablement & proposal assistance
  • Grounding layer for AI agents
Built with

Our toolkit

PythonpgvectorLangChainOpenAIElasticsearchFastAPIPostgreSQL
Legal · Document RAG

Contract Q&A that turns hours of review into seconds

We built a retrieval system over thousands of contracts with clause-level citations, letting the team answer complex questions instantly — with every answer traceable to the source.

−60%
Review time
95%+
Accuracy
1000s
Docs indexed
Good to know

Frequently asked questions

How do you prevent hallucinations?+

We ground every answer in retrieved sources with citations, and add evals that flag low-confidence or unsupported responses.

Can it respect document permissions?+

Yes. Retrieval is access-aware, so users only ever see answers drawn from content they're allowed to access.

Which models do you use?+

Whatever fits your accuracy, cost and privacy needs — from hosted models to open models running in your own cloud.

Ready to put RAG & Knowledge Systems to work?

Tell us what you're working on. We'll come back within one business day with a clear, no-pressure plan.