We know where your knowledge lives.
With decades of experience integrating enterprise systems, we know how to connect AI to ERP, PIM, CRM, technical documentation, support platforms, and the other systems that power your business.
What it is
Knowledge Grounding connects AI to your enterprise data so it can answer questions using your own information—not assumptions.
Large language models know the public internet. They don't know your products, customers, contracts, technical documentation, or the countless decisions that make your business unique.
Every response is grounded in your systems and documents, giving users confidence that the answer is accurate, current, and verifiable.
It's the foundation behind every AI application we build. Whether it's helping buyers find the right product, supporting sales teams with account knowledge, or enabling operations to search millions of technical documents, grounded knowledge makes every AI experience more reliable.
WHY AAXIS
Why AAXIS
Governance comes built in.
Knowledge retrieval respects existing security permissions, ensuring employees and AI agents only access the information they're authorized to see while supporting compliance and audit requirements.
We build for trust.
Grounding isn't just connecting documents. We optimize retrieval so AI consistently finds the right information, cites its sources, and delivers answers users can trust.
You own the knowledge layer.
A reusable foundation your other AI applications retrieve from, not a black box locked to one vendor.
WHAT WE BUILD
Retrieval that answers from your content
The problem
Teams and agents need answers buried across documents, and a generic model invents plausible-sounding ones.
The solution
Retrieval-augmented generation (RAG) over your indexed content, so every answer is drawn from your material and carries a citation back to the source.
WHAT WE BUILD
A knowledge layer over fragmented sources
The problem
The same knowledge is scattered across PIM, file shares, wikis, and past conversations.
The solution
Ingestion, chunking, and embedding into vector, document, and conversation stores — with a semantic layer and knowledge graph where relationships matter — so one trusted layer spans them all.
WHAT WE BUILD
Grounding tuned for accuracy and trust
The problem
A retrieval system that returns the wrong passage is worse than none.
The solution
Retrieval tuned and evaluated for accuracy and citation quality, with access controls respected and content kept fresh as your sources change.
WHERE IT'S USED
Knowledge Grounding powers AI across the enterprise, including:
- Conversational commerce that answers detailed product and compatibility questions.
- Product intelligence that searches specifications, technical documentation, and regulatory content.
- Sales copilots that surface customer history, pricing, and product expertise during conversations.
- Service and compliance assistants that validate interactions against policies, procedures, and approved documentation.
- Operations teams that can search years of institutional knowledge in seconds instead of hours.
HOW IT'S BUILT
The technology behind trustworthy enterprise AI
The retrieval stack beneath your agents, built on your content and governed for access.
- 01Data ingestion
What it does: connects enterprise systems and continuously indexes content.
- 02Knowledge layer
What it does: organizes structured and unstructured information using vector databases, document stores, and knowledge graphs.
- 03Retrieval
What it does: uses semantic and hybrid search to deliver accurate, cited answers.
- 04Governance
What it does: applies permissions, content freshness, lineage, and audit controls across the knowledge layer.
PROOF
In production today
Representative engagements — grounding built into a working agent. Client names are under NDA.
01
Grounded product knowledge — HVAC distribution
THE PROBLEM
The conversational commerce agent for an HVAC distributor needed to answer compatibility and spec questions buyers used to phone in.
THE SOLUTION
We indexed the product knowledge and tuned retrieval so the copilot answers from it directly, with the source behind each answer.
THE RESULT
Spec-accurate self-service is what lets two-thirds of B2B buyers — who now prefer a rep-free experience (Gartner) — actually serve themselves.
TECHNOLOGY
Retrieval & RAG — hybrid and semantic search, re-ranking, citation, and retrieval evaluation.
02
Retrieval over technical & regulatory documents — chemical manufacturing
THE PROBLEM
A chemical manufacturer's specifications lived across millions of technical documents, safety data sheets, and regulatory files.
THE SOLUTION
We built the retrieval layer that the product information agent reads from, so specs and attributes surface accurately and traceably.
THE RESULT
AI-assisted enrichment grounded this way cuts content prep from ~20 minutes per SKU to ~2 (≈90% faster) while keeping answers auditable.
TECHNOLOGY
Ingestion & indexing — source connectors, chunking, embedding models, and refresh pipelines.
FAQs
Knowledge Grounding FAQs
Structured for answer engines — the questions buyers ask AI assistants, answered plainly.
Retrieval-augmented generation — the layer that lets AI answer from your own documents and data accurately, with sources, instead of relying on what a general model happened to be trained on.
We tune retrieval for accuracy and citation and evaluate it against real questions, so answers are grounded in your content and every one can be traced to its source.
Yes. Retrieval is permission-aware, so people and agents only see what they're cleared for — and it ties into our AI Governance layer for audit.
You need grounding for any agent that answers from your knowledge. Building it as a reusable layer means the next application retrieves from the same foundation rather than starting over.
Industries we serve
Industries
Building Materials
Streamlining operations through enterprise digital services and automation.
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