AI Knowledge & RAG

Enterprise Knowledge Assistants

The answer to most internal questions already exists, in a wiki page, a policy doc, or a Slack thread from eight months ago. The problem is finding it before pinging the one person who remembers. We build assistants grounded in your actual documentation, so an employee gets a sourced answer in seconds.

• Grounded in your real documentation : Answers come from your wikis, policy docs, and knowledge bases, not a generic model guessing at specifics.

• Fewer repeat questions to experts : Common questions get answered directly, so senior staff stop being a human FAQ.

• Answers that stay current : The assistant re-indexes as source documents change.

• Citations on every answer : Every response links back to the source document, so people can verify it.

• Access that matches your org chart : Only surfaces documents a given employee is already permitted to see.

Document Intelligence

Someone on your team is still keying invoice totals, contract terms, or form fields into a system by hand. It's slow, error-prone, and doesn't scale. We build extraction pipelines that read unstructured documents and turn them into structured data, with validation on anything the model isn't confident about.

• OCR plus LLM extraction : OCR reads the raw text and layout, and an LLM interprets it in context, catching fields pure OCR misreads.

• Structured output from messy formats : Handwritten notes, scanned faxes, and inconsistent vendor layouts all normalize into the same structured fields.

• Confidence scoring on every field : Each extracted value gets a score, so the system knows what it's sure of.

• Exception handling that catches problems : Low-confidence extractions get routed for human review instead of flowing straight into your ERP or CRM.

• Integration into systems you already use : Extracted data lands directly in your ERP, CRM, or accounting system.

Secure Knowledge Search

Plugging your internal documents into a public AI search tool is a fast way to end up with a compliance problem, if the search doesn't respect who's allowed to see what, it becomes a shortcut around every access control you've set up. We build search that's permission-aware from the ground up.

• Permission-aware results, always : Search results are filtered against your existing access control system in real time.

• Encryption at every layer : Data is encrypted in transit and at rest, including the vector embeddings themselves.

• Full query audit logging : Every search and every result shown is logged for compliance.

• On-prem or private cloud deployment : For regulated industries, the entire search stack can run inside your own environment.

• No data leaves without a reason : Sensitive content isn't sent to external services it doesn't need to touch.

RAG Solutions

Building the retrieval pipeline itself, chunking strategy, embeddings, hybrid search, and evaluation that grounds an AI tool's answers in your own data? That's covered in depth on our Microsoft AI Solutions page.