Make everything your company knows searchable.
We build private AI knowledgebases and assistants using Retrieval-Augmented Generation — so your team can ask questions in plain language and get accurate answers grounded in your own documents, with sources cited.
- Retrieval-Augmented Generation (RAG)
- Internal AI assistants
- Semantic & enterprise document search
- Secure, permission-aware access
Overview
What is Retrieval-Augmented Generation?
General-purpose AI models don't know your policies, contracts, product specs, or procedures — and when they guess, they can be confidently wrong.
Retrieval-Augmented Generation (RAG) solves this by searching your own content first, then giving the model only the relevant passages to answer from. The result is an assistant that answers from your knowledge, cites where each answer came from, and respects who is allowed to see what.
Problems this solves
Knowledge scattered everywhere
Information lives across shared drives, wikis, PDFs, email, and people's heads.
Search that doesn't find anything
Keyword search misses documents that use different wording, so people give up and ask a colleague.
Slow onboarding
New hires spend months learning where things are and how things are done.
The same questions, again and again
Experts spend hours answering repeat questions instead of doing their own work.
What we can build
Capabilities
What we can design and deliver within this practice — always scoped to the problem you actually need solved.
- 01
Retrieval-Augmented Generation systems
Retrieval pipelines that ground AI answers in your content, with citations.
- 02
Private AI knowledgebases
A secure, central knowledge layer built from your existing documents and systems.
- 03
Internal company AI assistants
Assistants that answer employee questions about policies, procedures, and products.
- 04
Enterprise document search
Fast search across thousands of documents and multiple repositories from one place.
- 05
Semantic search systems
Search by meaning, not just keywords, so relevant results surface even with different wording.
- 06
AI-powered knowledge management
Keep knowledge current with automated syncing, freshness tracking, and gap detection.
- 07
Document ingestion pipelines
Parse, clean, chunk, and index PDFs, Office files, web pages, and scanned documents.
- 08
Vector database implementation
Vector storage designed and tuned for retrieval accuracy, speed, and cost.
- 09
Secure conversational interfaces
Chat over company data with authentication, permission-aware retrieval, and audit logs.
Business benefits
What changes for your business.
Answers in seconds
Find the right policy, spec, or precedent without digging through folders.
Trustworthy by design
Every answer links back to its source so people can verify it.
Knowledge that stays
Expertise is captured and searchable — not lost when people change roles.
Secure access
Permission-aware retrieval means people only see content they're allowed to see.
Use cases
Examples of what this looks like in practice.
Illustrative scenarios showing the kinds of problems this service is designed to solve.
Internal policy & HR assistant
- The situation
- Employees email HR and operations with the same policy questions every week.
- What we'd build
- An internal assistant that answers from the handbook and policy library, citing the exact section.
Technical documentation search
- The situation
- Support and field teams dig through manuals and spec sheets to answer customer questions.
- What we'd build
- Semantic search and Q&A across product documentation, available on desktop and mobile.
Contract & document review
- The situation
- Teams need to find specific clauses and obligations across hundreds of agreements.
- What we'd build
- A document intelligence tool that searches, compares, and summarizes contract language with references.
Sales enablement knowledgebase
- The situation
- Sales reps struggle to find current pricing, product details, and approved messaging.
- What we'd build
- A searchable assistant over sales material that drafts accurate answers to prospect questions.
FAQ
Common questions
Don't see your question? Ask us directly.
How is this different from a general AI chatbot?
A general chatbot only knows its training data. A RAG system answers from your documents, cites its sources, stays current as your content changes, and can enforce access permissions.
Is our data kept private?
Your documents can be stored in infrastructure you control, processed only by model providers configured not to train on your data, and protected by authentication and role-based access.
What kinds of content can be included?
PDFs, Word and Excel files, slide decks, wikis, web pages, help-desk articles, databases, and scanned documents via OCR. We can also connect to sources such as shared drives and knowledge tools.
How do you keep answers accurate?
Through careful document processing, retrieval tuning, source citations, and evaluation against real questions from your team. When the system can't find a reliable answer, it says so instead of guessing.
Related solutions
Often combined with
Ready to make your company knowledge searchable?
Tell us about the problem you're trying to solve. We'll help you understand the options, the trade-offs, and what it would take to build.
Step 1
Share the challenge
Tell us what's slowing your team down — a few sentences is enough.
Step 2
Talk it through
A conversation with an engineer to understand your goals and constraints.
Step 3
Get a clear plan
A written proposal with scope, timeline, and cost. No obligation.