Turn your enterprise data into answers your team can act on
Your team has the data — in wikis, drives, ticketing systems, data warehouses. They can't find it fast enough. Knowledge agents retrieve, ground, cite, and synthesize across your entire knowledge surface. We design them, build them, and run them in production.
Trusted by teams building and running mission-critical platforms
The challenge with the status quo
Your data isn't the problem — your team's ability to find and trust it is. Wikis go stale. SharePoint becomes a graveyard. Even when the answer exists, no one finds it in time. Generic LLMs hallucinate. The fix isn't more data — it's grounding the agent in yours, with provenance.
How we design, build, and run them
Our approach combines product thinking, engineering discipline, and operational rigor to deliver knowledge agents that work in production — not in a slide deck.
Knowledge mapping
We audit your knowledge surface — wikis, drives, ticketing systems, data warehouse, internal apps. We figure out what to ground the agent in, and what to leave out.
Retrieval architecture
We design the retrieval pipeline: chunking, embeddings, hybrid search, rerankers. Tuned to how your team actually asks questions.
Production engineering
We build the agent on Azure, .NET, and Semantic Kernel — wired into your auth, your data, your audit log. Citations and confidence scores from day one.
Governance & tuning
We monitor what's being asked, what's being cited, and what's drifting. We update the corpus and retrieval params as your knowledge evolves.
What you get
Numbers from real engagements, not marketing decks.
Share of answers that cite the correct source document
citation accuracy
Typical improvement vs. existing search across your knowledge surfaces
faster retrieval
When the agent doesn't know, it says so. No hallucinations on grounded queries.
made-up answers
What these agents do
Production-grade capabilities, designed for enterprise systems and operational scale.
Hybrid Retrieval
Vector search + keyword + reranking. The right answer doesn't always live in the most semantically similar chunk — we tune for what works on your data.
Citation-First Design
Every answer comes with the source — links, page numbers, paragraph anchors. Auditable by default.
Permission-Aware
Agents respect your existing access controls. Users only retrieve from documents they're allowed to see.
Multi-Source Synthesis
Agents pull from multiple systems in a single query — wiki, SharePoint, Salesforce, data warehouse — and synthesize the answer.
Freshness Tracking
Agents know when a document is stale. They flag outdated answers and route to a human SME when confidence drops.
Domain Tuning
Embeddings, prompts, and retrieval params tuned to your domain vocabulary — not generic web language.
The engagements that build knowledge agents
Most knowledge agents engagements start with strategy, move through engineering, and end up in operations. Pick where you are.
Agent Strategy
You know AI agents are the move. You don't know which workflows to target first. We run sprints against your real data and tell you exactly where agents will deliver.
View engagementAgent Engineering
Chatbots answer questions. Agents reason through problems, call APIs, update systems, and handle the task end-to-end. We design multi-agent systems with guardrails, escalation paths, and human oversight built in.
View engagementAgent Knowledge Layer
Your agents are guessing because they don't have context. We build the knowledge and retrieval layer that gives them access to your docs, your data, and your terminology.
View engagement
Ready to ship?
Tell us what you want an agent to own. We'll help you scope where it acts, where humans stay in the loop, and how to ship it to production.
Schedule a discovery call