Knowledge Agents
Answers from your own knowledge, with the source attached.
Your team has the information, spread across wikis, drives, tickets, and warehouses. They can’t find it fast enough, or trust it when they do. Knowledge agents retrieve, ground, cite, and synthesize across all of it. We engineer them, and we run them in production.
Part of
02Engineer
Turn possibility into production.
Data & knowledge engineering
Where this sits in the lifecycle
The frontier capability behind it
Agents are the mechanism. The capability is the point.
Reasoning systems
Models work through policy, rules, and edge cases, and show how they reached the answer.
Where it landsEligibility, underwriting, compliance review
Multimodal AI
Documents, images, and forms become structured data without anyone keying them in.
Where it landsInvoices, medical records, field photos
The problem
The problem isn’t a lack of data. It’s finding and trusting it. Wikis go stale, shared drives become graveyards, and generic AI fills the gaps with confident guesses.
- Knowledge spread across five or more systems searched separately
- Generic AI tools that invent answers to domain questions
- Stale documents nobody knows not to trust
- Compliance and legal needing every answer cited
How we work
Four steps. One team the whole way.
- 01
Map the knowledge
Audit where answers live, what to ground the agent in, and what to keep out.
- 02
Design retrieval
Chunking, embeddings, hybrid search, and reranking tuned to how your people actually ask.
- 03
Engineer it in
Wired into your identity, your data, and your audit log, with citations from day one.
- 04
Govern and tune
Watch what’s asked, what’s cited, and what’s drifting, and keep the corpus current.
What’s included
What knowledge agents covers. In practice.
Hybrid retrieval
Vector search, keywords, and reranking, tuned to what works on your data.
Citation-first design
Every answer comes with its source, so it can be checked.
Permission-aware
Your existing access controls apply to what the agent can read.
Multi-source synthesis
One question answered across wiki, SharePoint, Salesforce, and the warehouse at once.
Freshness tracking
Stale documents are flagged, and low-confidence answers go to a person.
Domain tuning
Retrieval and prompts tuned to your vocabulary, not generic web language.
What you can hold us to
Commitments. Not marketing ranges.
What every build commits to. Performance is measured on your own work during the engagement, not quoted from industry ranges.
- every answer
- Cited
- every answer
- Links, pages, and passages back to the source
- aware
- Permission
- aware
- People only retrieve from documents they can already see
- when it doesn’t know
- Says so
- when it doesn’t know
- Low-confidence questions route to a subject expert
Go deeper
Other kinds of work agents take on.
- EngineerTurn possibility into production. Build the applications, agents, integrations, and data infrastructure that put AI inside real business workflows.
- Workflow AgentsTake the manual workflow off your team, step by step.
- Customer-Facing AgentsResolve the request, not just reply to it.
- Multi-Agent SystemsFor the process that crosses systems, teams, and decision rights.
- Agent operationsOnce it’s live, someone has to own it. How we run agents in production and keep them improving.
Bring us your documentation. We’ll make it answer.
Wherever the answers actually live: wikis, PDFs, ticket history, someone’s head. We’ll show you what it takes to get cited answers out of it.