Services
AI Agents
Custom agents that read, reason, and act inside your business, connected to your data and your tools, with guardrails you control. Not just chatbots.
In short
Custom agents that read, reason, and act inside your business, connected to your data and tools, not just chatbots.
A focused agent ships in 4–8 weeks; multi-agent systems in 8–16 weeks.
Who it's for
Teams whose knowledge is locked in documents nobody reads, whose support queues repeat the same questions, or who want AI features inside their product that do real work instead of small talk.
Problems we solve
- Your team answers the same questions all day
- Knowledge lives in docs and chats nobody can find
- You want AI in your product, but not a generic chatbot bolted on
- Monitoring, research, and triage work doesn't scale with headcount
What you get
Deliverables, not promises.
- Agent design: the tools it can use, the data it can see, and the guardrails it obeys
- A working agent integrated with your data and your systems
- A RAG pipeline over your documents, with citations back to the source
- Evaluation and monitoring setup, so quality is measured, not vibes
- Documentation and handover, so your team can tune and extend the agent
How we work
No black boxes.
01
Scope the job
We define exactly what the agent should do, what it must never do, and how we'll measure success.
02
Ground it in your data
Documents, knowledge bases, and systems connected through retrieval, with sources cited.
03
Build & evaluate
Tight loops against real examples from your business, with evals that catch regressions.
04
Ship with guardrails
Human-in-the-loop where the stakes are high, monitoring everywhere, and a clear kill switch.
Technologies
Timeframe
A focused agent ships in 4–8 weeks; multi-agent systems in 8–16 weeks.
Questions
Asked often, answered plainly.
How is an AI agent different from a chatbot?
A chatbot answers from a script or a single model call. An agent plans steps, calls your tools, reads your data, and takes actions (issuing a refund, updating a record, drafting a report) inside limits you define. If it can't act, it's not an agent.
Can an agent use our internal data safely?
Yes. Agents read through retrieval with the same permissions as the systems they connect to. They see only what you allow, every answer cites its source, and sensitive actions can require human approval before they run.
How do you keep agents from making things up?
Three ways: answers are grounded in retrieved documents with citations, outputs are checked against eval sets built from your real cases, and high-stakes actions go through human-in-the-loop review. Hallucination is managed with engineering, not hope.
Which models do you build on?
We're model-agnostic. Frontier models for hard reasoning, smaller or open models where cost and latency matter, chosen per task and swappable later, because the agent's logic lives in your system, not in any one provider.
Also at Trace
Tell us what you're building.
We'll come back within a day with thoughts on scope, approach, and timing.
Book a call