AI agents and RAG systems that work in production.
Most AI projects stop at demos. We build systems teams can rely on.
Answer quality
Verified
Rollout
Production-ready
Visibility
Observable
Company Data
Docs, tickets, and internal notes
Retrieval
Rank the right source context
AI Agent
Run prompts, tools, and logic
Verified Answer
Return cited answers and handoffs
Two systems we build again and again.
Reliable retrieval and workflow automation solve real work in production.
Reliable RAG Systems
Turn company knowledge into AI assistants that answer from the right sources, cite their work, and fail safely.
AI Workflow Agents
Automate repetitive operational workflows with agents that can read context, follow rules, and hand work back to teams cleanly.
What changes after the demo.
Reliable systems need grounded context, visible behavior, and clear rules for when to automate and when to escalate.
Ground the system
Good outputs start with good context. We design retrieval, memory, tool use, and decision boundaries before prompt tuning.
Measure it in the open
Every system needs evals, traces, and failure modes you can inspect. We build those in from day one.
Ship where the work happens
The best agent is the one that fits the workflow. We integrate with docs, support queues, GitHub, and internal ops systems.
A RAG assistant should show its sources.
The answer is useful because the user can inspect the evidence behind it.
Generated answer
Enterprise support accounts now require a named escalation owner, a two-hour P1 response target, and a written weekend handoff.
Cited source
Policy PDF p.4
Cited source
Runbook Appendix p.2
Source excerpts
Starting July 1, all enterprise support accounts must assign a named escalation owner. Weekend coverage must include a written handoff. P1 incidents require an initial response within two hours.
GitHub PR review agent
Reads diffs, checks repo rules, and drafts useful review comments.
Incident analysis agent
Clusters error signals and proposes the likely first fix path.
Start Here
If the system has to work after the demo, we should talk.
We scope the workflow, audit the data path, and design the reliability layer before automation starts touching real work.