Examples
Product-shaped systems, not demo flows.
Each example shows what the interface does and what the system has to prove underneath.
Example 1
RAG Knowledge Assistant
Upload a PDF, ask a question, and inspect the answer against the cited source.
Documents are chunked, retrieved, ranked, and passed into the response layer with source metadata intact.
Talk about a RAG systemGenerated 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.
Developer opens PR
Read the diff, touched files, and repo rules.
AI agent analyzes code
Check risky changes, missing tests, and style issues.
Agent suggests improvements
Draft comments, summary notes, and fix suggestions.
Example 2
GitHub PR Review Agent
The agent reads the diff, checks project rules, and leaves high-signal review notes.
The value comes from the workflow around the model: repo context, rule lookup, traceable comments, and clear action boundaries.
Discuss agent workflowsExample 3
Incident Analysis Agent
Logs flow in. The system groups failures, drafts the likely cause, and suggests the first fix path.
Logs, deploy markers, and prior incident notes all feed the analysis layer so the summary stays tied to observable behavior.
Plan an incident systemSentry logs
Collect grouped errors, stack traces, and deploy markers.
AI analysis
Cluster related failures and compare recent changes.
Root cause summary
Draft the likely cause and recent trigger.
Suggested fix
Suggest the first mitigation or rollback path.
Need A Custom System
We can shape the workflow around your data and operating model.
Good production systems are specific. They reflect your data, failure modes, review habits, and decision boundaries.