AI Features & Agent Integrations
AI in your product, not on your roadmap. We build the features your users actually touch — chat, intake, extraction, agent access — with the guardrails, logging, and human review that keep them alive in production.
What we build
Chat & Assistants Grounded in Your Data
Customer-facing and internal assistants that answer from your documentation, catalog, and records — not from the model's imagination. Retrieval, citations, and escalation paths to a human when confidence drops.
AI Intake & Triage
Qualify, route, and prioritize the requests flooding your inbox and forms. AI proposes the classification; your team makes the consequential calls. See how our intake & triage systems work →
Agent APIs & MCP Servers
Your customers increasingly have AI assistants of their own. We build the scoped, token-authenticated APIs and MCP servers that let those agents use your product safely — read what they're allowed to read, act where you permit it, and never touch what they shouldn't.
Document & Data Extraction
Turn PDFs, emails, spreadsheets, and legacy exports into structured data your systems can use. Typed outputs, validation at the boundary, and audit trails for every extraction.
Built to survive production
The demo is the easy part. The difference between an AI feature and an AI liability is everything around the model — and that's where we spend our time.
Guardrails & Typed I/O
Structured inputs and outputs, policy checks, rate limits, and scoped permissions. The model can only do what the system lets it do.
Human-in-the-Loop
Consequential decisions get a human reviewer by design. AI handles the volume; your team keeps the judgment calls.
Logging & Cost Visibility
Every prompt, response, and tool call logged and queryable. Monthly numbers on tokens, latency, escalation rate, and savings versus manual.
This is the approach documented in our Responsible AI practice — it's not a compliance page, it's how the features are actually built.
We run this ourselves
Our own client portal exposes a scoped agent API and MCP server — our clients' AI assistants read project status and file requests through it, in production, today. Our delivery pipeline runs on coding agents that turn approved tickets into reviewed pull requests. Our intake triage runs on the same patterns we sell.
When we propose an AI feature for your product, it's because we've already operated something like it with our own clients on the line — not because it demoed well.
Have a feature in mind?
Tell us what your users need and we'll tell you honestly whether AI is the right tool — and what it will take to run it well.
Start a Conversation15-minute technical deep dive. No sales deck, just a conversation with an engineer.