From the agent that handles your inbox to the SaaS product your team can sell — we build the AI layer end-to-end, with the evals and infra that make it dependable in production.
We automate the workflows that quietly eat your team's week — ops, support, sales, finance, content — with AI pipelines that run 24/7 with human-in-the-loop where it matters.
Goal-driven agents that plan, call tools, and finish tasks — research, outreach, internal copilots, multi-step workflows — shipped with evals, guardrails, and observability.
Production-grade AI products and apps — built from the prompt layer to the payments layer. We take your idea from a Figma frame to a live, billable SaaS.
Drop intelligent features into the product your users already love — semantic search, smart summaries, copilots, recommendations, voice — without rebuilding the stack.
Private-data assistants over your docs, tickets, and databases — vector stores, retrieval, re-ranking, and grounded answers your team can actually trust.
Frontier and open models — prompt engineering, function calling, fine-tunes, distillation — chosen by the use case and benchmarked, not by the news cycle.
AI woven into the tools you already pay for — HubSpot, Salesforce, Slack, Notion, WhatsApp, Zapier, n8n — so adoption is zero-friction from day one.
Where AI actually moves your P&L — and where it shouldn't. A 30-60-90 plan with shipped pilots, success metrics, and a kill-switch for what doesn't compound.
We benchmark frontier and open models against your use case — context, latency, cost, governance — and pick the one that ships in production, not the one that wins on Twitter this week.
Long-horizon reasoning, careful tool use, and best-in-class writing. Our default for nuanced workflows and customer-facing copilots.
Multimodal speed, function-calling, and the broadest ecosystem. Our pick for voice copilots, fast classifiers, and high-volume retrieval.
Open-weight frontier model — fine-tunable, self-hostable, and the foundation for compliance-sensitive deployments and on-prem agents.
Massive context window and native multimodality. Our choice when the workload is video, very long documents, or Workspace-anchored tasks.
Long-horizon reasoning, careful tool use, and best-in-class writing. Our default for nuanced workflows and customer-facing copilots.
What our agentic systems look like in production — eval dashboards, traces, tool-use logs, and the human-in-the-loop checkpoints that keep quality honest.
Every AI build maps to a P&L line — cost saved, revenue earned, hours back. If it can't be measured against a number, we won't ship it.
Every agent ships with an eval suite, output guardrails, and live observability — so quality is engineered, not vibes-checked.
Full autonomy where the stakes are low. Human review where they aren't. The boundary is designed, not assumed.
We pilot small, measure honestly, then scale what compounds. No six-month discovery decks — production within weeks.
Two recent agentic builds — each measured against a P&L line, each shipped with the eval suite that keeps it honest in production.
2026Autonomous ops agent that triages tickets, posts updates to Slack, and escalates on its own. Pulled 40 weekly hours off the support team in pilot.
2025Private-data assistant grounded on the company's 18,000 policy and SOP docs. Citations, role-based access, and an eval suite that runs nightly.
Their AI ops agent took 40 hours of weekly manual work off our team — and the implementation paid for itself inside the quarter.
We launched our AI-powered SaaS in under 12 weeks. Their team handled the product, the model layer, and the infra — we just sold it.
The internal copilot they shipped is the only tool our managers open before email. That's the bar — and they hit it.
