We’ve built customer-facing agents and internal-ops agents in production. The hard parts — orchestration, guardrails, evaluation, cost control — are where we live.
Single-prompt LLM calls don’t solve real workflows — they solve toy problems. You need an agent that researches, decides, executes, and recovers.
Multi-agent orchestration via LangGraph with explicit state machines, not implicit chains. (Armis)
Agents that hallucinate at the wrong moment crash production systems.
"Zero-Impact" guardrails — pre-flight checks, "Page Looks Good" UI validation, hard rollback paths. (Armis)
Frontier models cost too much to run on every step.
We separate "learning" steps (high-end models, run rarely) from "execution" steps (cheap models or scripts, run at scale).
Demoware doesn’t survive contact with real ops.
We’ve shipped agents into a Cybersecurity conference demo (Panorays) and into Armis’s MVP roadmap — production-shaped, not notebook-shaped.
Not a framework demo — a production stack: orchestration, guardrails, observability, and cost-tiered routing, all wired into your existing systems.
The pieces that turn an agent demo into a system Operations will actually run on call.
Anchor reference: the Armis pattern — 1-month Assessment → MVP T&M.
A high-impact strike team that diagnoses, architects, and ships. We bring the ML engineers, infra, and domain expertise needed to deliver measurable lift within weeks.
A dedicated research partnership where we co-develop proprietary models alongside your team — from initial hypothesis through production deployment.
A controlled experimentation environment for validating strategies before they touch production. Test against realistic system dynamics and quantify impact upfront.
No pitch decks, no generic demos — just a technical conversation about your data and your goals.