Multi-agent orchestration
LangGraph patterns with explicit state machines, not implicit chains.
USE CASE · Agentic systems
Multi-agent systems that ship and stay shipped.
We have built customer-facing agents and internal-ops agents in production. Orchestration, guardrails, evaluation and cost control are the hard parts, and they are where we live.
Built with your teamYour data. Your infrastructure.
Researcher + Writer
Two-agent architecture validated for autonomous device remediation (Armis)
Frontier + cheap
Dual-path execution: expensive LLMs learn the flow once, cheap scripts run it in bulk (Armis pattern)
Challenge → Solution
What's broken, and what we do about it.
This is your problem
This is our solution
Problem
Single-prompt LLM calls don’t solve real workflows — they solve toy problems. You need an agent that researches, decides, executes, and recovers.
Our solution
Multi-agent orchestration via LangGraph with explicit state machines, not implicit chains. (Armis)
Problem
Agents that hallucinate at the wrong moment crash production systems.
Our solution
"Zero-Impact" guardrails — pre-flight checks, "Page Looks Good" UI validation, hard rollback paths. (Armis)
Problem
Frontier models cost too much to run on every step.
Our solution
We separate "learning" steps (high-end models, run rarely) from "execution" steps (cheap models or scripts, run at scale).
Problem
Demoware doesn’t survive contact with real ops.
Our solution
We’ve shipped agents into a Cybersecurity conference demo (Panorays) and into Armis’s MVP roadmap — production-shaped, not notebook-shaped.
Capabilities
What we ship for agentic teams.
Not a framework demo — a production stack: orchestration, guardrails, observability, and cost-tiered routing, all wired into your existing systems.
Production-shaped agents
The pieces that turn an agent demo into a system Operations will actually run on call.
Browser automation as a tool
Playwright integration for agents that need to drive UIs that don’t expose APIs.
Tool-using agents with web research
Tavily and similar integrations for agents that need to ground decisions in current information.
Guardrails & safety
Zero-impact patterns: pre-flight checks, UI validation, rollback-first design. Built for OT-style "no downtime" environments.
Cost-tiered model routing
Claude Sonnet 4.5 + GPT-4o for reasoning, Haiku for cost. Separate "learning" steps from "execution" steps.
Observability
LangFuse for AI workload observability. The same operational rigor as the rest of your production stack.
Engagements
How we deliver agentic systems.
Anchor reference: the Armis pattern — 1-month Assessment → MVP T&M.
Rapid-Impact Intervention
SWAT Team
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.
- End-to-end diagnostic and solution delivery
- Cross-functional team: ML engineers, infra, domain SMEs
- Measurable KPI improvement with defined timelines
Applied Research Partnership
The ML Lab
A dedicated research partnership where we co-develop proprietary models alongside your team — from initial hypothesis through production deployment.
- Joint model development and full knowledge transfer
- Custom algorithms built on your data and objectives
- Structured engagement from discovery to production scale
Risk-Free Experimentation
Simulator
A controlled experimentation environment for validating strategies before they touch production. Test against realistic system dynamics and quantify impact upfront.
- Realistic simulation with historical data replay
- A/B scenario testing for system-level decisions
- Quantified impact forecasting before production rollout
Proof
Agents in production.
Ready to build?
No pitch decks, no generic demos — just a technical conversation about your data and your goals.