Agents that plan, act and finish the job
We design multi-agent systems that don't just answer questions — they plan, call tools, and complete real work on their own. From a single task-focused agent to orchestrated fleets with retrieval, long-term memory and safety guardrails, we build agents that hold up in production, not just in a demo.
The tools, languages and frameworks we reach for to ship agentic ai systems.
Model-agnostic by design — we route each workload to the right model for quality, latency and cost.
Anthropic Claude — Opus, Sonnet & Haiku for orchestration, reasoning and fast tool-calling
OpenAI GPT — where a workload calls for it
Open-weight models (Llama, Qwen, Mistral) self-hosted for cost or privacy
Where it runs, how it scales, and how we keep it observable.
We map the workflow, the tools the agent needs, and what "done" looks like.
We wire up the agent loop, tools, retrieval and memory against your data.
A curated eval set gates every change — we ship on measured quality, not vibes.
We deploy to your cloud with monitoring, guardrails and cost controls.
A chatbot answers; an agent acts. Our agents plan multi-step tasks, call real tools and APIs, and complete work end to end — with guardrails and a human in the loop where it matters.
Every agent ships with an evaluation harness — a curated set of input/expected-output cases that runs on every change and gates releases. Plus tracing, cost limits and fallback paths.
We are model-agnostic and route per task — Claude for reasoning and orchestration, faster/cheaper models for high-volume steps, and self-hosted open-weights when privacy or cost demands it.
Tell us the problem — we'll scope a pilot and ship something real.
