An AI agent development company that ships to production
We are an AI agent development company that builds agents which survive their first year. That means evaluation sets before features, per-agent identity and scoped credentials, cost tracked per completed task, and a human approval gate on anything irreversible.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Single-process agents
One workflow, done properly: real baseline numbers, shadow mode against live traffic, staged rollout by case category.
Multi-agent orchestration
Supervisor and subagent architectures, but only where the task genuinely needs one. Most do not, and we will say so.
Tool layers on MCP
Your systems exposed once through a stable interface, so the second agent costs a fraction of the first.
Evals and observability
Evaluation sets built from real production traces, plus tracing and cost attribution wired in from day one.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
How is this different from building a chatbot?
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A chatbot answers. An agent decides what to do next at runtime and then does it, which means the hard problems are permissions, failure handling, and cost per completed task rather than prompt wording. Those are what we build for first.
How long does an AI agent take to build?
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Ten to fourteen weeks from scoping to real production volume for a single-process agent, including four weeks running in shadow mode alongside your team. Anyone quoting three weeks is describing a demo.
What if our process is too messy to automate?
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Usually it is not. Your team already works around the same gaps by cross-referencing and escalating, and an agent can do both. The bar is whether a competent new joiner could learn the job in a fortnight.
Which models and frameworks do you use?
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Whatever fits the workload, kept portable. Tool layers on MCP, evaluation sets in your repository, no lock-in to a single provider's proprietary format.
Do we own the agent when you are done?
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Completely, from day one. Code, prompts, tool definitions, evaluation sets, and traces, with clean IP assignment and NDAs as standard.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.