Multi-agent orchestration
We design and build multi-agent systems for work that genuinely fans out: many documents, many candidates, many independent checks. Orchestrator and subagent design, parallel execution, and traceability across every thread. We'll also tell you when a single agent is the better answer, which is more often than the category admits.
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.
Orchestrator design
A planner that delegates only genuinely independent work and does the rest itself.
Subagents that stay scoped
Workers that finish the assigned job and escalate blockers instead of improvising around them.
Parallel fan-out
Concurrent execution so wall-clock is the slowest task, not the sum of all of them.
Traceable across threads
Per-agent transcripts and cost attribution, because twelve interleaved logs are an investigation.
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
When is multi-agent actually worth it?
+
When subtasks are genuinely independent and the work fans out. Our test: could you describe each subtask to a contractor in one message and use what comes back without a conversation? If yes, delegate. If you'd need back-and-forth, one agent should do it.
Why do most multi-agent projects fail?
+
Coordination overhead exceeds the benefit. Subagents duplicate work, the orchestrator spends more tokens coordinating than doing, and nobody can debug twelve interleaved transcripts. Usually the fix is fewer agents, not a better framework.
Doesn't running many agents get expensive?
+
It used to be the main objection. Current cheap-tier agentic models have moved that line enough that fan-out is now worth trying on cost grounds where it wasn't a year ago. We still measure it per workload rather than assuming.
How do you stop agents conflicting with each other?
+
Shared mutable state is the trap. Each subagent gets its own scope or its own worktree, and if the work can't be partitioned cleanly, that's a strong signal it shouldn't be parallelized at all.
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.
Start the conversation