A dispatcher hands tickets to idle workers on different machines and models.
Projects
Work we have shipped
Systems from our own operations and client work, anonymised: agent fleets, serverless architecture, data pipelines and the dashboards that watch them.
17 projects shown
Numbers shown are illustrative and rounded.
One self-hosted gateway in front of several LLM providers.
Token usage across every runtime and provider in one ledger.
A fintech lead platform built by our agent fleet from approved specs.
A serverless pipeline that turns a place into a narrated audio chapter.
Two review legs on different models, never the one that wrote the code.
Failures become a queue depth metric instead of a lost request.
Agents name a role, never a model id; a registry resolves it.
Big agent jobs wait when subscription limits are close.
Scheduled pulls of partner reports into an archive and a campaign database.
Dev and prod in separate accounts, deployed from one template.
Vector memory that answers with a few relevant facts, or nothing.
A recurring audit finding: tables modelled like Postgres that throttle under real load.
Our open-source Python framework for bootstrapping AWS Lambda functions.
Every agent message to a chat app passes one gated, delivery-checked bridge.
Templates that deploy identically to every account, checked rule by rule.
Which agent work needs a frontier model, and which is mechanical.
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Start with one workflow
The best proof is a running pilot on your real data. Tell us what is slowing you down.
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