AI & Automation
AI systems that run in production, not just in demos
Production agentic systems, deterministic workflow orchestration, and bespoke LLM integrations.
Why AI projects stall or can’t be trusted.
Prototypes that never reach production
A convincing demo has no evaluation, observability or failure handling, so it cannot be trusted with real work or real customers.
Manual workflows that do not scale with volume
Skilled people spend their time on repetitive triage, extraction and routing that a governed automated system could handle.
LLM behaviour that is unpredictable and unauditable
Outputs vary run to run, costs are opaque, and there is no record of why a system took a given action.
AI that runs with limits, checks and a record.
- Bounded autonomy
Agentic AI systems with bounded autonomy
Tool-using agents with explicit permissions, human approval points and audit trails for every action taken.
- Retryable
Deterministic workflow orchestration around LLMs
Durable, retryable pipelines where the model handles the fuzzy step and code handles control flow, validation and recovery.
- Measured
Bespoke LLM integrations and evaluation
Retrieval, structured output and evaluation harnesses wired into existing systems, with cost and quality measured continuously.
Technologies involved
- Python
- TypeScript
- PostgreSQL
- pgvector
- Temporal
- OpenTelemetry
How we work
AI & Automation work is delivered through one of three engagement models: build, embed or operate.
- Build
- Embed
- Operate
Client endorsement
The agent went into our live support workflow without disrupting it, and every decision it makes can be traced.
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Discuss AI & Automation with an engineer
Speak directly with a senior engineer about your systems, scope, and timeline.