AI Development & Automation
Practical AI features and automation, deployed with guardrails.
At a glance
LLM-powered features, retrieval systems, agents, and workflow automation that reduce real handling time — with evaluation, fallbacks, and cost control built in.
Typical tooling
- TypeScript
- Python
- OpenAI
- Anthropic
- pgvector
- Cloudflare Workers AI
- Temporal
- LangGraph
Most AI projects fail for boring reasons: no measurable baseline, no evaluation set, no fallback when the model is wrong, and no owner for the workflow after the demo. We start by finding a process with a clear input, a clear output, and someone who currently does it by hand.
We build the boring parts seriously — retrieval quality, prompt and schema versioning, evaluation harnesses, cost and latency ceilings, human review where the stakes justify it, and full audit logs. That is what separates a production capability from a prototype.
We are model-agnostic. We will use whatever combination of hosted models, small self-hosted models, and plain deterministic code gives the best reliability per unit of cost.
What we actually do
Workflow automation
Document intake, classification, enrichment, routing, and notifications across the tools you already use.
Retrieval & knowledge assistants
Internal or customer-facing assistants grounded in your documents, with citations and access controls that respect permissions.
Agentic processes
Multi-step agents with tool access, guarded actions, spend limits, and human approval on anything irreversible.
AI product features
Summarisation, drafting, extraction, and recommendations embedded directly into your own product surface.
Evaluation & monitoring
Golden datasets, automated scoring, regression tests on prompts, and dashboards for quality, latency, and spend.
Data readiness
Cleaning, structuring, and permissioning the source data so the model has something reliable to work with.
How an engagement runs
- 01
Candidate triage
List every candidate workflow, score by volume, handling time, risk, and data availability, then pick one.
- 02
Baseline
Measure how the task is done today — time, cost, error rate — so improvement is provable.
- 03
Pilot
Ship to a small group with human review, and log every output for evaluation.
- 04
Evaluate & tune
Refine retrieval and prompts against the eval set until quality clears the agreed threshold.
- 05
Rollout & handover
Expand scope, train the process owner, and set up ongoing quality and cost monitoring.
Next step
Tell us what needs fixing
Send us the problem in plain language. You will get a considered reply with a recommended next step — not a sales sequence.
We reply to every enquiry within one business day.