AI & Automation
Turn AI from an experiment into a product.
We build AI features that hold up in production: LLM applications, agents, retrieval systems and automation, with evaluation, guardrails and cost controls designed in from the start.
Most AI pilots never reach production.
A demo that works on ten examples is easy. A system that works on ten thousand, stays within budget, and earns your team’s trust is the actual work.
- Impressive demos that fail on real data
- No way to measure quality or catch regressions
- Unpredictable cost and latency at scale
Evaluation first. Then retrieval, models and guardrails.
Before we write a prompt, we build an evaluation set from your real cases. Every change to retrieval, prompts or models is measured against it, so quality improves deliberately and stays improved.
1Data
- Documents & records
- Product events
2Retrieval
- Chunking & embeddings
- Vector search
3Reasoning
- LLM + tools
- Agent workflows
4Safety
- Guardrails
- Evaluation suite
What we deliver.
The ai & automation work we take on, from the first release to years of evolution.
- AI strategy
- Find the use cases worth building, with a business case and a data-readiness check.
- LLM applications
- Assistants, copilots and document workflows built on leading models.
- AI agents
- Agents that take actions in your systems, with approval steps and audit trails.
- RAG systems
- Retrieval over your documents and data, with citations users can verify.
- Automation
- Workflow automation that removes repetitive work across teams.
- Computer vision
- Inspection, document capture and image understanding.
- Predictive analytics
- Forecasting and scoring models that feed real decisions.
- Models
- OpenAI
- Anthropic Claude
- Google Gemini
- Open-weight models
- Retrieval
- pgvector
- Pinecone
- Elasticsearch
- Orchestration
- LangGraph
- Tool calling
- Python
- Operations
- Evaluation harness
- Tracing
- Cost dashboards
How we deliver.
Every engagement follows the same six steps, scaled to the size of the problem.
- 01
Discover
Understand the business, users and goals.
1–3 weeks
- 02
Define
Architecture, strategy and product roadmap.
1–2 weeks
- 03
Design
UX, UI and interaction systems.
2–6 weeks
- 04
Build
Engineering, integrations and infrastructure.
8–24 weeks
- 05
Launch
QA, deployment and analytics.
1–2 weeks
- 06
Scale
Optimization, iteration and growth.
Ongoing
Related work.
Live products where this work played a part.
Questions, answered.
The things clients usually ask before we start. Anything else, just ask.
Ask us something elseWhich model should we use?
It depends on the task, data sensitivity and budget. We benchmark candidate models on your own evaluation set and often combine a strong model for hard steps with a cheaper one for routine work.
Is our data safe?
We design for data minimization: enterprise API agreements that exclude training on your data, private deployments where required, and access controls that mirror your existing permissions.
How do you reduce hallucinations?
Retrieval with citations, constrained outputs and evaluation sets that catch failures before users do. Where the stakes are high, a person approves the action.
What does an AI pilot involve?
A focused pilot typically runs six to eight weeks with a fixed scope: one use case, an evaluation set, a working prototype on real data and a clear go or no-go recommendation.