What it takes to put an AI agent into production
An agent that works in a demo is a long way from one you can trust with real customers and real systems. Here is what closes the gap.
Service 05
AI that does useful work, reliably, inside your real systems
We build AI features and automation that connect to your data and tools: assistants that answer from your own documents, agents that complete multi-step tasks, and pipelines that turn unstructured input into structured records. Every system is designed to be measured, monitored and kept under human control.
- Outcome
- Repetitive, judgement-light work is handled by software, and your people spend their time where it counts.
- Typical stack
- Claude, OpenAI, LangChain, Python, PostgreSQL, TypeScript, Docker
When this is the right call.
Teams drown in documents
Staff spend hours reading, extracting and re-keying information from emails, PDFs, forms or contracts into other systems.
Knowledge is hard to find
Answers exist somewhere in policies, wikis, tickets or past projects, but finding them takes too long and depends on asking the right person.
A prototype won't reach production
An AI demo impressed people, but it is unreliable, expensive to run or impossible to evaluate, and nobody is confident putting it in front of customers.
You want AI in your product
Customers expect smarter search, summaries or assistance inside your software, and you need it built responsibly and cost-effectively.
What you get.
Assistants grounded in your data
Retrieval-augmented assistants that answer from your documents and systems, with sources cited so answers can be checked.
AI agents and workflow automation
Agents that carry out multi-step tasks across your tools, with clear permissions, approval steps and logs of every action taken.
Document processing pipelines
Extraction and classification of emails, invoices, forms and contracts into structured data, with confidence checks and human review where needed.
AI features inside your product
Search, summarisation, drafting and classification built into your existing application and user experience.
Evaluation and monitoring
Test sets, quality scoring, cost tracking and alerting so you know how the system performs and when it changes.
How we approach it.
We start by picking a narrow, valuable task and defining what good looks like before building anything. That means collecting real examples, agreeing how outputs will be judged and deciding where a person must stay in the loop. A system that reliably handles one well-defined job is worth far more than a general assistant that is right most of the time.
Models are one component among many. Most of the engineering sits around them: retrieving the right context, often from PostgreSQL with pgvector; validating structured outputs; handling failures and retries; restricting what an agent is allowed to do; and logging every step so behaviour can be audited. We work with models from Anthropic and OpenAI and design so you can switch providers as prices and capabilities change.
Evaluation is continuous, not a one-off. We build test suites from real cases, run them whenever prompts or models change, and track cost and latency alongside quality. Data handling is agreed up front, including what is sent to which provider and how long it is kept, so the system fits your privacy and compliance obligations. The code, prompts and evaluation data are yours.
Tools we use for this work.
From first conversation to production.
- DiscoverUnderstand the business, the users and what success looks like.
- DefineRequirements, architecture, scope and a roadmap you can plan around.
- DesignThe product experience, tested before it is expensive to change.
- BuildEngineering in short cycles, with working software to review every sprint.
- LaunchTesting, deployment and making sure it holds up in production.
- EvolveSupport, improvement and scaling once real usage starts.
Where we apply it.
- Professional ServicesClient portals and workflow tools for advisory firms
- Healthcare & Health TechClinically safe, interoperable software for health and care
- Fintech & Financial ServicesSecure, auditable software for regulated financial products
- Logistics & Supply ChainTracking, field apps and integrations for moving goods
Common questions.
How do you stop AI from making things up?
You cannot eliminate errors entirely, so the design has to account for them. We ground answers in your own data with cited sources, constrain outputs to structured formats that can be validated, and route low-confidence cases to a person. Ongoing evaluation against real examples shows how often mistakes happen and whether changes improve things.
Is our data safe if we use AI models?
We agree data handling before building: which data is sent to a model, which provider processes it, under what terms, and how long anything is retained. Business API terms from the major providers generally exclude your data from training, and sensitive fields can be removed or masked before they leave your systems.
What does an AI project cost to build and run?
Build cost depends on the number of systems involved, data quality, and how much evaluation and human review the use case needs. Running cost depends on volume and the models used; we measure it from the start and optimise by choosing the smallest model that meets the quality bar and caching where possible.
Should we use Claude, OpenAI or an open-source model?
It depends on the task, cost, latency and data requirements, and the best choice changes over time. We test candidate models against your own examples and build with an abstraction layer, so switching later is a configuration change rather than a rewrite.
Can AI agents act in our systems safely?
Yes, with sensible limits. Agents get the minimum permissions needed, sensitive actions require human approval, and every step is logged. We start with read-only or draft-only behaviour and widen autonomy only once performance has been proven on real work.
Often paired with.
- APIs & IntegrationsCustom APIs and system integrations that connect CRMs, ERPs, payment providers and internal tools.
- Custom Software DevelopmentBespoke business systems, internal platforms and workflow tools designed around your processes and owned outright by you.
- Cloud & DevOpsCloud architecture, infrastructure as code, CI/CD pipelines, monitoring and cloud cost optimisation.
Have a product worth building?
Tell us what you are trying to achieve. The first conversation is about understanding the problem — not selling you a solution.