Artificial intelligence
AI integration and LLM development
Quick answer
We put AI to work on the jobs that eat your team's week. Not a pilot that impresses in a demo and dies in month three. Features that sit inside the systems you already run and quietly remove work.
What that looks like in practice
- Support that actually resolves. A chatbot connected to your real documentation and order data, so it answers from facts instead of improvising. It hands over to a human when it should, and you see what it could not answer.
- Document processing. Invoices, forms, contracts and applications read and turned into structured data. This is usually where the fastest payback sits, because the manual version is slow and error-prone.
- Retrieval over your own content. A RAG pipeline so staff can ask questions of policies, case notes or product data and get answers with a source attached.
- Drafting and triage. First-pass replies, summaries and routing, with a person approving before anything is sent.
- Classification and scoring. Sorting enquiries, flagging risk, prioritising a queue.
How we decide what is worth building
We start with the work, not the technology. A short discovery looks at where time actually goes, which of those tasks are repetitive and rule-shaped, and what a mistake would cost. Tasks where an error is cheap and reversible are good first candidates. Tasks where an error is expensive need a human in the loop, and we design that in rather than bolting it on.
Sometimes the honest answer is that a better form or a fixed database query solves your problem for a tenth of the cost. We will tell you when that is the case.
What we build with
Anthropic Claude, OpenAI, and open-weight models where data has to stay in your own infrastructure. Vector search for retrieval, queue-backed workers for anything long-running, and evaluation harnesses so you can tell whether a change made the output better or just different.
Data and compliance
UK GDPR is designed in from the architecture stage, not retrofitted before launch. That means deciding early what leaves your network, what is logged, how long anything is retained, and whether a given model provider is acceptable for the data in question. For healthcare, care services and financial clients this is usually the part that decides whether a project can proceed at all.
Where our experience comes from
The team has built production systems at national consumer scale, including a Diwali engagement platform for Sony LIV and a live IPL gaming platform for Jio, and spent a year on payments infrastructure at ACI Worldwide where defects are not an option. That background shapes how we build AI features: with fallbacks, limits and a plan for the day the model returns something unexpected.
Common questions
Will AI actually save my business money, or is it hype?
It depends entirely on the task. AI pays for itself on repetitive, text-shaped work that currently takes a person hours: reading documents, drafting replies, sorting queues, answering the same twenty questions. It rarely pays on work needing judgement, relationships or accountability. We scope the first project around a task where you can measure hours saved, so you can judge it on evidence.
Do I need to replace my existing systems to use AI?
No, and we would usually advise against it. Most of our AI work sits alongside what you already run, reading from and writing to your existing database, CRM or document store through an API. A rebuild is a separate decision and should be made on its own merits.
What happens to our data when it goes to an AI model?
That depends which model and which plan, and it is a question we settle before writing any code. Business tiers from the major providers typically do not train on your data, but retention periods differ. Where data cannot leave your infrastructure at all, we use open-weight models hosted in your own environment. You get this in writing as part of the proposal.