AI & Machine Learning Development
The hard part of applied AI is rarely the model. It is grounding it in your data, measuring whether the output is actually good, and designing what happens when it is wrong — which it will sometimes be.
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What this covers
LLM integration
Language model features built into existing products — drafting, summarising, extraction, classification and conversational interfaces — against OpenAI, Claude and open models.
Retrieval-augmented generation
Grounding model output in your own documents and data through vector search, so answers cite something real instead of being produced from general training data.
Vector search infrastructure
Embedding pipelines and vector stores on Milvus, Chroma or MongoDB Atlas, including the chunking and re-indexing strategy that determines retrieval quality.
Evaluation and guardrails
Test sets, regression checks and output constraints, so a prompt change can be shown not to have broken something that previously worked.
Classical machine learning
Forecasting, classification, anomaly detection and recommendation where a trained model on your data outperforms a general one — and costs far less to run.
Cost and latency management
Caching, model routing and prompt design aimed at the two things that decide whether an AI feature survives contact with production traffic.
From first conversation to running software
- 01
Find the honest use case
Identifying where a model genuinely outperforms existing logic. Some candidate problems are better solved with a query and a rule, and we would rather say so.
- 02
Build an evaluation set first
Deciding what a good answer looks like, and how it will be measured, before building the thing that produces it.
- 03
Prototype against real data
Working with your actual documents and records, because retrieval quality depends entirely on the messiness of the source material.
- 04
Productionise
Cost controls, latency budgets, failure handling and monitoring — the work that separates a demo from a feature people rely on.
Bring us the problem, not the model
Discuss an AI projectPlatforms we've built
Projects from our portfolio. Each has its own write-up.
Trading
OptionTeller
An options analytics platform querying 70+ million daily trades, built on Next.js and Rust.
Financial Research
The Cytadel
A subscription market intelligence platform built on Supabase and Stripe.
What we build it with
Selected for what this kind of system needs, not a default toolkit.
Models
OpenAI
Claude
Hugging Face
Vertex AI
Retrieval
Milvus
Chroma
MongoDB Atlas
LangChain
Infrastructure
Python
AWS
Docker
Kubernetes
Where this work tends to land
Each of these sectors shapes the work differently — the industry page covers the specifics.
AI & Machine Learning: common questions
01
How do you keep a language model from producing confident nonsense?
Mostly by not asking it to recall facts. Retrieval-augmented generation grounds answers in your own documents, so the model is summarising retrieved text rather than producing information from training data. Beyond that: constraining output format, citing sources so a person can verify a claim, and designing the interface to make it clear the output should be checked where the stakes justify it.
02
Do you fine-tune models or use existing ones?
Usually existing models with good retrieval, because it is faster, cheaper and easier to change. Fine-tuning earns its place when you need a consistent output format or a specialised tone, and it does not fix factual grounding — that is what retrieval is for.
03
What happens to our data?
That depends on the deployment you choose, and it is a decision worth making deliberately. Commercial API providers publish their own data-handling and retention terms; self-hosted open models keep everything inside your own infrastructure. We will lay out the options against your requirements, but the contractual terms are between you and the provider.
04
How do you know whether the AI feature is any good?
By building an evaluation set before building the feature — a collection of realistic inputs with agreed expectations for the output. Without one, changes are assessed by whoever tried it most recently, and quality drifts invisibly.
05
Is machine learning always the right answer?
No, and this is worth saying plainly. Plenty of problems presented as AI problems are better solved with a well-written query, an explicit rule, or fixing the data quality underneath. A model adds cost, latency and unpredictability, so it should be earning something in return.
Bring us the problem, not the model
Describe what you want the system to do and what data you have. We'll tell you whether AI is the right tool and what evaluating it would involve.
- Reply within one business day
- NDA on request