Service

AI

AI & Machine Learning Solutions

From a single AI feature to a full RAG system or autonomous agent — production-grade, not demo-ware.

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AI is the hardest field to ship in production right now because the gap between an exciting demo and a system you can trust at scale is enormous. We've spent the last two years closing that gap — building retrieval pipelines that actually cite sources, agents that fail gracefully, evals that catch regressions, and cost monitoring so a viral tweet doesn't bankrupt you.

What we ship

Everything that fits the brief

The default toolbox. We'll scope it to exactly what your project needs after the first call.

LLM-powered features inside an existing product (chat, summarisation, classification, drafting)

RAG systems that index your internal docs, PDFs, wikis and tickets

AI agents that take actions across multiple tools and APIs

Fine-tuning, prompt-engineering and model selection for your domain

Multimodal apps spanning text, image, voice and document inputs

Eval suites, observability, cost guards and human-in-the-loop flows

Tech stack

Boring, battle-tested, fast

What we reach for by default. Happy to plug into whatever your team is already running.

OpenAIAnthropicGeminiLangChainLangGraphPineconeWeaviatepgvectorPythonFastAPIModalReplicate

How we work

Four steps from brief to launch

No mystery, no surprise invoices. You see the work as it happens.

  1. 01

    Problem-fit conversation

    AI isn't always the answer. We spend the first call making sure the problem actually needs AI and that the success criteria are measurable.

  2. 02

    Spike + eval framework

    We build a small, throwaway version of the core capability and a measurement harness — so we can prove the approach works before scaling it.

  3. 03

    Production build

    Resilient pipelines, retries, fallbacks, structured outputs, cost monitoring and the boring infra glue that turns a demo into a product.

  4. 04

    Iteration loop

    Models and prompts evolve weekly. We set you up with evals and dashboards so improvements are measured, not guessed.

What you walk away with

The bits that actually matter

Production AI features with measurable accuracy and a cost ceiling you control

Reductions of 40–60% in support tickets, manual data entry, or analyst time

Internal teams who understand the system and can extend it themselves

Frequently asked

The questions everyone asks

Should I build with OpenAI / Anthropic, or open-source models?+
For most products: start with hosted models (OpenAI, Anthropic, Gemini). The latency, quality and ops cost are dramatically better. Open-source models make sense when data residency, fine-tuning or per-token economics dominate — we'll help you decide.
What about hallucinations?+
Mitigated, not eliminated. The right architecture (retrieval, structured outputs, citations, evals, human review for high-stakes flows) brings the error rate to a level your product can tolerate. We design for that explicitly.
Do you do fine-tuning?+
Yes, when it's the right tool. Usually it's not — better prompts, RAG or model selection solve 80% of cases. When fine-tuning genuinely helps, we have shipped it on OpenAI, Anthropic and open-source stacks.
How do I keep AI costs predictable?+
We instrument every call from day one, set per-user and per-endpoint budgets, route cheap requests to cheap models, and cache aggressively. You get a monthly cost forecast that doesn't surprise you.

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AWS, GCP, Vercel, CI/CD pipelines, observability and cost optimisation — boring infra done right.

Let's build

Ready to start your ai & machine learning solutions project?

Send us the brief — even a rough one — and you'll have a fixed scope, quote and timeline back within 24 hours.

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