Custom AI Development
Custom AI development, bespoke AI products from prototype to production
Bespoke AI products built for your specific business needs: RAG systems, recommendation engines, predictive analytics, NLP pipelines, and AI-powered SaaS features. From prototype to production, we build AI that ships and scales.
Production AI, not Jupyter notebooks
There's a canyon between a working prototype in a notebook and a reliable production system. Most AI projects die in that canyon. We bridge it with proper engineering: containerised deployments, monitoring and alerting, automated retraining pipelines, graceful error handling, and the kind of infrastructure that lets AI run reliably at scale. We've built document processing systems that handle thousands of operations daily without human intervention. That's the standard we build to.
The stack that delivers
We build with Python for ML pipelines, TypeScript for application layers, and the best tools for each job: LangChain and LlamaIndex for RAG, Pinecone and Qdrant for vector search, OpenAI and Anthropic APIs for LLM capabilities, and open-source models when privacy or cost demands it. PostgreSQL with pgvector for structured + semantic search. Deployed on your cloud of choice with proper CI/CD. No experimental frameworks, no vendor lock-in. Just proven technology that works in production.
From RAG to recommendation engines
We build the full spectrum of AI products. RAG systems that make internal knowledge searchable and accurate. Recommendation engines that drive revenue by surfacing the right products to the right users. Predictive models that forecast demand, flag churn risk, and score leads. NLP pipelines that classify, extract, and summarise at scale. AI-powered features embedded into existing SaaS products. Whatever the use case, we take it from working prototype to reliable production system.
London & Singapore
With studios in both cities, we deliver work that resonates locally and scales globally. Whether you're a London startup expanding into Asia or a Singapore business building a global brand, we understand both markets and bridge the gap between them.
What's included
Everything you need, nothing you don't
RAG Systems
Retrieval-augmented generation systems that ground AI in your data: internal knowledge bases, document search, and intelligent Q&A.
Recommendation Engines
AI-powered recommendations for products, content, or actions, personalised to each user and optimised for your business metrics.
Predictive Analytics
Machine learning models for demand forecasting, churn prediction, lead scoring, and anomaly detection using your historical data.
NLP Pipelines
Text classification, sentiment analysis, entity extraction, summarisation, and language understanding for your specific domain.
AI-Powered Features
Embedding AI capabilities into your existing product: smart search, content generation, automated tagging, and intelligent workflows.
ML Model Deployment
Taking models from development to production with proper serving infrastructure, monitoring, versioning, and automated retraining.
Our process
How we work
Problem definition
Defining the specific problem, success metrics, data requirements, and technical constraints before writing any code.
Data assessment
Evaluating your data quality, quantity, and accessibility. Identifying gaps and building data pipelines where needed.
Rapid prototyping
Building a working prototype in 2–4 weeks to validate the approach, test with real data, and demonstrate feasibility.
Production engineering
Building the full system with proper infrastructure: APIs, monitoring, testing, deployment pipelines, and documentation.
Launch & scale
Production deployment, performance baseline, and ongoing support as usage grows and requirements evolve.
Frequently asked
Questions we get asked
How much does custom AI development cost?
AI products range from £15,000 for a focused feature to £80,000+ for complex, multi-model systems. We scope after a discovery phase where we define the problem, assess your data, and estimate the engineering effort.
What tech stack do you use for AI projects?
Python for ML and data pipelines, TypeScript for applications, PostgreSQL with pgvector for hybrid search, and LLM APIs from OpenAI and Anthropic. We use open-source models when privacy or cost requires it. Deployed on AWS, GCP, or Vercel depending on requirements.
How long does it take to build an AI product?
A prototype takes 2–4 weeks. A production-ready AI feature takes 8–16 weeks. Full AI products with multiple components take 3–6 months. We ship incrementally so you see value throughout.
What data do we need?
It depends entirely on what we're building. Some projects work with data you already have: documents, support tickets, transaction records. Others require new data collection. We assess this in the discovery phase and are honest about what's feasible.
Can AI products scale as our business grows?
Yes. We architect for scale from day one: horizontal scaling, caching layers, async processing, and infrastructure that handles 10x growth without re-engineering. Your AI grows with your business.