Custom AI Development. Pinecone
Pinecone custom ai development for ambitious businesses
We use Pinecone to deliver bespoke ai products and features that ship. Bespoke AI products built for one purpose: moving a business number you can name. RAG systems, recommendation engines, predictive analytics, NLP pipelines, AI-powered SaaS features. Principal-Engineer led, phased pricing, production from day one. Our headline case: a cost-reduction system at LloydsDirect that saves the business around £265,000 every month, shipped in eight weeks.
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. The LloydsDirect system processes more than a million prescriptions a month with no human babysitting. That's the standard we build to.
Phased build, fixed price per phase
Phase 0 is a written business case: the lever the AI will pull, the data it needs, the cost and the realistic timeline. Phase 1 is the production build, typically 3 to 6 weeks. You can exit cleanly after either phase. No long discovery decks, no abandoned proofs of concept. If you don't know what to build yet, start with an AI consulting engagement instead and we'll find the lever inside your operation first.
The stack that delivers
Python for ML pipelines, TypeScript for application layers, and the best tool for each job: LangChain and LlamaIndex for RAG, Pinecone and Qdrant for vector search, OpenAI and Anthropic APIs for LLM capabilities, open-source models when privacy or cost demands it. PostgreSQL with pgvector for structured plus semantic search. Deployed on your cloud with proper CI/CD. No experimental frameworks, no vendor lock-in.
Why Pinecone
Pinecone is a key part of our AI toolkit, chosen for reliability, performance, and production readiness rather than hype. We've used Pinecone extensively across AI projects and understand where it excels, where it falls short, and when it's the right choice for your specific use case. Every technology decision we make is grounded in what delivers the best results for our clients.
Related work
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
What if we don't know exactly what to build yet?
Start with an AI consulting engagement at /operational-ai. We embed in your operation for a week, find the lever AI can actually pull, and produce a written business case before any build. If the numbers don't add up, we tell you. If they do, we move into the build phase with a fixed price.
How much does custom AI development cost?
Engagements are phased with a fixed price per phase. Phase 0 (the written business case) typically runs 1 to 2 weeks. Phase 1 (the production build) typically runs 3 to 6 weeks. Full AI products with multiple components run 3 to 6 months. Fixed price per phase in SGD. You can exit cleanly after any phase.
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.
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 Phase 0 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. The LloydsDirect system handles more than a million prescriptions a month.