Our Services

AI Integration & Automation

LLM APIs, RAG architectures, and intelligent workflow automation wired into your existing systems — production-grade AI that works in the real world.

The Challenge

Everyone’s talking about AI, but most implementations stall at the proof-of-concept stage. Your team built a demo that impressed the board, but getting it into production — reliable, secure, cost-effective, integrated with existing systems — is a different problem entirely.

You need engineers who’ve shipped AI in production, not data scientists who’ve only trained models in notebooks.

What We Do

We integrate AI capabilities into your existing systems — LLM-powered features, RAG architectures for document intelligence, workflow automation, and ML model deployment.

We focus on production-grade implementations that are reliable, cost-effective, and maintainable. Every AI feature we build has monitoring, fallback handling, and cost controls built in from the start.

LLM Integration

OpenAI, Claude, and open-source model integration with prompt engineering, output parsing, and cost optimisation.

RAG Architectures

Retrieval-Augmented Generation systems for document Q&A, knowledge bases, and context-aware AI features.

Workflow Automation

Intelligent automation for document processing, data extraction, classification, and routing workflows.

ML Model Deployment

Taking trained models from notebooks to production with proper serving, monitoring, and versioning infrastructure.

AI-Powered Features

Smart search, content generation, recommendation engines, and natural language interfaces wired into your product.

Chatbots & Assistants

Customer-facing and internal AI assistants with guardrails, knowledge bases, and escalation to human support.

Technologies

Tech Stack

OpenAI API Anthropic Claude LangChain LlamaIndex Pinecone Weaviate ChromaDB Python FastAPI Hugging Face TensorFlow PyTorch AWS SageMaker Azure AI
Who This Is For

Companies with a working AI proof-of-concept that needs production hardening. Teams that want to add LLM-powered features to existing products. Businesses with repetitive manual workflows ripe for intelligent automation. Organisations sitting on document repositories that could be searchable and actionable.

FAQ

Frequently Asked Questions

Both, depending on the use case. For most business applications, fine-tuned API calls to frontier models are more cost-effective and maintainable than custom models. We build custom models when your use case genuinely requires it.
RAG architectures ground responses in your actual data. We implement output validation, confidence scoring, human-in-the-loop for critical decisions, and fallback handling. Every production AI feature has guardrails.
A focused integration — like adding AI-powered search or document Q&A — typically runs 6-10 weeks of development plus API costs. We design for cost efficiency with caching, batching, and model selection.
Yes. Most of our AI work involves adding capabilities to existing applications — not building standalone AI products. We work with your existing APIs, databases, and user interfaces.
We use enterprise API tiers that don't train on your data, implement data anonymisation where needed, and can deploy on-premise models for sensitive use cases. Your data stays yours.
We set up monitoring for model performance, cost tracking, and output quality. When models degrade or better options become available, we handle the updates.

Ready to Get Started?

Start with a 30-minute conversation — no commitment, no pressure.

Start a Conversation