What We Build
LLM integration for chat, summarisation, and content generation (OpenAI, Anthropic Claude, Gemini). RAG pipelines: document indexing, vector search (Pinecone, pgvector), retrieval chains. Agentic AI: multi-step autonomous agents with tool use, function calling, and custom orchestration. AI copilots built into your B2B SaaS dashboard. Document processing pipelines: OCR, extraction, classification, routing.
Compliance & Privacy
All AI implementations are built with compliance from the start: PII redaction before data reaches any LLM API. No training data retention (opt-out flags set on all API calls). GDPR-compliant data processing agreements with AI providers. For maximum data residency control: AWS Bedrock (EU region) or Azure OpenAI — your data never leaves your cloud.
RAG Architecture
Retrieval-Augmented Generation (RAG) is the architecture behind AI assistants that answer questions about your company's own data — without hallucinating. Brantum builds production-grade RAG systems: document ingestion pipelines, vector embeddings, semantic search, hybrid retrieval, and re-ranking. Used for: internal knowledge bases, customer support AI, product documentation assistants.
Agentic AI
Agentic AI goes beyond simple LLM chat — agents are autonomous systems that can take actions: search the web, query your database, call external APIs, write code, and complete multi-step workflows. Brantum builds agentic systems using LangChain, custom orchestration, and OpenAI's function calling — scoped and constrained for safe enterprise deployment.
Pricing
AI integration project (adding AI features to an existing product): £15,000–£80,000 depending on complexity. RAG pipeline project: £20,000–£60,000. Full AI product build: £80,000–£200,000. All AI projects begin with a 1-week AI Discovery Sprint (£5,000) to assess feasibility, select the right architecture, and estimate cost before any build commitment.
Relevant Case Studies
Frequently Asked Questions
Which AI models do you work with?
We work with OpenAI (GPT-4o, o1, o3), Anthropic Claude (Sonnet, Opus), Google Gemini (1.5 Pro, Ultra), and open-source models via Ollama, AWS Bedrock, and Azure AI. Model selection depends on your specific use case — cost, context window, compliance requirements, and output quality. We do not lock you into one provider.
How do you handle AI hallucinations?
Hallucination prevention is an architecture problem, not just a prompt engineering problem. For factual Q&A use cases, we use RAG (Retrieval-Augmented Generation) to ground LLM responses in verified documents. We implement output validation, source citation, and confidence scoring. For critical systems (FinTech, HealthTech), we add a human review layer for AI-generated outputs.
Is AI GDPR compliant?
AI integration can be GDPR compliant with the right architecture. Key requirements: data processing agreements with AI providers (OpenAI, Anthropic, and Google all offer these), PII redaction before data reaches LLM APIs, no training data retention (configure API calls with opt-out flags), data residency control (use AWS Bedrock or Azure OpenAI for EU data). Brantum designs all AI integrations with GDPR compliance by default.
How long does an AI integration project take?
Adding AI features to an existing product: 4–12 weeks. Building an AI-native product from scratch: 12–24 weeks. A focused AI Discovery Sprint (1 week, £5,000) assesses your existing codebase, defines the AI architecture, and produces a fixed-price estimate before any build work begins.
Can you add AI to our existing SaaS product?
Yes — adding AI to an existing product is one of our most common projects. We audit your current architecture, design the AI integration layer (typically: API proxy + context injection + output handling), and build the AI features as a separate service that integrates cleanly with your existing codebase.
