AI Engineering
LLM integrations built for reliability. RAG pipelines that retrieve the right context. AI-powered product features with cost visibility baked in from day one. An AI-native engineer who uses the same tools to build your product faster.
AI projects from £8,000 · AI audits from £2,500 · Fixed-price scoping available
30-50%
Faster delivery using AI-native tooling
5
AI product prototypes across law, code, content & automation
£600
Per day — AI-native delivery from day one
pgvector
RAG pipelines with hybrid BM25+semantic retrieval
Sound familiar?
🤖
Every LLM demo looks impressive. Then you ship to production and face hallucinations on edge cases, inconsistent output formats that break your parsing logic, latency that makes UX unusable, and costs that weren't in the business case. Building reliable AI products requires engineering rigour that most demos skip entirely.
📚
You built a RAG system. It retrieves documents. But the retrieved context is rarely the most relevant context, the chunks are too large or too small, the embeddings model doesn't match your domain, and the LLM ignores the context half the time anyway. RAG quality is an engineering problem with specific, solvable solutions — not a prompt problem.
💰
Your monthly Claude/OpenAI bill has tripled in 90 days. You don't know which features are responsible. You have no per-user or per-feature cost attribution. You're sending 15,000 tokens to the LLM for queries that need 2,000. AI infrastructure needs the same cost visibility discipline as cloud infrastructure.
🔒
"Does our data get used to train the model?" "Where is the data processed?" "Can we use a self-hosted model?" "What's your data retention policy?" These are questions enterprise sales requires answers to. AI architecture decisions made early determine whether you can answer them — or whether you have to rebuild.
How we help
01
Production LLM integration using Claude (Anthropic), GPT-4 (OpenAI), and Gemini — via the Vercel AI SDK for streaming, tool calling, and structured output. Prompt templates engineered for consistency, structured output schemas, function calling for agentic workflows, and the evaluation harnesses that tell you when your prompts regress.
02
Retrieval-Augmented Generation systems that actually retrieve the right context. pgvector or Pinecone for vector storage, late chunking and hierarchical indexing strategies, hybrid BM25+semantic retrieval, reranking with Cohere, and the evaluation pipeline that measures retrieval quality before you ship. ContractLens (our legal RAG prototype) demonstrates the full stack.
03
We build AI features into existing products — not standalone demos. ShipReview (automated code review), BriefPilot (content briefing), DataNarrative (chart-to-text generation), and FlowForge (workflow automation) all demonstrate AI features integrated into product workflows. We use AI to accelerate our own delivery 30-50%, then apply the same leverage to yours.
04
Model routing, prompt caching, response caching, token budgeting, and the observability stack that gives you cost attribution at the feature level. We've built AI infrastructure that handles 50,000 requests/day with full cost visibility and circuit breakers for rate limits. AI costs are engineering costs — they should be engineered, not hoped away.
Proof of work
ContractLens
Contract risk analyser — RAG pipeline over legal docs, clause extraction, risk scoring, and comparison engine
Claude · pgvector · RAG
ShipReview
AI code review tool — GitHub PR integration, diff-aware analysis, architectural feedback, and suggestion generation
Claude · GitHub API · Diff Analysis
BriefPilot
Content briefing assistant — briefing generation from keywords, SEO research synthesis, and competitive gap analysis
Claude · Perplexity · Streaming
DataNarrative
Chart-to-text generator — uploads chart images, generates natural language summaries with key insight extraction
Claude Vision · GPT-4o · Next.js
FlowForge
Workflow automation builder — natural language to workflow definition, conditional logic, integrations via webhooks
Claude · n8n-style · Vercel AI SDK
Why AI-native matters
AI-native development means Claude Code for implementation, AI for code review, AI for test generation, and AI for documentation. The result is 30-50% faster delivery on every project — not just AI projects. When you hire Vanguard, you get the productivity multiplier built into the day rate.
Most agencies are experimenting with AI tools. We've already rebuilt our workflow around them. The difference shows in delivery speed, in code quality, and in the kind of architectural thinking that only happens when implementation details aren't the bottleneck.
Pricing
AI audits and architecture reviews from £2,500. Fixed-price scoping available. Know your investment before you sign.
Ready to start?
Tell us about your LLM integration, your RAG challenge, or the AI feature you need to build. We'll come back with a concrete technical plan within 24 hours.
Typical response within 24 hours · No sales pressure