BB Store Management — AI Features
Semantic product search with pgvector, demand forecasting from sales history, and an AI copilot for reorder decisions. Shipped with evals, cost caps, and a Claude fallback.
LLMs & ML features
AI Integration
LLMs where they earn their keep. Claude, OpenAI, and embedding-based features integrated into your product with fallbacks, guardrails, and a real eval loop. No throwing a chatbot at everything.
Engagement
AI Integration
- Investment
- From £15k
- Timeline
- 4–12 weeks
- Pricing model
- Fixed-price or T&M
- Next availability
- Next slot: 3 weeks
What you get
AI that earns its keep
Four things we deliver on every AI integration — not marketing claims, engineering practices.
Prompt library + evals
Versioned prompts with a test suite. We measure output quality against real examples, not vibes.
Model + fallback wiring
Primary model with an automatic fallback. When OpenAI rate-limits, Claude picks up. No broken features.
Rate & cost controls
Per-user quotas, cached completions, and budget alerts. You see token spend before it surprises you.
User-facing UX for AI outputs
Streaming responses, clear error states, and honest uncertainty signals. AI that users actually trust.
Who this is for
Use cases
- Products adding their first real AI feature without the chatbot-on-everything trap
- Teams who need LLM outputs measured and tested before shipping
- Applications that require model fallbacks and cost guardrails
- Products using semantic search or retrieval-augmented generation
- Founders who want to understand token cost and quality before committing
- Systems that need human-in-the-loop controls for sensitive outputs
How we work
Our process
Four phases. Prototype before commit. Evals before launch. No vibes-based shipping.
Discovery
What's the real AI use case? Is it worth the token cost? We stress-test the idea before committing.
Prototype
Build a thin slice, wire up a real eval set, and prove the outputs hold up on your actual data.
Build
Production integration with monitoring, fallback models, cost controls, and a proper prompt registry.
Launch
Ship with guardrails: rate limits, content filters, human-in-the-loop where it matters, and live evals.
Proof of work
We practise what we build
Client work is under NDA. Here's what we ship in the open.
Before you ask
Frequently asked questions
Which LLM should I use — OpenAI, Claude, or something else?
Depends on the task. Claude is stronger at long-context reasoning and structured instructions; OpenAI has the mature ecosystem and best function-calling; Gemini is cheap at scale; open-source models (Llama, Qwen) make sense if data sovereignty matters. We run your real prompts against 2–3 models during prototyping and pick based on measured quality, latency, and cost — not marketing.
How do you handle hallucinations?
Three layers: retrieval-augmented generation so the model cites your actual data, structured output schemas that reject malformed responses, and a human-review step for anything that touches money, health, or legal outcomes. We also run an eval suite on every prompt change so regressions get caught before deploy.
What does "eval" actually mean?
An eval is a test suite for AI outputs. We collect 50–200 real examples with expected answers (or quality rubrics), then score every model change against them automatically. It's the difference between "seems fine when I tried it" and "95% accuracy on the customer support dataset". Without evals, you're flying blind.
How do you control costs at scale?
Per-user token quotas, aggressive caching of repeated completions, prompt compression, routing cheap queries to smaller models, and budget alerts that page us before you get a £10k surprise bill. We also track cost-per-request in Langfuse so you know exactly which feature is expensive and why.
Is it safe to send our data to cloud LLMs?
Depends on your data and regulator. OpenAI and Anthropic offer zero-retention enterprise agreements and don't train on API data. For UK GDPR or NHS-adjacent workloads we use Azure OpenAI with EU data residency, or self-host open-source models on your own infrastructure. We advise on the trade-offs before a single token leaves your network.
Ready to add real AI?
Tell us what you're building — we'll reply within 24 hours on working days with a prototype scope, a fixed quote, and a realistic start date.