Services · AI integration
AI that works in production. Not just in demos.
- 2–3 weeks
- to a working RAG prototype on your own data
- 8–12 weeks
- to a production-ready system with evals and guardrails
- £200–1,500 / mo
- typical inference cost for a 10k-conversation support bot
- Any model
- Claude, OpenAI, Bedrock or open-source — no vendor lock-in
How an engagement runs
Strategy, build, deploy. The same engineers, start to finish.
- 1
AI strategy & consulting
Find the highest-impact opportunities, and be honest about the ones that do not need AI at all.
We evaluate your systems, data readiness and team, then recommend models, platforms and architecture for your use case and budget. You leave with a practical roadmap: milestones, cost estimates and measurable success criteria.
Included
Use-case identificationData readiness assessmentModel selectionROI analysisAI roadmap - 2
Custom AI development
RAG assistants that answer from your data. Agents that run workflows end to end. Well beyond API wrappers.
Hands-on with OpenAI, Anthropic, AWS Bedrock and open-source models in production. We build the surrounding infrastructure too: vector stores, embedding pipelines, prompt management, and the guardrails that keep outputs reliable and safe.
What we build
RAG chatbotsAI agentsLLM integrationWorkflow automationDocument intelligenceNLP - 3
Integration & deployment
Into your existing systems, APIs and workflows — without disrupting what already works.
Deployed on AWS (Bedrock, Lambda, ECS) with infrastructure as code, staging that mirrors production, and monitoring of quality, latency and cost per request from day one. Post-launch we refine prompts, retrieval and model choice as usage scales.
Included
Cloud deploymentMonitoring & observabilityOngoing optimisation
Why Maven
Built for the real world, not the demo.
The model is the easy part. Everything around it — retrieval, evals, guardrails, cost control, observability — is what we have spent our careers building.
Production-ready
Error handling, fallbacks and monitoring from the start, so it works when it matters most.
Data privacy first
Sensitive data stays inside your infrastructure, compliant with UK and EU data protection.
Measurable ROI
Every project ships with success metrics tracked against business outcomes.
No vendor lock-in
Architectures that swap between OpenAI, Anthropic and open-source without a rebuild.
Full-stack capability
Frontend, backend, APIs, databases and cloud — the whole application around the model.
Honest guidance
We will tell you when AI is not the right answer. We solve problems, not sell technology.
How is this different from just calling the OpenAI or Anthropic API?
The API is the easy part. Production AI needs prompt management, evaluation harnesses, fallbacks, cost controls, guardrails, retrieval pipelines, and observability — none of which the model vendors ship for you. We build the production wrapper around the model so it stays reliable and cost-controlled at scale.
How much does an AI feature actually cost to run in production?
It depends heavily on traffic and model choice. A typical RAG-powered customer-support chatbot serving 10,000 monthly conversations runs £200-£1,500/month in inference costs, plus £50-£300 for vector storage. We benchmark options upfront so you know the unit economics before committing.
Should we use OpenAI, Anthropic, or open-source models?
It depends on the task. We typically default to Anthropic Claude or OpenAI for production reliability, switching to open-source (Llama, Mistral) when data residency or per-token economics demand it. We benchmark all three on your specific use case before recommending.
Can you build an AI agent that integrates with our existing tools?
Yes. We build agents that integrate with Salesforce, HubSpot, Notion, Slack, internal APIs, and databases via tool-use patterns. The agent decides which tools to call based on the user request, and we add guardrails to keep it from doing anything destructive without approval.
How do you prevent hallucinations in a customer-facing chatbot?
Retrieval-Augmented Generation (RAG) grounds answers in your real documentation, an evaluation harness catches regressions before deploy, and an output validation layer blocks responses that fail confidence thresholds. We also instrument every conversation so you can audit answers and continuously improve.
How long until we see a working prototype?
We typically deliver a working RAG-chatbot prototype in 2-3 weeks and a production-ready system in 8-12 weeks. For simple workflow-automation agents we have shipped MVPs in under 7 days.
A working prototype on your data in 2–3 weeks.
Bring the use case and the data. We will tell you honestly whether AI earns its place — and if it does, show you it working.