Services - AI integration and development services
We build AI solutions that work in production — not just in demos. From RAG chatbots to autonomous agents, we handle the infrastructure, observability, and reliability engineering that makes AI trustworthy at scale.
Whether you want to build an intelligent chatbot that answers questions from your company data, automate complex workflows with AI agents, or embed predictive capabilities into your existing software, our team has the expertise to deliver.
AI Strategy & Consulting
Every successful AI implementation starts with a clear strategy. We work with your team to identify the highest-impact opportunities for AI within your business, whether that means automating repetitive workflows, enhancing customer experiences, or unlocking insights from your data.
We evaluate your existing systems, data readiness, and team capabilities to recommend the right approach. Not every problem needs AI, and we are honest about when simpler solutions are the better choice. When AI is the right fit, we help you select the right models, platforms, and architecture for your specific use case and budget.
The result is a practical AI roadmap with clear milestones, cost estimates, and measurable success criteria, so you can move forward with confidence rather than hype.
Included in this phase
- Use case identification
- Data readiness assessment
- LLM and model selection
- ROI analysis
- AI roadmapping
- Technology evaluation
Custom AI Development
We build AI-powered features and products that solve real business problems. From intelligent chatbots with RAG (Retrieval-Augmented Generation) that answer questions from your own data, to autonomous AI agents that handle complex workflows end-to-end, we develop solutions that go well beyond basic API wrappers.
Our team has hands-on experience integrating large language models from OpenAI, Anthropic, AWS Bedrock, and open-source alternatives into production applications. We build the surrounding infrastructure too: vector databases, embedding pipelines, prompt management systems, and guardrails that keep AI outputs reliable and safe.
Whether you need a standalone AI product or want to embed intelligence into your existing software, we handle the full development lifecycle from prototype to production.
What we build
- RAG chatbots
- AI agents
- LLM integration
- Workflow automation
- Predictive analytics
- Natural language processing
- Document intelligence
Integration & Deployment
An AI feature is only valuable if it works reliably in production. We integrate AI capabilities directly into your existing systems, APIs, and workflows so your team can start benefiting without disrupting what already works.
We deploy on AWS using services like Bedrock, SageMaker, Lambda, and ECS, architecting for scalability, cost efficiency, and low latency. Every deployment includes monitoring, logging, and alerting so you have full visibility into how your AI features are performing.
Post-launch, we help you iterate. AI solutions improve over time with better data, refined prompts, and user feedback. We provide ongoing support to optimise performance and reduce costs as your usage scales.
Included in this phase
- Cloud deployment. We deploy to AWS using infrastructure-as-code, with staging environments that mirror production for safe iteration.
- Monitoring & observability. Full visibility into model performance, response quality, latency, and cost per request from day one.
- Ongoing optimisation. We continuously refine prompts, retrieval pipelines, and model selection to improve accuracy and reduce running costs.
Why Maven - AI that works in the real world
We focus on building AI solutions that deliver measurable business value, not impressive demos that fall apart in production.
- Production-ready. We build for scale and reliability from the start. Our AI solutions include error handling, fallbacks, and monitoring so they work when it matters most.
- Data privacy first. Your data stays yours. We architect solutions that keep sensitive information within your infrastructure and comply with UK and EU data protection regulations.
- Measurable ROI. Every AI project we deliver includes clear success metrics. We track performance against business outcomes so you can see the real impact.
- No vendor lock-in. We design flexible architectures that work across LLM providers. Switch from OpenAI to Anthropic to open-source models without rebuilding your system.
- Full-stack capability. AI does not exist in isolation. We build the complete application around it: frontend, backend, APIs, databases, and cloud infrastructure.
- Honest guidance. We will tell you when AI is not the right solution. Our goal is to solve your problem, not to sell you technology you do not need.
FAQ - AI integration FAQs
The questions we hear most from teams about to add AI to production.
- 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.