AI Services & Capabilities
We implement your systems, connect your data, and layer in AI agents that work alongside your team turning curiosity into real capability.
Most businesses know AI matters, few know exactly where to start. We audit your operations, map your workflows, and identify the highest-ROI opportunities for AI before you spend a single penny on implementation.
Your CRM, helpdesk, marketing platform, or internal tools don't need to be replaced, they need to be made smarter. We embed AI capabilities directly into the systems your team already uses, with minimal disruption.
When off-the-shelf AI products don't fit, we build from scratch. Custom LLM-powered applications, RAG pipelines, AI agents, and automation systems, engineered to your exact specification and trained on your data.
Repetitive manual tasks are the easiest wins for AI. We design and build intelligent automation workflows that run 24/7, combining AI reasoning with your existing tools to eliminate the work your team shouldn't be doing.
Not the clunky rule-based chatbots of 2018, we build genuinely intelligent conversational agents that understand context, use your knowledge base, take real actions, and hand off to humans at exactly the right moment.
Your data holds answers your team doesn't have time to find. We build AI systems that surface meaningful patterns, generate plain-English insights from complex datasets, and flag anomalies before they become problems.
What we do
Three interconnected practices, consulting, integration, and development, designed to take you from AI-curious to AI-operational, whatever your starting point.
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Real applications
AI works differently in every business. Here are the most impactful use cases we've built across industries, each one grounded in a real problem, not a tech demo.
Sales & CRM
An AI system that scores inbound leads in real time based on website behaviour, CRM history, and firmographic data, routing hot leads to sales instantly and nurturing cold ones automatically. Reps spend time on the deals that will close.
Customer Support
A conversational AI agent trained on a company's product documentation, FAQs, and support history, integrated directly with their helpdesk and CRM. Resolves 60–70% of tickets without human involvement, escalates the rest with full context.
Internal Operations
A RAG-powered internal assistant connected to a company's Notion, Confluence, Google Drive, and Slack, letting employees ask natural language questions and get accurate, sourced answers instantly. No more 20-minute searches for a policy document.
Marketing & Content
A multi-step AI pipeline that takes a single content brief and generates SEO-optimised blog posts, social captions, email sequences, and ad copy, all in the brand's voice, reviewed by a human, and published via CMS integration.
Finance & Reporting
An AI system that ingests weekly financial and operational reports, identifies anomalies, generates plain-English executive summaries, and flags items requiring attention, delivered to leadership automatically every Monday morning.
E-commerce & Retail
A custom recommendation system trained on purchase history, browsing behaviour, and product embeddings, surfacing genuinely relevant products at the right moment across email, web, and post-purchase flows. Not 'customers also bought.'
Technologies
We're model-agnostic and tool-agnostic, we pick the right technology for your specific problem, not the one we happen to know best.
LLMs
Frameworks
Vector DB
Automation
Cloud AI
Eval & Obs
Why work with us
AI consulting is full of people who talk about AI. We're one of the few teams that actually builds it, and has the track record to show it works.
We work with OpenAI, Claude, Gemini, and open-source models. We recommend what's right for your problem, not what we happen to know best or what's most fashionable right now.
Most AI consultants stop at the strategy deck. We go all the way to shipped, monitored, production AI systems, so you're not left with a roadmap and no one to execute it.
AI systems need guardrails. We build in hallucination mitigation, output validation, human-in-the-loop checkpoints, and data privacy controls from the start, not as an afterthought.
Most AI projects take too long because teams over-engineer the first iteration. We prototype fast, get something in front of real users quickly, and refine based on what actually happens.
How we work
We follow a structured approach that moves fast without cutting corners, from first conversation to live AI system.
We start by understanding your business deeply, your workflows, data landscape, existing tools, and where the friction lives. We identify which problems are genuinely solvable with AI and which ones aren't, so you don't waste budget on the wrong things.
Before writing production code, we build lightweight prototypes of the AI system, testing model selection, prompt architecture, and integration points. You see something working early, which means feedback is concrete and changes are cheap.
We build the production system, LLM integrations, RAG pipelines, automation workflows, or custom applications, running continuous evaluation loops to measure accuracy, latency, and reliability before anything goes near live users.
AI systems need a different kind of QA. We test for prompt injection, hallucination rates, edge case handling, and output consistency, plus data privacy and compliance review before any user-facing deployment.
We deploy to production with observability baked in, logging, cost tracking per query, performance dashboards, and automated alerts. AI systems degrade quietly without monitoring; we make sure you always know how yours is performing.
Post-launch is where AI systems really improve. We run ongoing optimisation, prompt refinement, model upgrades as new versions release, fine-tuning on accumulated data, and expanding to new use cases as confidence grows.
Questions
Honest answers to the questions we get asked most, before, during, and after AI projects.
Yes, in fact, non-tech businesses often see the biggest early wins because there's more low-hanging fruit. Customer support, internal operations, reporting, and sales follow-up are all areas where AI delivers value regardless of your industry. The technology is now accessible enough that you don't need a data science team to implement it.
ChatGPT is a general-purpose tool, it knows a lot about everything but nothing specific about your business, your customers, or your data. A custom AI solution is configured on your specific context, your product docs, CRM data, support history, and company knowledge, so it gives answers that are accurate for your business. It also integrates with your existing systems and acts on them.
Data privacy is built into every AI project from the start, not treated as a compliance checkbox at the end. We implement data minimisation, use enterprise-grade APIs with no training data opt-in, build access controls so AI only sees what it should, and document all data flows for GDPR compliance. For sensitive industries we can recommend on-premise or private cloud model deployment.
Hallucination is a real risk and we take it seriously. We mitigate it through RAG (grounding answers in your actual documents), output validation layers, confidence scoring, human-in-the-loop checkpoints for high-stakes decisions, and structured outputs for tasks that require factual precision. No AI system is 100% accurate, but well-engineered ones can be reliable enough for real business use.
It varies significantly depending on scope. A focused AI integration (e.g. adding an AI chatbot to an existing CRM) can start from a few thousand pounds. A custom RAG-based knowledge system or multi-agent automation platform runs into the tens of thousands. We always recommend starting with a scoped discovery engagement so you know exactly what you're getting before committing to a full build.
Model selection depends on the task. Claude tends to perform best for nuanced reasoning, long documents, and safety-critical outputs. GPT-4o is strong for structured data tasks and has the broadest ecosystem. Gemini is the right choice when deep Google Workspace or Vertex AI integration is needed. We run benchmarks on your specific use case before committing, model choice is an engineering decision, not a brand preference.
AI systems aren't set-and-forget. Models update, your data changes, and user behaviour shifts in ways that affect performance. We offer ongoing monitoring and optimisation retainers, tracking accuracy, latency, and cost per query, running prompt refinements, upgrading model versions as they improve, and expanding to new use cases when the initial system is stable.