Clear AI strategy.
Clean implementation. No hype.
We help you cut through the noise — auditing where AI actually creates value in your business, selecting the right tools, and doing the engineering to integrate OpenAI, Anthropic, Gemini, or custom models cleanly into your existing stack.
Everything you need, nothing you don't.
Each engagement is scoped to what you actually need. No upselling, no bloat.
AI Opportunity Audits
A structured review of your operations and product to identify the highest-ROI AI opportunities with clear effort estimates.
LLM & API Integration
Clean, production-ready integration of OpenAI, Anthropic, Gemini, or open-source models into your existing codebase.
Vendor Selection & Cost Modeling
Objective comparison of AI providers on accuracy, cost, latency, and compliance — with a cost model for your usage volume.
Prompt Engineering & Optimisation
Structured prompts, few-shot examples, and evaluation frameworks that maximise output quality and reduce token costs.
AI Readiness Assessment
Gap analysis of your data, infrastructure, and team capabilities — with a prioritised roadmap to become AI-ready.
Team Training & Enablement
Hands-on workshops for your engineering, product, and operations teams on building and managing AI systems.
Simple steps. Clear outcomes.
We keep the process lean so you spend less time in meetings and more time seeing results.
Audit & Opportunity Mapping
We spend time understanding your business, processes, and data — then map specific, high-value AI use cases.
Strategy & Vendor Selection
We deliver a prioritised roadmap, recommended tech stack, and cost model — no vendor kickbacks, just what fits best.
Implement & Enable
We do the integration work and train your team to own and extend it without depending on us forever.
How it plays out in practice.
A real-world scenario from start to finish — the kind of outcome we build toward.
Cutting LLM costs 60% while adding a new AI feature
A SaaS company had shipped an AI feature on a premium model and was alarmed by the bill — and unsure whether they were even using the right provider or prompts.
We ran an AI cost-and-architecture audit, benchmarked providers on quality and price for their exact task, moved the bulk of traffic to a cheaper model with the premium one reserved for hard cases, compressed prompts, added caching, and built an evaluation framework so quality could be measured, not guessed.
LLM spend dropped roughly 60% with no measurable drop in output quality, and the client got a repeatable eval harness to safely test future prompt and model changes.
Seen it work across industries.
These are the kinds of problems we've solved — and the results they produced.
AI Readiness Roadmap
Helped a legal services firm identify 6 AI use cases, prioritise by ROI, and deliver an 18-month implementation roadmap.
LLM Integration into Product
Integrated Claude into an existing B2B SaaS platform — prompt engineering, fallback logic, cost monitoring, and eval framework.
AI Cost Optimisation
Reduced a client's OpenAI spend by 60% through model selection, prompt compression, and caching strategy — same output quality.
Questions about ai consulting & api integration.
The things prospects ask us most. Still unsure? Book a free call and ask us directly.
We are not sure where AI helps our business. Can you advise?
That is exactly what the AI opportunity audit is for. We review your operations and product, then map the highest-ROI use cases with honest effort estimates — and we will tell you plainly where AI is not the right tool.
Which AI providers do you integrate — OpenAI, Anthropic, or Gemini?
All of them, plus open-source models. We select based on accuracy, cost, latency, and compliance for your specific use case rather than defaulting to one vendor, and we build fallback logic so you are not dependent on a single provider.
How do you keep LLM API costs under control?
Through model selection, prompt compression, caching, and usage monitoring. We routinely cut client LLM spend significantly while holding output quality — and we give you a cost model before you commit to a provider.
Can you integrate AI into our existing application?
Yes. Most of our integration work plugs cleanly into an existing codebase — adding LLM features, evaluation frameworks, and monitoring without rebuilding what already works.
Will our team be able to maintain the AI features after you leave?
Yes. We do the integration and then train your engineering, product, and ops teams to own and extend it, so you are not dependent on us forever.