AI Consulting
Turn AI Spend Into AI Returns.

Your data does not leave your control. We architect agentic systems using either on-premise LLM deployments, private Azure OpenAI instances with zero data retention, or self-hosted open-source models (Llama, Mistral) where cloud is not an option. For any third-party API calls, we configure zero-retention agreements and document the data flow in your governance pack.

The UK has taken a principles-based, pro-innovation approach to AI regulation — overseen by the ICO, FCA, CMA, and sector regulators rather than a single AI Act. If your business sells into the EU, or processes EU citizens' data, you also fall under the EU AI Act, which classifies systems into risk tiers (unacceptable, high, limited, minimal). High-risk systems — those involved in recruitment, credit scoring, critical infrastructure, or biometrics — require conformity assessments and human oversight mechanisms. Our Governance & Risk Audit maps your systems against both UK and EU frameworks and produces the documentation your legal and compliance teams need.

Every agent we build includes a human-in-the-loop escalation path. Low-confidence outputs are flagged for human review before action. All decisions are logged with the input, the model's reasoning, and the outcome — creating a full audit trail. We also set confidence thresholds and define what "wrong" looks like before go-live, so the system is tuned to your acceptable error rate, not a generic one.

Chat interfaces are point tools — they require a human to prompt them, interpret the output, and take action. Agentic automation is different: agents trigger on events, chain multiple steps together, call external APIs, update your systems of record, and operate without human intervention. If your team is spending time copy-pasting between ChatGPT and your CRM, that's a workflow we can automate end-to-end.

A consultant delivers a report and leaves. A Fractional CAO holds an ongoing leadership position — attending leadership meetings, owning the AI roadmap, managing vendor relationships, and accountable for outcomes. The engagement continues beyond the initial audit, and we measure ourselves against the same KPIs your board sees. Typically 2–3 days per month, scaled up or down based on active build phases.

We are model-agnostic. We commonly work with Claude (Anthropic), GPT-4o (OpenAI via Azure), Gemini Pro, and open-source models like Llama 3 and Mistral. Model selection is driven by your use case requirements: latency, context window, cost, data residency, and performance on your specific task type. We benchmark multiple models before recommending one, and we build your agents to be model-swappable so you're never locked in.