§ 4.3 · /solutions/ai-agents

AI agents that handle the judgement calls — and learn from the ones humans still take.

Production AI agents wired into your ERP, banking, mailbox, and operational systems. They reason about exceptions, draft communications, propose decisions, and escalate to humans when the confidence isn't there. Cloud, local, or hybrid — same architecture.

Where rules end and judgement starts.

Most automation projects stall at the same place — a process that's 80% rule-based and 20% judgement. The deterministic 80% is easy. The remaining 20% is where SaaS workflow tools, low-code platforms, and traditional RPA give up. That's where mid-market operators end up either hiring more people, or quietly accepting that the work doesn't scale. AI agents are built for that 20%.

Patterns we deploy repeatedly.

Exception-handling agents

Mismatched invoices, unmatched payments, ambiguous orders. The agent reasons about the case using your historical resolutions, ERP context, and supplier or customer communications, then either resolves it or surfaces the case to a human with evidence and a recommended action.

Drafting & communication agents

Agents draft supplier clarification emails, customer order confirmations, dispatch updates, and internal handoffs. Humans review and send — the agent removes the writing burden, keeps tone consistent, and learns from edits.

Confidence-gated autonomy

Every agent decision carries a confidence score. Above the threshold, the agent acts. Below, it routes to a workbench with the reasoning, the evidence, and the recommended action. Thresholds tighten as the agent's track record grows.

Resolution memory

When a human resolves an exception, the agent learns the pattern. The next time a similar case appears, the threshold is lower or the rule is internalised. ROI compounds quarter on quarter rather than plateauing.

Sovereign / local deployment

Healthcare, finance, defence, and any operator whose customers or regulator say "no cloud AI" — we run the same agents against local Ollama on Apple Silicon or on-prem GPUs. Same architecture, different inference endpoint.

Full audit trail

Every input, every reasoning step, every output is logged. Required for regulated operators (HSE, NHS, financial services, aviation), useful for everyone else.

Cloud, local, or hybrid — same architecture.

Cloud-first

Claude (Opus, Sonnet, Haiku), OpenAI, Anthropic Agent SDK. Fastest to deploy, lowest operational overhead, the right answer when your data and your regulator are happy with cloud inference.

Talk to our agentic team

Sovereign / local AI

Local Ollama with Qwen, Llama 3.3, or Mistral models, running on Apple Silicon Macs or on-prem GPU servers. Same agents, same workbenches, no data leaves your network.

See how we scope sovereign engagements

Ready for an AI agents conversation?