Shipping AI products that enterprise buyers will let near their data, on a budget that has to last.

Classification of what you ship, the technical documentation, risk management, logging and human oversight the Act asks of providers, and the evidence your enterprise customers will ask for as deployers. Built into the product rather than written up afterwards.

Cost measured per customer, per feature and per model call, and an architecture that costs almost nothing when idle. Usage-priced services, reserved capacity where the load is predictable, and the routing that sends each request to the cheapest model that meets the bar.

A new product from a blank page, or AI added beside a system that already has customers, data and a codebase only the original team can change. The same cadence either way: a working demonstration every fortnight, and a stabilisation phase first when the system is already live.

Traces on every model call, an immutable audit log of what was decided and with whose data, cost and latency per tenant, and alerts routed to an engineer rather than a mailbox. The answers an enterprise buyer wants before they sign, produced by the system rather than by hand.

An evaluation set built from your real cases before the first release, then accuracy, regression, prompt injection and data exfiltration tests run on every release after it, so the evidence exists when a buyer asks for it.

For when a general model is too expensive per call, too slow, or cannot be allowed to see your data. Fine-tuned open-weight models served privately in the EU on Hexploits Cloud LLM, with no training on inputs and no logs, and an evaluation set that proves the fine-tuned model beats the general one before it goes live.

Classification, documentation, logging and human oversight for the systems the Act names as high-risk, delivered by the team behind swarmd.ai.

Right-sizing, reserved capacity and architecture changes that reduce the bill without reducing resilience.

Multi-step automations that call your systems under policy, with rate limits, approvals and an immutable log.

Metrics, logs, traces and alerting routed to the people who can act on them.

Prompt injection, data exfiltration and model access controls for the AI systems you are adopting.

Private deployment of open-weight models, EU-hosted, so your data never trains someone else’s product.

The controls, documentation and oversight that let you show how your AI systems are tested, monitored and supervised, mapped to ISO 42001.

AI agents can triage support tickets, write your documentation, and answer questions from your own records - but only if you feed them your data. Here is what that trade actually costs, and how AI sovereignty lets you get the value without giving up control.

Customers now buy inside AI conversations. The Agentic Commerce Protocol from Stripe and OpenAI lets an AI agent run your checkout while you stay merchant of record. What it costs to adopt, who is already selling through it, and what a retailer should do now.

When an AI agent spends money on your behalf, how do you prove you authorised it? Google’s Agent Payments Protocol gives every agent transaction a signed record of intent. What it means for businesses that sell, buy or underwrite, and what to do now.

x402 lets an AI agent pay for a web resource inside a single request, settled in seconds for a fraction of a penny. What it does to the economics of anything priced per request, its limitations stated plainly, and why for most UK businesses it is a watch item rather than a project.