Deploy takes the system proven in Build and puts it into production on infrastructure you control, with the security, monitoring and governance your auditors expect. We roll it out in stages, so trust is earned step by step and people decide when it's ready for each next step.
Most AI projects that fail in production don't fail on the technology. They fail because they were switched on for everyone at once, before anyone trusted them.
We increase the system's autonomy one stage at a time. Each stage has exit criteria agreed in advance, and it only moves on when the evidence says it should.
The system runs alongside your team on real work, but nobody acts on its output. We compare its answers with what your people actually did.
The system drafts and a person approves every output. Every edit they make is captured to improve it.
We go live for one team, site or customer segment, at the level of autonomy the use case calls for.
Rolled out to everyone it was built for, with monitoring, support and a route into Scale.
Where the system runs decides your data residency, your running costs and how much control you keep. There's no single right answer, so we recommend based on your data, budget and risk appetite, and we design so you can move later without starting again.
Getting value quickly from the strongest models, where the data is low-risk or can be masked first.
Watch out forPer-use costs that grow with volume, and dependence on the provider's terms and regions.
Businesses already on a major cloud that want everything under their own security policies and contracts.
Watch out forCloud skills on your side, and making sure the regions you need offer the models you want.
Sensitive personal data, strict UK residency requirements and steady high volumes, where a fixed cost beats paying per use.
Watch out forOpen models need careful evaluation against your task, and capacity has to be planned ahead.
Data that must never leave your site, very low latency needs, or poor connectivity at the sites that use it.
Watch out forUpfront hardware cost, physical security, and keeping the hardware and models up to date.
Every deployment passes the same production-readiness gate. Automated checks prove what can be proven, and named people sign off the rest.
AI systems can be wrong with total confidence. So every system we deploy is designed to catch mistakes, contain them and hand over to a person when it should.
Quality, response times, cost and drift tracked from the first live request, with alerts that reach a person.
Personal data masked where it isn't needed, and unsafe or off-topic requests and answers filtered.
When confidence is low or the stakes are high, the system hands the case to a person instead of guessing.
Every request, answer, model version and human override is logged, so you can explain any decision later.
Models and prompts are versioned and pinned, so a provider's update can't quietly change how your system behaves.
A tested route back to the previous version, plus an off switch that your own people can use.
Larger customers will ask hard questions about how you use AI and where their data goes. We hand you the evidence to answer them, drawn from the same work we do to answer due-diligence questionnaires from enterprise clients of our own.
Completed with you, covering lawful basis, data minimisation, retention and individuals' rights, including automated decision-making.
People are told when they're dealing with AI, decisions can be reviewed by a person, and records are kept, in line with the EU AI Act and the ICO's guidance. AI regulation →
Hosting and architecture assessed against the NCSC's 14 Cloud Security Principles, and documented.
Access control, secrets, logging and incident response run under our ISO/IEC 27001-certified ISMS, and we hold Cyber Essentials. Our security approach →
go-live · advisor-quote-draft · v1.4.2 hosting your tenancy · UK South · model pinned shadow 480 real jobs compared · bar met ✓ assisted 3 weeks · edit rate within target ✓ security review closed · secrets vaulted ✓ dpia approved · records of processing updated ✓ ops alerts live · rollback tested ✓ people 14 advisors trained · owner named ✓ go-live approved: client MD + 53d North lead engineer release limited · 2 sites → full in 4 weeks
On a managed AI service, in your own cloud account, on UK private hosting, or on hardware at your site. We recommend one based on your data, budget and risk appetite, and design so you can move later.
In four stages: shadow running alongside your team, assisted with a person approving every output, a limited release, then full release. Each stage has exit criteria agreed in advance, and people decide when to move on.
Every system passes the same go-live gate across quality, security, data protection, operations and people. Named people sign off the data protection impact assessment, the remaining risk and the go-live decision itself.
People are told when they're dealing with an AI, AI-generated content is labelled, a person can review or override decisions, and inputs, outputs and overrides are logged.
Alerts go to a named engineer who knows your system. Every deployment has a tested rollback route and an off switch your own people can use.
Once it's proven in production, we roll the system out across teams and workflows, and use what we've learned to bring the next use case in faster and cheaper.