Build turns the use case you agreed in Assess into a working, tested AI system. We engineer it through two production lines, our Code Factory and our Data Factory, so every change is planned, traceable and security-reviewed. Then an engineer approves it before it ships.
Our code and data factories make us fast, but speed only counts when you're heading the right way. Every build is led by experienced engineers who have shipped real systems for real businesses, and who know where AI helps, where it doesn't, and where it can do harm.
That's the part you can't download, and it runs through every stage below. Look for the green markers.
Knowing when a rule, a report or a plain database query beats a model, and saying so, even when it's a smaller job for us.
Years of building for dealer groups, contact centres and service businesses. We already know your systems, your customers and your regulators.
Our CTO is also our named Data Protection Officer, and every build runs under our ISO/IEC 27001-certified information security management system. Compliance is part of the design, not an audit at the end.
AI only pays off when people use it. We test with your staff, train them, and adjust the system around how they really work.
We don't start from a blank page. The decisions made in Assess are turned into requirements with acceptance criteria, and every test we write traces back to one of them.
NVIDIA describes AI as a five-layer stack: energy, chips, infrastructure, models and applications. Each layer depends on the one beneath it, and the economic value shows up at the top.
Small and medium businesses don't build power stations or chips. But every layer still shapes what your system costs, how fast it runs and where your data goes. We engineer the top three layers and design around the bottom two.
Select a layer to see what it means for your build.
Where economic value is created. Models put to work on real problems, from drug discovery to robotics.
This is your use case from Assess: agents, assistants and automations wired into the systems you already run, such as your CRM, dealer system or finance platform. Our Code Factory builds this layer.
Does it change how the work gets done, and can we prove it?
An engineer and your users decide when it's ready for pilot. Passing tests isn't enough on its own.
Systems that understand language, and also biology, chemistry, physics and the physical world. Language models are only one kind.
No single model is right for every task. We test frontier, open and small specialist models against your own data, then choose for each task on accuracy, cost, speed and where the data can go.
Which model is good enough, at the lowest cost and risk?
Engineers read the evaluation results and make the call, including saying no when no model is good enough yet.
Land, power delivery, cooling, networking and the systems that connect thousands of processors. NVIDIA calls these "AI factories".
For you, this layer decides where the system runs and where your data lives: a cloud API, UK hosting, your own tenancy or on-premises hardware. UK GDPR and data residency are settled here, and our Data Factory builds the data foundation underneath.
Where should this run, and where is the data allowed to go?
Our CTO, who is also our named Data Protection Officer, signs off where your data goes and how it's protected.
The processors that turn energy into computation at scale, built for parallel work, high-bandwidth memory and fast interconnects.
You won't buy chips, but they still set your speed and your bill. We size each workload so you aren't paying for frontier-scale compute on a simple classification task. Smaller models on modest hardware often do the job.
Are we paying for more compute than the task needs?
We explain the cost trade-offs in plain English, so you choose knowing what you're giving up.
The foundation. Power has to be generated in real time, and it limits how much intelligence can be produced.
Every token costs power and money. We design for efficiency with caching, batching, shorter prompts and smaller models where they're good enough. That keeps running costs predictable and the footprint down.
What does each answer cost to produce, and can it cost less?
You get a running-cost forecast you can budget against, and we go through it with you.
We build with our own agentic engineering platforms. Specialist AI agents do the repetitive work at speed, and our engineers set the direction, check the output and approve what ships. That's how a small, senior team delivers production-grade systems on an SME budget.
An AI-native software lifecycle. Specialist agents plan, build, test and review, with git as the backbone and an engineer in charge.
Agentic workflows built only for data. Everything is driven from the platform, with no one-off scripts or hand-written SQL in production.
Every job gets its own branch and commit history, so you can see exactly what changed, when and why.
Acceptance criteria are written before the code is. If a test can't point to a requirement, it doesn't count.
Lead agents are verified by a different model, which catches the blind spots a single model shares with itself.
Our security and compliance reviewer sees only the finished code, so it judges what was built rather than what was intended.
Code and data both land in staging first. Nothing reaches your live systems without a check.
Agents make us fast. Accountability stays with the people whose names are on the work.
We aren't tied to one model provider. Every model choice is tested against your own data and use case, then weighed against cost, speed and where your data is allowed to go.
Leading commercial models for complex reasoning, writing and agentic work, used where their quality pays for itself.
Open-weight models run on infrastructure we or you control, so sensitive data never leaves the boundary you set.
Compact language models and fine-tuned classifiers for high-volume, well-defined tasks, at a fraction of the cost per call. We benchmark them on our in-house NVIDIA hardware before we recommend them.
job/quote-drafting · branch feat/advisor-quote-draft analyst 12 requirements · 31 acceptance criteria lead plan created · resumed from journal builder api, prompt pack, dealer-system connector verifier plan + diff checked by second model ✓ security cold review · 2 findings · 2 fixed ✓ tests 31/31 criteria passing ✓ data audit: 0.4% duplicates removed · staged ✓ awaiting engineer approval → merge to staging
Agents do much of the volume work, but engineers set the direction, approve every plan and review every change before it merges. No code ships without a human sign-off.
Our agentic platform for data work. It connects your sources, maps schemas, audits quality and duplicates, applies your business rules and publishes to a staging database, where the data is checked before it goes live.
Requirements are written with acceptance criteria before the code, and every test traces back to one. Work is also checked by a second model, reviewed for security without context, and tested in real browsers where users will click.
We're not tied to one provider. We test frontier models such as Anthropic Claude and OpenAI, open models we can self-host, and small specialist models against your data, then choose for each task on accuracy, cost, speed and data residency.
NVIDIA's description of AI as five layers: energy, chips, infrastructure, models and applications. We engineer the top three layers for you and design around the bottom two to keep speed and running costs under control.
Once the pilot meets its success measures, we move it into production on infrastructure you control, with the monitoring and governance to match.
The five-layer model is NVIDIA's, as described by Jensen Huang in "AI is a 5-layer cake" on the NVIDIA blog. The interpretation for small and medium enterprises is 53d North's own. NVIDIA is a trademark of NVIDIA Corporation.