Solutions, not experiments
An AI solution is the whole system: the interface people use, the data pipeline feeding it, the retrieval layer, the evaluation harness, the monitoring, and the operational process around it. Buying a model API is not a solution — it is one component of one layer.
We take ownership of the outcome. That means we are accountable for whether the sales team actually uses the copilot, not merely for whether it was delivered on schedule. Adoption is a design problem as much as an engineering one, and we treat it that way from the first workshop.
Solution categories we build
Across 50+ AI products, a few patterns recur often enough that we have hardened templates for them. Starting from a proven pattern rather than a blank page removes several weeks of architecture risk.
- Internal knowledge assistants over scattered document stores
- Customer-facing AI products with usage metering and billing
- Decision support systems that summarise and rank options for a human
- Intelligent search across product catalogues, contracts and archives
- Domain copilots embedded in the tools your team already uses
- Document intelligence pipelines for regulated, high-volume workflows
Built on your data, kept under your control
Every solution is grounded in your own content, which is what makes it useful and what makes data governance non-negotiable. We deploy inside your cloud account where required, use enterprise zero-retention agreements with model providers, and build fully air-gapped on-premise systems for healthcare, finance and government clients.
Access control is enforced at the retrieval layer, not bolted on at the interface. A user asking a question can only ever receive content they were already permitted to see.
What you receive
- A deployed, production AI solution with real users on it
- Data pipeline and retrieval layer built on your content
- Role-based access control enforced at the retrieval layer
- Evaluation suite, monitoring and cost dashboards
- Adoption playbook and team training
Technologies we use
AI Solutions for Business — frequently asked questions
What is the difference between an AI solution and AI development?
AI development is the engineering work of building the system. An AI solution is the complete outcome — the system, the data foundation, the interface, the rollout and the operational process that makes it stick. If you already know precisely what to build, you need development. If you know the business problem but not the shape of the answer, you need a solution engagement.
Can you deploy inside our own cloud environment?
Yes. We deploy into your AWS, Azure or GCP account as standard, so data never leaves infrastructure you control. For clients with stricter requirements we build fully air-gapped on-premise deployments running open-weight models on your own GPUs.
How do you handle sensitive or regulated data?
Access control is enforced at the retrieval layer so users can only surface content they are already entitled to. We use enterprise zero-retention agreements with model providers, support PII redaction before inference, and maintain full audit logging. We have delivered under HIPAA, GDPR and SOC 2 constraints.
What if our data is messy?
It usually is, and that is a normal starting point rather than a blocker. The discovery phase includes a data readiness assessment that tells you honestly what is usable now, what needs cleaning, and what is not worth the effort. Occasionally the recommendation is to fix the data foundation before building anything on top of it.
How do you measure whether the solution worked?
Success metrics are agreed before the build starts and instrumented into the product — adoption rate, task completion time, deflection rate, accuracy against the golden dataset, and cost per transaction. You get a dashboard, not a quarterly slide.
Talk through your ai solutions for business project
A free 30-minute call with an engineer — not a salesperson. You leave with a scope, a cost range and an honest view of the risks.
Book a free strategy call
