Most organisations have run an AI pilot. Far fewer have one in production, used every day, with the security, cost and accuracy understood. The gap is rarely the model. It is data, integration, permissions, evaluation and the operating model around it.
We help you choose the use cases worth doing, build them into the systems you already run, and operate them like any other production service.
Use cases measured on adoption and outcome, not on a pilot that impressed a steering group.
AI connected into the platforms we already support.
Your data is not used to train public models, and access follows your permissions.
Our own enterprise AI platform, for when a product fits better than a build.
Every AI service we offer is shaped around these six. They are where pilots stall, and none of them is solved by choosing a better model.
Ideas ranked by value, feasibility, data availability and risk, with the cost of running each one in production estimated up front.
Most portfolios shrink by half once the running cost is written down.
Fewer use cases, chosen for return.
The documents, records and systems each use case needs, checked for quality, ownership, access and freshness.
An assistant is only as accurate as the content it is grounded on.
Answers grounded in data you trust.
AI wired into the ERP, CRM and service tools people already use, rather than a separate window nobody opens.
Adoption follows the workflow, not the demo.
AI where the work happens.
Identity, data access, logging and guardrails designed so the AI can see and do only what the user could.
Permission mistakes are the most expensive AI failures.
No new route around your access model.
Test sets, accuracy measures and regression checks built for each use case, run before every change.
Without evaluation, every prompt change is a guess.
Quality you can measure, release by release.
Monitoring, usage, cost per transaction, feedback loops and model updates run as a managed service.
AI cost scales with use, so it is reported like any other utility.
A service with an owner and a budget line.
Each page describes one part of the work. Most engagements combine two or three of them.
Independent advice on where AI pays back in your business, and where it does not.
Use case portfolio, data and platform readiness, governance and a costed roadmap.
Use cases taken from pilot to production with security, evaluation and support.
Assistants, search and document AI grounded in your own content.
Agents that take actions across systems, with permissions, limits and human approval.
Document processing, triage and workflow automation with people kept on the exceptions.
AI connected into Oracle, Microsoft, Salesforce and your own applications.
How an AI engagement runs
Where each AI service fits in the life of a use case.
| Service | Typical output | When | Model |
|---|---|---|---|
| AI Consulting | Advice, assessments, opinions on proposals | Any time | Advisory |
| Strategy and Readiness | Use case portfolio, roadmap, governance | Before investment | Project |
| Generative AI | Assistants, search, document AI | Build | Project |
| Agentic AI | Agents that act across systems | Build | Project |
| AI Automation | Document and workflow automation | Build and run | Project or managed |
| AI Integration | AI inside Oracle, Microsoft, Salesforce | Build | Project |
| Enterprise AI Implementation | Production rollout and operations | Build and run | Project or managed |
An AI portfolio of thirty pilots usually produces none in production. The effort goes into demonstrations, and the work that makes a use case reliable, secure and affordable never gets funded.
We would rather take three use cases all the way: integrated, evaluated, permissioned, monitored and owned. Then reuse that foundation for the next three.
Talk to an AI expertScope is set after discovery.
With two or three use cases where the data exists, the process owner is willing, and the benefit can be measured. Discovery produces that shortlist and the reasoning behind it.
No. We use enterprise services and configurations where your prompts and data are not used for model training, and we design access so the AI sees only what the user can see.
The one that fits the use case, cost and data residency: Azure OpenAI, Oracle, Salesforce and other major providers, open models where they suit, and our own PumkinAI platform.
Each use case has an evaluation set and agreed criteria for accuracy, adoption and cost. They are run before go-live and before every change.
They are now part of AI Solutions. Data science and machine learning work continues as part of the AI engagements where a use case needs it.
Or the pilot that never made it to production. Discovery produces a ranked list and a plan you keep either way.