01 AI opportunity mapping and roadmap
Recurring work, slow decisions and costly handoffs mapped with the people who do the job. Opportunities ranked by business value, data readiness and implementation effort, with a recommendation on what to build first and what needs groundwork.
02 AI agents and workflow automation
Agents and automations designed, built and deployed for sourcing, supplier follow-up, planning and reporting. Connected to approved information and systems, with defined responsibilities, approval points and exception handling. Built in your own Microsoft 365, with Copilot Studio and Power Automate.
In use at a clientFor a multi-site company, our spend analysis agent prepares the quarterly picture across all sites. The review that took two weeks now takes two days.
In use at a clientOur supplier follow-up agent asks suppliers to confirm each order and its delivery date, flags the ones that have not answered, and follows up. The buyer handles the exceptions.
SFSupplier follow-up agent01 · Being scopedSee how we build an agent → 03 Decision intelligence and simulation
Forecasting, cost modelling, optimisation and simulation applied to decisions about demand, inventory, suppliers and capacity. Assumptions made explicit and options tested before resources are committed. In procurement, these models run inside Alterna, the platform we built for bill-of-materials pricing and alternatives.
Realised result$1M saved in validation batches and a second construction phase deferred, after our cell-therapy digital twin put capacity 30% (modelled) above the previous estimate. Read the case →
04 AI operating model and governance
The rules for how AI opportunities are selected, funded, approved and owned across the company. Responsibilities, decision rights, data access and review routines set with the managers who will run them. For regulated workflows, validation requirements agreed with Quality before the build.
05 AI workshops and team adoption
Hands-on Microsoft Copilot workshops for procurement and sales & marketing teams, built around the participants' own tasks. People learn to prepare inputs, assess outputs, build agents and improve their own workflows. An optional four-week follow-up measures whether the new way of working holds in daily work.
See the workshops → 06 AI performance and scaling
Existing AI initiatives reviewed against the work they were meant to improve. Quality, adoption, running cost and business contribution measured, weak points corrected, and the solutions that work extended across teams or sites, each with a named owner and support after handover.