Case studies · 12 engagements · 25 reported outcomes
AI and data engagements across financial services, healthcare, manufacturing, public sector and consumer businesses — the work, the outcome, and what moved. Client identities withheld; results as reported, unrounded. Find the evidence closest to the decision you are facing.
| Generative AI & agents | AI & machine learning | Strategy & governance | AI security & cyber risk | Data engineering & platforms | AI & data literacy | |
|---|---|---|---|---|---|---|
| Financial services | ||||||
| Healthcare & life sciences | ||||||
| Manufacturing & industrials | ||||||
| Professional services | ||||||
| Government & public sector | ||||||
| Retail & consumer |
Results
Figures as reported in each engagement's write-up; client identities withheld.
Showing all 12Clear filters
| Engagement | Industry | Practice | Headline result | Open |
|---|---|---|---|---|
| Contract compare-and-comply across MSAs, SOWs, POs and invoicesA global financial information and ratings provider | Financial services · Professional services | Generative AI & agents · Strategy & governance | 15–25%of documents nonstandard — the only ones that now need a reviewer | Open → |
| A decision layer inside its investment and risk platformA global asset manager | Financial services | AI & machine learning · Data engineering & platforms | 32%better investment–distribution alignment | Open → |
| An omni-channel virtual agent for technical supportA global electronics manufacturer | Manufacturing & industrials | Generative AI & agents | 37%fewer Tier 1 live-agent contacts | Open → |
| An AI and analytics layer on the existing planning systemA large food manufacturer and distributor | Manufacturing & industrials | AI & machine learning · Data engineering & platforms | 18%fewer stockouts at key partners | Open → |
| A label-aware AI assistant for field and patient questionsA life-sciences data and communications provider | Healthcare & life sciences | Generative AI & agents · Strategy & governance | Audience-specific rules enforced on every response · auditable | Open → |
| Rewards optimisation: the right offer, depth and timingA national quick-service restaurant chain | Retail & consumer | AI & machine learning | 14%more loyalty sign-ups | Open → |
| Loyalty personalisation across app, email and SMSA regional fast-casual dessert brand | Retail & consumer | AI & machine learning | 120k+customers moved to the new app | Open → |
| A people-data platform over the core people systemA multi-site care provider | Healthcare & life sciences | Data engineering & platforms | 70%of staff found it easier to use | Open → |
| A forecasting engine for one function, one numberA global manufacturer | Manufacturing & industrials | AI & machine learning | +20%forecast accuracy | Open → |
| Tamper-evident verification of pandemic health eventsA US state government | Government & public sector | AI security & cyber risk · Data engineering & platforms | 98%validated first attempt | Open → |
| An AI assistant inside the live 311 call flowA major US city's 311 service | Government & public sector | Generative AI & agents | One trusted, ZIP-localised source during a misinformation period | Open → |
| An AI and data literacy programme across the workforceA regional health organisation | Healthcare & life sciences | AI & data literacy | 80%of staff reported improved skills | Open → |
Start from the decision
Twelve engagements is enough to recognise the shape of yours. Describe what you are trying to decide, what is already in place and who has to sign it off, and we will tell you which of these it resembles — and which starter service it points to — before you commit to anything.
Not sure which dimension is the weak one? The Assessment Sprint scores six, against your own systems, in two weeks.
Twelve engagements. Find the evidence closest to the decision you are facing.
Bring us your decision
