Examples of AI Work

Each example starts with a business problem, then shows what we’d do and what changes.

StrategyMid-size manufacturer

Before: Leadership was fielding AI pitches from every vendor, with no way to compare them and no one owning the decision.
What we'd do: Map the opportunities against their processes and data, rank them by value and effort, and set up an operating model: owners, decision rights, and measures.
After: Three projects underway with clear owners, two set to fix first, and the rest set aside with the reasons written down.

BuildField service company

Before: Every new job took a chain of calls, lookups, and follow-ups before a crew could go out.
What we'd build: Agents that take requests, prepare jobs from service history, schedule crews, and follow up with customers. The office handles the exceptions.
After: More jobs move through with the same office team, and nothing waits on a callback.

Evaluate and improveCustomer support team

Before: Their AI assistant answered most questions well and some badly, and no one could tell which.
What we'd do: Build a test set from real tickets, compare fixes on it side by side, and keep the one that measures best.
After: Fewer wrong answers reach customers, and the team knows how good the assistant really is.

AI leadershipGrowing services firm

Before: AI questions kept landing on the COO's desk, and the board wanted a plan.
What we'd do: A part-time AI lead sets priorities, reviews vendors before contracts are signed, and reports to the board.
After: Clear owners, fewer impulse purchases, and a plan the board can follow.

TrainingProfessional services firm

Before: Staff used AI tools unevenly, and partners worried about client data.
What we'd do: Role-based workshops on the firm's own documents, with clear rules for what goes into which tool.
After: Consistent everyday use, inside rules the partners agreed to.

Strategy, then BuildTraining company

Before: Revenue stopped at the edge of the instructors' calendars.
What we'd build: A practice tool that lets customers rehearse between sessions, with feedback grounded in the company's method.
After: A new product to sell that doesn't need an instructor in the room.

BuildSpecialist supplier

Before: Custom requests waited on one expert to pull specs, stock, and pricing together. Some never got a quote.
What we'd build: Agents that draft each quote from those sources and the company's pricing rules. The expert reviews and sends.
After: More requests get an answer, and the expert spends the day on the calls that need judgment.

Examples are illustrative.

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