Every project below is a real engagement delivered in our first few months of trading. Company names are withheld for confidentiality. The problems, solutions, and outcomes are genuine.
Established scientific instrument manufacturer seeking to enter an adjacent high-value market. No systematic approach to prospect identification or competitive positioning. Market research was ad hoc.
Outcome: Six months of market research compressed into 6 weeks. Competitive displacement strategy built from evidence, not assumptions.
Fast-growing SME selling globally through a hybrid direct/distributor model. Sales teams lacked consistent competitive positioning across regions.
Outcome: Global sales teams gain instant access to competitive intelligence previously locked in key people’s heads, shifting from price discounts to value conversations, in the right language, for every market.
Researchers waste hours selecting the right preparation protocols. Expertise scattered across notes and scientists’ memories. Competitors offer no intelligence layer.
Outcome: AI engine architected for direct product embedding, combines structured domain knowledge with semantic AI to recommend optimal protocols. Codifies institutional expertise. Differentiates product, strengthens OEM partnerships, and generates market intelligence.
Support was manual, reactive, and fragmented. Cases exceeding 4 months. No tracking of free support consumption. Engineers are answering emails instead of designing.
Outcome: A five-stage AI orchestration pipeline reduced triage time from 30+ minutes to under 2 minutes. Engineering hours reclaimed for R&D. Built-in commercial policy enforcement turns every out-of-contract request into a revenue opportunity.
Founder personally wrote every quotation: 10+ hours/week, capping growth. No subcontractor automation. No online presence.
Outcome: Founder moves from quote-writer to quote-approver. Subcontractor tendering is automated. Sustainability and social impact are tracked on every job. The tool becomes a licensable product.
25+ operating companies with no consistent framework for evaluating AI tools, classifying risk, or governing usage across a distributed MD-led structure, and growing pressure from the board to act.
Outcome: Board-adopted AI governance framework covering tool classification, approved and banned tool lists, M365 Copilot and GitHub Copilot rules, EU AI Act compliance, and third-party protocols. Now the group standard across all subsidiaries, with ongoing quarterly maintenance.
