InoGen

Inventory Management and Procurement Optimisation

Identifying and Building a Product for UK Healthcare Practices
Healthcare
Machine Learning
AI Strategy
Product Build
Procurement

Market opportunity validated and quantitatively sized

MVP in active development for 2026 launch

Co-development partnership with a practice owner

Structured pilot designed with pre-agreed success metrics

We identified a gap in the UK healthcare market where thousands of practices share the same procurement problem and no adequate tooling exists. Working with a practice owner as a co-development partner, we validated the opportunity, defined an MVP, and are building an AI-assisted inventory management product designed for the market, not just one organisation.

The Problem

Healthcare practices in the UK face a persistent operational problem: managing inventory and procurement. Every practice needs to keep consumables, equipment, and materials in stock. Every practice orders from a mix of suppliers. And almost every practice does it badly.

Our Approach

We identified this as a product opportunity: a gap in the market where thousands of practices share the same problem and no adequate tooling exists. Rather than building a bespoke solution for one organisation, we set out to build a product designed for the market, working with a single practice owner as a co-development partner.

Before committing to a product direction, we mapped the full landscape of potential products that could serve healthcare practices, assessing each against three dimensions: value to practice owners, technical feasibility given typical data maturity, and adoption likelihood. Inventory and procurement optimisation scored highest across all three. We assessed the competitive landscape and confirmed the gap was real: generic tools require too much configuration, assume data maturity that practices do not have, and offer no domain-specific intelligence.

The practice owner brings domain expertise, daily operational context, and a live environment to test in. We bring the product thinking, data engineering, and AI capability. This partnership guards against the most common product failure mode: building something that works in a demo but not in a real practice. The pilot is structured as a controlled experiment with success metrics defined in advance, designed to produce evidence that becomes the foundation for selling to other practice owners. The product is currently in development (specific details are confidential).

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Results and Impact

MetricStatus
Market validationCompleted: opportunity sized, competitive landscape assessed, addressable market quantified
Product roadmapPrioritised and in execution, with MVP as the lead deliverable
Co-development partnershipEstablished with a practice owner who participates in design, testing, and feedback
MVPIn active development, with launch expected in 2026
Pilot approachDesigned with defined metrics and pre-agreed success thresholds
Go-to-market readinessPilot evidence base will support commercial conversations with other practice owners

The product has moved from an identified market gap to a funded build with a co-development partner, a defined MVP, and a structured path to market.

Key Takeaways

  • Robust market assessment determined whether this was a product worth building. Sizing the opportunity against value, feasibility, and adoption prevented committing development effort to a problem nobody would pay to fix.

  • Co-developing with a practice owner produces a better product than building in isolation. A partner who uses early features in their actual workflow and gives unfiltered feedback prevents the product team from optimising for elegance over utility.

  • The pilot is as much a sales tool as a validation exercise. Prospective customers will ask "does this work?" A pilot with pre-agreed metrics and a controlled design produces the credible, quantified answer that enables the go-to-market conversation.