Foundation
Understand products
Create accurate, trustworthy product understanding from descriptions, images, attributes and other signals.
Boutique lab for retail
Tinkerkraft builds AI systems that turn complex product data and catalogues into connected knowledge—powering better decisions and more relevant customer experiences.
Retail catalogues were built to store products, not to understand them. The information is fragmented, inconsistent and rarely captures all the meaning that customers and retail teams need.
We build the systems that turn this complexity into knowledge that is structured, connected and ready to use.
What we believe
Better AI starts with the right context.
Which attributes matter? When are two products meaningfully equivalent? What information can be trusted? These are domain questions—not just modelling problems.
One connected journey—from making sense of products to creating experiences that understand what people are looking for.
Foundation
Create accurate, trustworthy product understanding from descriptions, images, attributes and other signals.
Structure
Model the relationships between products, categories, catalogues and the concepts that give them meaning.
Experience
Apply deeper product understanding to improve how products are managed, discovered, compared and chosen.
Placing varied products into complex, evolving taxonomies while handling uncertainty and exceptions.
Recognising when incomplete and inconsistent listings refer to the same product across catalogues.
Measuring whether product information is complete, consistent, trustworthy and fit for purpose.
Designing taxonomies, ontologies and knowledge structures that capture how products actually relate.
Applying AI to nuanced product claims where language, evidence and context all matter.
Connecting product meaning with customer intent to make finding and choosing feel more natural.
Built for retail’s messy reality
We break challenging problems down to their fundamentals—what must be true, what evidence exists and how success should be measured—then build the simplest system that can work.
Question assumptions, make the fundamentals explicit and frame the real problem before choosing an approach.
Make the domain, relationships, language and sources of truth explicit.
Define what good means, test honestly and understand the consequences of different errors.
Develop systems that work with people, adapt to change and improve over time.
We begin with the domain—its language, constraints and sources of truth. The technology follows.
We interrogate the data to uncover patterns, challenge assumptions and ground design decisions in evidence.
We measure success by decisions improved and value created—not features shipped or models deployed.
We work in small, testable increments—putting useful capability into practice early and learning what to build next.
We make uncertainty and failure modes visible, measure quality honestly and bring in expert review when judgement is required.
LLMs, ontologies, knowledge graphs and agentic AI are building blocks. We use what the problem demands.
Join Tinkerkraft
We’re building a small, thoughtful team for people who enjoy hard problems, intellectual honesty and the responsibility to turn promising ideas into useful systems.
Send us a short introduction and something you’ve built or solved.
Introduce yourselfWe’d like to meet
Bring us a difficult catalogue, discovery or retail-knowledge problem.
ai@tinkerkraft.com