Industrial data that
earns its keep.

InRecipe.AI turns messy equipment, parts, and item-master records into structured, verifiable data — and into the business outcomes that clean data finally unlocks. An industrial-services customer's contract-pricing ML model gained 50% accuracy. A mining OEM enriches a parts master that powers four downstream systems. A marine manufacturer's procurement and customs flows run from one trusted source.

Industrial companies sit on equipment, parts, and item-master databases they can't fully trust. The same Atlas Copco compressor appears four ways. Parts catalogs have the same SKU under five names. Item masters arrive at production planning with blanks where there should be cost, lead time, and HS code. And every analytics or pricing or compliance initiative downstream assumes clean inputs that don't exist.

Every record takes the same auditable path. What runs is declared up front in a recipe you sign off on. Each skill reads real sources and emits a value with its evidence. A validator checks every claim before it reaches your database. Nothing enters on the model's word alone.

Clean master data isn't the goal - it's the unlock. The same enriched record moves margin, speeds procurement, and de-risks compliance: the initiatives you already want to run but can't, because the inputs aren't there. Here is what it frees up.

InRecipe.AI doesn't just clean data. It unlocks the value that bad data was blocking.

The proof isn't a benchmark - it's the work itself. InRecipe is live and in pilot across industrial air, mining, and marine today, on real registers: three pack shapes - devices, parts, and item-masters - one method. Customers are named anonymously here; every figure is real.

Every customer engagement produces curator work that compounds back into the pack. Every pack release benefits every customer in the vertical. Speed of pack authorship is the durable advantage — and new verticals ship by copying the machinery and swapping the content. The Industrial Air pack is live. The Mining Spare Parts and Marine Equipment packs proved the same machinery extends from devices to parts to item-masters.

The wedge is industrial data. The platform is industrial data, made trustworthy.

Four domain packs, a shelf of reusable skills, and a growing base of curated manufacturer knowledge. These are the assets every new engagement builds on, and they compound with each one.

Skills are shared across packs; only the content differs. That's why a new vertical ships in days, and why the curated catalog compounds into a durable, defensible asset.

Every value InRecipe.AI touches has an owner. We sort them into three tiers by who that owner is - and that ownership, not a policy you have to trust us on, decides what's shared and what stays yours. The rule is simple: if a manufacturer or a regulator already publishes it, it can be shared; if it's about your operations, it never leaves your tenant.

The real number isn't what InRecipe.AI charges. It's what untrusted data already costs you - quietly, on every quote, every shipment, every stalled initiative. A pilot makes that cost visible before you commit to anything.

Enrichment fixes the backlog you already have. That treats the symptom. The cause lives upstream, in how items get created, who owns them, and how they are governed. Data Design has delivered that governance side for years, across energy, industry, and platforms. InRecipe is the engine inside those engagements; the advisory credibility is already established.

Tools without governance treat symptoms. Governance without tools leaves the backlog standing. The full offering is both.

InRecipe.AI is a Data Design product, built by a team that has spent the last decade in industrial AI, supply chain, and enterprise SaaS.

Where your data goes, which models touch it, and how the architecture maps to where EU AI regulation is heading. The short version: your data stays in your environment, the model never trains on it, and every value is traceable.

Send us the security questionnaire - the audit trail was built for exactly that conversation.

A pilot is low-risk by design. You bring one business case and a sample of your data; we cover the AI cost and do the build. From there, two tracks run in parallel: InRecipe enriches the data and keeps it clean, while our advisory team fixes the upstream processes that let it go bad - so you are genuinely ready to autopilot, not just temporarily tidy.

We bear the cost of the first week. You bring the data and judge the output. That's the whole risk.