WAVECT vs CRAFTWORKS

Wavect or craftworks. An industrial machine-learning specialist, or a product team that ships software with AI inside.

craftworks does factory-floor machine learning: predictive quality, predictive maintenance, visual inspection, and the navio MLOps platform, with Audi, VERBUND and ÖBB on the published reference list. We build complete products where AI is one pillar of several. The two offers look similar on a services page and are bought for completely different jobs.

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“The model was excellent. What we lacked was the product around it: the onboarding, the billing, the reason anyone would log in.”

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How they actually differ

Six dimensions where craftworks and Wavect actually diverge.

WAVECT DIMENSION ALTERNATIVE

Complete products with AI inside: app, backend, billing, product call.

CORE STRENGTH

Industrial machine learning: predictive quality, maintenance, visual inspection.

Founders, scale-ups, enterprise pilots.

TYPICAL CLIENT

Large industrials and utilities: Audi, VERBUND, ÖBB per their published references.

Bespoke builds, you own the stack and the IP.

PRODUCT VS SERVICES

Services plus navio, their own MLOps platform.

User-facing products: web, mobile, AI, on-chain.

WHERE THE WORK LIVES

Production lines, plants and grids: models wired into industrial operations.

Weekly outcome fee or fixed-price Werkvertrag. No timesheets.

PRICING MODEL

Project engagements. Pricing not published.

We push back on scope and the should-we-build-it question, founder to founder.

SCOPE OWNERSHIP

Specialist delivery against an industrial data mandate.

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The real difference, in practice

craftworks has real machine-learning depth, and it shows where it counts: a decade of industrial AI since 2014, 40-plus employees, EU research projects, and a reference list of large industrials like Audi, VERBUND and voestalpine, all per their published figures. They also ship a product of their own, navio, an MLOps platform for deploying and monitoring models. If your problem is a production line that needs predictive maintenance or visual inspection, this is a specialist built for exactly that.

The shape of their work follows the clients: data-science mandates for manufacturers and utilities, where the deliverable is a performing model wired into industrial operations. That is a genuinely hard discipline, and it is not ours.

Our job starts where the model stops being the product. We build the complete thing around it: the user-facing app, the backend, the billing, the onboarding, and the product decisions about what ships at all. AI is one pillar of our builds, alongside web, mobile and on-chain, and we are happy to stand on solid ML built by a specialist.

If the deliverable is a model in a factory, go to craftworks. If the deliverable is a product people log into, see how we build AI products.

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When each is the better call

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When Wavect is the better call

  • The model is a component and the product is the deliverable: users, onboarding, billing, roadmap.
  • You need product judgement and a fractional CTO alongside the build, not a data-science mandate.
  • Your AI need is LLMs, RAG, or AI features inside an app, not factory-floor computer vision.
  • You want one senior team across web, mobile, AI and chain instead of stitching specialists together.
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When craftworks is the better call

  • Your problem is predictive maintenance, predictive quality, or visual inspection on real production lines. That is their published core.
  • You need an MLOps platform to deploy and monitor models, which is exactly what navio is for.
  • You are a large industrial and want a specialist with referenced work at Audi and VERBUND scale.
  • The hard part of your project is the model itself, not the product around it.

Factory-floor ML, craftworks. The product around the model, us. On a serious industrial product you may legitimately need both, and we are happy to build on a specialist's models.

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Relevant Wavect work

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FAQs

We build AI products: LLM integrations, RAG pipelines, AI features inside web and mobile apps. We do not run industrial data-science mandates like vision-based defect detection on a production line. That is specialist territory, and craftworks is one of the specialists.
Yes, and the split is natural: a specialist builds and operates the model, we build the product, the interface and the business logic around it. We have no problem recommending that setup when the ML is genuinely hard.
Neither publishes a number that makes the comparison honest, and the units differ: their work is sized by industrial data mandates, ours by weekly outcomes from €400 to €20,000 per week or a fixed-price Werkvertrag. Scope one project both ways and compare bids.
If you need to deploy and monitor your own models in production, yes, an MLOps platform from a team that lives in that world is a strong argument. If you will never train a model yourself, it is not a factor in your decision.
Source: craftworks.aiLast reviewed:
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