--- title: "Wavect vs craftworks" canonical: https://wavect.io/compare/wavect-vs-craftworks/ language: en description: "craftworks publishes factory-floor machine learning: predictive quality, predictive maintenance, visual inspection, anomaly detection and data engineering, plus …" image: "https://wavect.io/img/general/bak/open_graph_preview.jpg" --- WAVECT vs CRAFTWORKS # Wavect or craftworks. An industrial machine-learning specialist, or a product team that ships software with AI inside. craftworks publishes factory-floor machine learning: predictive quality, predictive maintenance, visual inspection, anomaly detection and data engineering, plus navio, which it describes as an MLOps platform. Audi, VERBUND, ÖBB and voestalpine are among the client references shown on its site. We build complete products where AI is one pillar of several. The two offers look similar on a services page and are usually bought for different jobs. Independent comparison published by Wavect. Not affiliated with or endorsed by craftworks. Illustrative buyer scenario, not a customer quotation The model was excellent. What we lacked was the product around it: the onboarding, the billing, the reason anyone would log in. TL;DR craftworks is an industrial machine-learning specialist: predictive maintenance, visual inspection, the navio MLOps platform, Audi and VERBUND on the reference list. We build complete products with AI inside. Factory-floor ML, that is them. The product around the model, that is us. // 01 ## Decision snapshot VerdictThe practical split: their solutions page leads with predictive quality, predictive maintenance and visual inspection on production lines, and also lists industrial software and generative AI. [2](#cmp-src-2) Ours is the product around the model: users, onboarding, billing, roadmap. On a serious industrial product you may legitimately need both, and we are happy to build on a specialist’s models. ### Wavect is best when - 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. ### The alternative is best when - Your problem is predictive maintenance, predictive quality, or visual inspection on real production lines. All three are named on their solutions page. [2](#cmp-src-2) - You need an MLOps platform to deploy and monitor models. That is how they describe navio. [4](#cmp-src-4) - You want a specialist with referenced work at large industrials. Audi, VERBUND, ÖBB and voestalpine are among the client references shown on their site. [3](#cmp-src-3) ### Questions to ask before you choose 1. Their solutions page lists industrial software and generative AI alongside the machine-learning work, so the scope question is a real one. [2](#cmp-src-2) Ask which of those the engagement actually covers, and who owns the product decisions about what gets built. 2. Ask who owns the trained models and the data if navio is used to deploy and monitor them, and whether you can run those models without the platform. On the navio page we reviewed we did not find statements about model ownership, data export, or running models outside navio. [4](#cmp-src-4) 3. Ask us the same two questions and compare the written answers. 4. None of this implies anything is withheld: product pages are marketing surfaces, not contracts, and the only reliable place for scope, ownership and exit is the signed agreement, ours included. ### Commercial risk to watch - The visible price is not the whole cost; the risk sits in where the scope boundary falls and what happens the first time it moves. - Ask what the vendor is paid to increase: hours, seats, retained advice, platform usage, or shipped outcomes. ### Evidence notes - Every statement about craftworks on this page is footnoted below with the page we read it on and the date we opened it. - This comparison states published scope and asserts nothing about the quality of their work. // 02 ## Project fit: not limited to small builds Project size and provider headcount are separate decisions. Wavect’s delivery unit is a named senior core, but that does not limit the work to MVPs or short startup projects. Larger projects are split into phases with separate budgets and acceptance criteria. We also build internal systems and process automations inside a client’s existing repositories, tooling and workflows. A vetted bench supports continuity and scope-specific capacity. A larger provider may still be the better choice when the main requirement is several parallel teams, an incumbent framework agreement or certifications Wavect does not currently hold. // 03 ## 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 | Their solutions page leads with predictive quality, predictive maintenance, visual inspection, anomaly detection and data engineering, and also lists industrial software and generative AI. [2](#cmp-src-2) | | Founders, scale-ups, enterprise pilots. | TYPICAL CLIENT | Large industrials and utilities. Audi, VERBUND, ÖBB and voestalpine are among the client references shown on their site. [3](#cmp-src-3) | | Bespoke builds, you own the stack and the IP. | PRODUCT VS SERVICES | Services plus navio, which they describe as their own MLOps platform. [4](#cmp-src-4) | | User-facing products: web, mobile, AI, on-chain. | WHERE THE WORK LIVES | Production lines, plants and grids: their solutions page describes models and software for industrial operations. [2](#cmp-src-2) | | Weekly outcome fee or fixed-price Werkvertrag. No timesheets. | PRICING MODEL | Project engagements. On the pages we reviewed we did not find published prices. [2](#cmp-src-2) | | We push back on scope and the should-we-build-it question, founder to founder. | SCOPE OWNERSHIP | Ask which parts of the product their statement of work covers, since the published scope spans models and industrial software. [2](#cmp-src-2) | // 04 ## The real difference, in practice craftworks has real machine-learning depth. Their company page states a founding year of 2014, 40+ people and two offices in Vienna. [1](#cmp-src-1) The Austrian company register lists craftworks GmbH in Vienna under FN 424082a with status active. [5](#cmp-src-5) Their solutions page publishes predictive quality, predictive maintenance, visual inspection, anomaly detection and data engineering. [2](#cmp-src-2) They also ship a product of their own, navio, which they describe as an MLOps platform for managing, deploying and monitoring machine-learning models, with edge deployment and MLflow integration. [4](#cmp-src-4) Audi, VERBUND, ÖBB, voestalpine, Wien Energie, Österreichische Post, B. Braun and Wienerberger are among the client references shown on their site. [3](#cmp-src-3) If your problem is a production line that needs predictive maintenance or visual inspection, this is a specialist built for exactly that. One correction to a lazy version of this comparison. Their published scope is not only models: the same solutions page also lists industrial software, described as custom software for industrial operations, and generative AI. [2](#cmp-src-2) So the honest split is not capability, it is scope on your specific engagement. The question to put to them, and to us, is narrow: does the statement of work cover the user-facing app, the backend, the billing and the onboarding, or does it stop at a performing model wired into operations, and who owns the product decisions about what gets built at all? Ask it in writing before you sign. 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](/services/artificial-intelligence/). // 05 ## When each is the better call // 01 ### 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. // 02 ### When craftworks is the better call - Your problem is predictive maintenance, predictive quality, or visual inspection on real production lines. All three are named on their solutions page. [2](#cmp-src-2) - You need an MLOps platform to deploy and monitor models. That is how they describe navio. [4](#cmp-src-4) - You are a large industrial and want a specialist with referenced work at that scale. Audi, VERBUND, ÖBB and voestalpine are among the client references shown on their site. [3](#cmp-src-3) - The hard part of your project is the model itself, not the product around it. Their published solutions are built around exactly that problem. [2](#cmp-src-2) 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. // 06 ## Relevant Wavect work - [SERVICE AI Development](/services/artificial-intelligence/) - [SERVICE MVP Development](/services/mvp-development/) - [CASE STUDY Twinsoft AI: prototype to enterprise-grade MVP, two weeks](/case-studies/twinsoft-ai/) - [CASE STUDY PromptID: 0 to production in 6 weeks, pilot and investor demo ready](/case-studies/promptid/) - [GUIDE How to choose a software development agency](/software-development-guide/how-to-choose-a-software-agency/) // 07 ## FAQs ### Does Wavect do machine learning at all? 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 publishes visual inspection and predictive quality as core solutions. [2](#cmp-src-2) ### Could Wavect and craftworks work on the same project? 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. Worth noting that their solutions page also lists industrial software, so the boundary is something to agree explicitly rather than assume. [2](#cmp-src-2) ### Which is cheaper? We cannot answer that honestly, because the units differ and only one side publishes a number. On the pages we reviewed we did not find published prices from them. [2](#cmp-src-2) Ours is a weekly outcome fee from EUR 400 per week, EUR 10,000 to EUR 30,000 per month for full-stack delivery, or a fixed-price Werkvertrag. Scope one project both ways and compare bids. ### Is navio a reason to pick craftworks? If you need to deploy and monitor your own models in production, it is a real argument: they describe navio as an MLOps platform covering model management, deployment, monitoring and edge deployment, with MLflow integration. [4](#cmp-src-4) Ask how models and data are exported and whether they run outside the platform, because on the navio page we reviewed we did not find that addressed. [4](#cmp-src-4) If you will never train a model yourself, it is not a factor in your decision. Source: [craftworks.ai](https://www.craftworks.ai) Last reviewed: 2026-08-04 by [Kevin Riedl](/team/kevin-riedl/) [wiki ↗](https://www.wikidata.org/wiki/Q139796365) ## Sources for the statements about craftworks Every numbered claim above links to the source we checked, with the date we checked it. Linking a source is not a partnership or an endorsement. 1. [Company page: founding year, headcount and number of offices](https://www.craftworks.ai/company/about-us/) craftworks. retrieved 2026-08-04 2. [Solutions page: the published scope, including predictive quality, predictive maintenance, visual inspection, anomaly detection, data engineering, industrial software and generative AI](https://www.craftworks.ai/solutions/) craftworks. retrieved 2026-08-04 3. [Company site: the named client references shown on the front page](https://www.craftworks.ai/) craftworks. retrieved 2026-08-04 4. [navio product page: MLOps platform for model management, deployment and monitoring, including edge deployment and MLflow integration](https://www.craftworks.ai/navio/) craftworks. retrieved 2026-08-04 5. [Austrian company register entry FN 424082a: legal name, registered seat, registration date and status](https://www.northdata.com/craftworks%20GmbH,%20Wien/424082a) North Data (Austrian Firmenbuch data). retrieved 2026-08-04 About this comparison This is an independent comparison published by Wavect. We are not affiliated with, endorsed by, or partnered with the companies named here, and all third-party company names, brands and trademarks are the property of their respective owners. Statements about other providers are taken from publicly available sources, primarily their own published pages, as of the review date shown on this page, and may have changed since; please verify them directly before deciding. Each individual comparison lists the sources for its statements about the other provider, with the date each source was checked. Any buyer scenarios shown are illustrative examples only, not quotations from or accounts by actual customers. This comparison was written to the best of our knowledge and with the intent to remain objective. If you believe anything here is inaccurate or unfair, please reach out and we will correct it. [office@wavect.io](mailto:office@wavect.io) ## Still weighing the options? Tell us what you are building. 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