Applied AI · Manufacturing companies 80–500
We design dedicated AI tools that support the processes of manufacturing companies.
Dedicated micro-applications designed for specific process bottlenecks. Built together with the department team, on the client's premises, on their processes and data.
What determines the success of an AI implementation in a manufacturing company is neither the technology nor the methodology itself, but a thorough understanding of the everyday problems and needs of the department team.
That knowledge lives in the team itself: the manager, the specialist, the operator. It is contextual, fragmentary, rarely documented. Without extracting it, every solution is reduced to an off-the-shelf tool fitted to a generic problem.
A company's data tends to be just as scattered. It lives in spreadsheets and in systems from different vendors, built at different times and rarely talking to each other. Each tool works correctly on its own; together they form isolated islands with no common point of reference. An off-the-shelf tool knows neither the team's knowledge nor the real context of that data.
Applied AI is the practice of extracting that knowledge and organizing the data context a process relies on, then translating both into working tools fitted to the specific process and team. Over time, successive tools connect into the company's internal ecosystem on a shared data source.
From process to working tool
Four stages, common to every implementation, regardless of the package.
Full methodology description →- 01
Diagnosis
The team's process knowledge is extracted through interviews, workstation observation and workshops. In parallel, data sources are assessed and organized — a stage called context engineering, preceding the build of every tool.
- 02
Design
The problems identified are translated into concrete solutions. At this stage the mode of each tool is chosen and the tools are prioritized.
- 03
Build
Tools are built in iterative cycles, together with the department team. Successive versions are tested on real data and cases, with the timeline matched to the scope.
- 04
Competence transfer
The team takes part in the entire process. What stays in the company is the ability to develop tools independently and to identify further areas for automation.
A solution for one department, on its data
A dedicated micro-application learns from the specifics of the plant: technical documentation, procedures, project history and customer correspondence. That is why it solves the department's specific problem more precisely than a universal tool that lacks this context.
Every assistant answer includes a source reference and the reasoning path. The system is verifiable, not a black box.
Domain assistants and AI agents
Solutions that learn from the plant's technical documentation, procedures and project history. They support the operator, engineer or customer service specialist in daily work, with access to organizational context no off-the-shelf tool has.
Quotation and technical calculation applications
Applications that turn the client's technical documentation into a structured quotation, cost estimate or specification. They combine document analysis with the plant's knowledge base and material selection rules.
Sales and marketing process automation
Classic business applications: custom CRMs, voicebots, content generators, pre-sales briefs. These are tasks companies usually outsource on a permanent retainer — here they are built as a tool on the company's side. Some include AI components at runtime, some run deterministically.
Five steps to working together
- 01Free
Applied AI Test
An online test, 15 minutes. An assessment of the company's readiness across seven dimensions, with a PDF report sent by email.
- 02Free
30-minute consultation
Phone, video or an in-person meeting. Qualifying questions, a presentation of Applied AI, a recommendation of a concrete next step.
- 03from EUR 750 net
AI Readiness Audit
On-site consultant work and a written report. An assessment of processes, data, competences, use cases and ROI, plus a 12-month roadmap.
- 04quoted after consultation
Pilot Implementation
Four weeks of work in one department. Process interviews, workshops, a scoped set of use cases, two working tools implemented, competence transfer.
- 05Retainer
Implementation Partnership
A monthly implementation retainer after the Pilot Implementation. Portfolio expansion across further departments, work with teams and advisory in a single rhythm.
Who leads the implementations
Jarosław Jaśkowiak
Twenty years of experience in B2B business development: sales strategy, structuring sales processes, leading sales teams in mid-sized companies. In recent years, a shift toward designing and implementing Applied AI solutions in manufacturing companies.
The starting point of every AI implementation is mapping the process bottlenecks and building the right context infrastructure — not the technology.
Frequently asked questions
Full FAQ →What does the cost of an implementation depend on?
The cost depends on the scale of the company, the number of departments covered and the scope of tools to be built. The only fixed item is the AI Readiness Audit, from EUR 750 net. The remaining packages are priced individually after the Audit, once the real scope of work is known.
Is there a money-back guarantee?
Yes, at two stages. The AI Readiness Audit is paid only after the report is presented — the client sees the result before paying. In the Pilot Implementation the first week is refundable: if the client withdraws from further work after it, the amount paid is returned. The risk of a poor fit stays on Artech's side.
What stays in the company once the engagement ends?
The tools are built together with the department team, not delivered as a closed product. Along with them stays an understanding of how they work and the competence to develop further ones. The technology stack is an industry standard, not a closed platform, so the company is not tied to a single vendor. Further development can be carried out independently or under the Implementation Partnership model.
How is this different from an agency or external consulting?
An agency or external consultancy delivers repeatable work on a retainer — periodic reports and re-created process documents, with no asset on the company's side at the end. Artech builds that repeatable layer as a dedicated tool that stays in the company and over time stops generating cost. Strategy and hard decisions remain with people.
What about GDPR and data security?
Most tools can be implemented without sending customer data outside — the data stays within the client's infrastructure. In situations requiring full privacy, a local language model is deployed on the client's infrastructure. The choice of path is made at the AI Readiness Audit stage, together with the IT department.
What if our data is not well organized?
That is a common starting point and not an obstacle. The first weeks of work include assessing and organizing the data sources. This stage, called context engineering, precedes the build of every tool. An organized data context emerges during the implementation — it is not a precondition for starting it.
Start with a free 30-minute consultation
The decision on paid engagements — the AI Readiness Audit and the Pilot Implementation — comes after the first conversation.