Customer requests that previously required weeks can now be processed 80% faster

Theegarten-Pactec is a leading specialist in packaging applications for the confectionery and food industry. Its machines are used by global consumer goods companies with demanding production environments, strict quality requirements, and highly specific technical standards.

Each customer project begins with detailed requirements, often in the form of URS documents, specifications, technical questionnaires, or customer-specific standards. These documents define how a machine must be configured, documented, validated, and delivered.

Theegarten-Pactec’s customers often provide extensive technical specifications that are updated regularly. Customers send long URS packages, technical documents, and requirement lists that must be reviewed with high precision before the company can respond.

This created a major challenge for the technical sales team. Each request has to be read, classified, commented, and translated into Theegarten-Pactec’s internal working format. Relevant requirements then have to be routed to the right departments, such as mechanical engineering, electrical engineering, automation, documentation and project execution.

The process is heavily dependent on experienced employees. These experts rely on years of product knowledge, customer history, standards interpretation, and practical project experience. This makes technical sales a competitive advantage, but also created know-how silos.

If requirements in the past were missed, updated standards overlooked, or red flags identified too late, the impact could have been significant: longer response times, avoidable clarification loops, margin risk, and additional engineering work later in the project.

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The Solution

Together with Atira, Theegarten-Pactec introduced an AI-supported process for orchestrating the Inbound processes, starting with automated URS commenting.
The Atira agents read incoming customer documents - whether PDF, Word, Excel, scanned files, technical questionnaires, or emails and translate them into Theegarten-Pactec’s internal working format. They extract relevant technical and commercial requirements, classify them, comment on them while referencing the underlying knowledge base.
The system builds on historical URS documents, Theegarten-Pactec standards, component knowledge, and department feedback. Instead of starting every review from scratch, teams receive a structured, AI-supported draft that highlights what matters for each department.
This turns customer specifications into a reusable knowledge base and creates the foundation for faster, more consistent technical sales work.

Automated URS and Specification Commenting

Atira automatically processes customer URS documents and technical specifications independent of input format. The agent identifies individual requirements, maps them into Theegarten-Pactec’s internal format, and creates initial comments based on internal knowledge sources, component details, past project history, and company standards.
This gives technical sales teams a structured starting point for review. Instead of manually searching through long customer documents, employees can focus on validating AI-supported recommendations, resolving exceptions, and preparing a high-quality customer response.

Department Routing and Knowledge Capture

Customer requirements are routed to the relevant in-house departments, such as mechanics, electrical engineering, automation, documentation and  project management.
Each department receives the requirements relevant to its scope, while all comments, decisions, and project-specific knowledge remain stored in one central database. This makes information easier to find during later stages of the project and ensures that knowledge does not remain locked in individual inboxes, spreadsheets, or memories.
Over time, each processed customer request strengthens the company’s institutional knowledge.

AI as a Recommendation Layer

Atira does not replace expert judgment but acts as a recommendation layer for technical sales and engineering teams.
For example, when a customer requirement specifies a noise emission limit, the system can compare that requirement against machine documentation, known performance values, and existing standards. It then suggests whether the requirement is fulfilled, not fulfilled, or requires clarification - with reasoning and source references.
The final decision remains with Theegarten-Pactec’s experts, but the AI helps ensure that critical red flags, deviations, and optimization opportunities are surfaced earlier.

URS Commenting as a foundation for additional Use Cases

The URS commenting process created a shared data foundation for further AI-supported use cases.
The same structured knowledge can support comparisons between international norms, for example US and EU standards, and help align customer requirements with CE declarations or machinery directives. It can also support machine documentation drafts, standardized specifications, and long-term reuse of project knowledge in engineering and order execution.

Outcome

Theegarten-Pactec is transforming its technical sales from a linear, manual process into a systematic and growing knowledge asset.
Complex customer requests that previously required weeks of expert review can now be processed 80% faster, with AI-supported comments, requirement classification, source references, and department routing available early in the process.
The result is faster response times, more consistent customer communication, reduced risk of missed red flags, and better preservation of internal know-how.
For Theegarten-Pactec, AI becomes a multiplier of technical expertise: helping experienced employees focus on the highest-value decisions while making their knowledge available across projects, departments, and future customer requests.

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