Herodotus
A new standard in maintenance powered by AI
Why implement AI in maintenance operations?
Until now, knowledge was scattered — across files, notes, and the memories of experienced staff. Every failure meant searching for answers, asking questions, and losing time. Herodotus introduces a new operational model: knowledge is always available, always up-to-date, and contextual — integrated in one place, powered by AI and enhanced with a RAG (Retrieval-Augmented Generation) architecture.
This is not just a chatbot. It’s a system that retrieves specific technical data, cites sources, and supports operational decisions — reducing MTTR, eliminating recurring issues, and empowering operators to act independently.
Common challenges in maintenance operations
Knowledge is scattered and unavailable when needed
Documentation, service notes, and failure history — scattered across various systems and files.
Dependence on experienced employees
Operational knowledge is undocumented, “scattered across shifts,” and difficult to transfer to new employees.
Excessive time to diagnose and repair (MTTR)
Employees waste time searching for information and asking questions, relying on intuition rather than data.
Recurring errors and lack of standardization
The same failures are solved repeatedly from scratch, with no systematization or prevention in place.
How Herodotus works – features that deliver real results
Centralization of technical knowledge
It integrates documentation, tickets, checklists, and logs — all accessible in one place.
Natural language search
Ask questions like “how to fix error E23 on line X?” and get precise answers from the documentation.
24/7 availability — regardless of shift or experience level
The system supports everyone — from novice to expert, regardless of knowledge level.
Root Cause Analysis and continuous improvement
Helps identify root causes, generates topics for TPM/RCM meetings, and supports Kaizen initiatives.
Data security and control
Processes only client data, operates on-premise or in the cloud in accordance with IT policy.
Support for Autonomous Maintenance (AM)
Up to 40% of failures resolved without maintenance team involvement — directly by operators.
Safe deployment of an AI system into the production environment
Analysis and diagnostics
At this stage, we conduct a detailed audit of technical documentation, incident history, procedures, and IT infrastructure. The goal is to understand maintenance processes and identify areas where Herodotus can deliver the greatest operational value.
Configuration and customization
Based on the collected information, we configure the system to reflect the client’s organizational structure and real operational processes. We build a unified knowledge base and define access rules as well as integrations with existing tools.
Deployment and user training
In this phase, we launch the system in the production environment, conduct tests, pilots, and hands-on training. We teach teams how to use Herodotus effectively in daily operations — for troubleshooting, RCA (Root Cause Analysis), and preventive actions.
Support and solution development
After deployment, we provide ongoing technical support, knowledge base development, query optimization, and guidance on further integration with the client’s systems. Our goal is continuous improvement of the solution and its long-term value for the organization.
What do you gain by implementing Herodotus with our team?
- 50–70% reduction in downtime
- Shorter MTTR and faster incident response
- Increased operator autonomy (AM – Autonomous Maintenance)
- Retention and accessibility of operational knowledge
- Full security and compliance with IT policy
What do you gain by implementing Herodotus with our team?
Herodotus is applicable in:

Large-scale manufacturing facilities with complex machine infrastructure

Pharmaceutical, food, automotive industries and logistics

Organizations operating in shifts and with high staff turnover

Companies investing in standardization, Autonomous Maintenance, and AI-driven maintenance operations

Discrete and high-volume manufacturing environments where response speed and operational continuity are critical
AI/ML
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