A large petrochemical sector operator runs a seven-year dedicated team engagement with Azati to develop and maintain an on-premise warehouse inventory management platform with AI-assisted nomenclature normalization, addressing high costs from storing, maintaining, and disposing of unclaimed stock.
years of continuous dedicated team development and operation
typical inventory reduction achieved through this kind of nomenclature normalization
typical savings per asset reported for this kind of AI-driven inventory optimization
A large petrochemical sector operator was carrying high costs from storing, maintaining, and disposing of inventory that nobody was using. The platform Azati develops and maintains manages that warehouse inventory, with AI and machine learning components normalizing and matching nomenclature across different coding systems so the same material isn't tracked as several different items.
Azati has run a dedicated team for this client for seven years, covering development, DevOps, and QA. This is the client's first outsourcing relationship for this system, with no other vendors involved before or alongside Azati.
Storing, maintaining, and disposing of inventory that wasn't being used was driving up costs, and inconsistent nomenclature across systems made it difficult to even see the scale of the problem clearly:
The client's infrastructure runs entirely on-premise, which means working within fixed resource ceilings rather than elastic cloud capacity:
This is a multi-year dedicated team engagement, the client's first and only outsourcing relationship for this system. Azati's dedicated team, spanning development, DevOps, QA, and automated QA, has grown with the platform over seven years rather than rotating through it.
Mismatched nomenclature across disconnected coding systems is a common, expensive problem in industrial inventory management. Azati's AI and machine learning components normalize and match that nomenclature directly, turning a manual reconciliation task into something the platform handles on its own.
Azati's QA and automated QA engineers developed automated tests covering the product's key functionality and APIs, introduced multi-threaded test execution, configured CI pipelines, and ran tests in isolated Docker environments.
Seven years on one platform built real expertise in inventory management for the petrochemical sector: normalizing data from disparate ERP systems, optimizing MRO inventory, and integrating with maintenance and supply chain processes.
Seven years, one platform, no vendor turnover. Let's talk about what your inventory or industrial platform actually needs.
Cut my inventory costsAzati's team builds and maintains the core functionality the business interacts with directly: searching warehouse inventory, creating custom scenarios, defining scopes of materials for specific operational needs, and producing dashboards for stock visibility.
AI and machine learning components normalize and match nomenclature for materials coded differently across disconnected systems, addressing one of the more persistent sources of inaccurate inventory counts in large industrial operations.
Azati's QA and automated QA engineers developed automated tests for key product functionality and APIs, introduced multi-threaded test execution, configured CI pipelines, and ran GUI and API tests in isolated Docker environments, with full documentation and execution reports including screenshots, traces, logs, video, execution time, and test statuses.
Azati's DevOps engineer supports the platform's on-premise infrastructure, using Kubernetes and Helm for deployment management and Keycloak for authentication and access control.
Azati staffed a full dedicated team covering development, AI-assisted data engineering, DevOps, and QA and automated QA, plus project management, for this client over a seven-year engagement, on a Dedicated Team basis.
The team worked in an Agile model, with planning shaped around the realities of fixed, on-premise compute rather than elastic cloud scaling:
AI-assisted nomenclature matching gave the business a clearer view of true inventory across previously fragmented records, directly addressing the high storage, maintenance, and disposal costs tied to unclaimed stock.
This is the client's first and only outsourcing relationship for this system. Seven years of continuous delivery, with no need to bring in another vendor, reflects sustained trust in the engagement.
The engagement built expertise in inventory management for the petrochemical sector: normalizing data from disparate ERP systems, optimizing MRO inventory, and integrating with maintenance and supply chain processes, along with deploying AI and machine learning solutions inside on-premise infrastructure.
What this engagement demonstrates beyond the feature list:
A lot of unclaimed or duplicate inventory exists simply because the same material is recorded under different codes in different systems. Solving that matching problem with AI directly addresses the cost driver, rather than layering better reporting on top of inconsistent data.
A dedicated team that grows with a system for seven years, across development, AI, DevOps, and QA together, accumulates institutional knowledge of the platform and the client's domain that a rotating vendor roster never builds.
Azati's dedicated team for this engagement combined development, AI, infrastructure, and quality engineering roles working as a single unit over seven years.
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