Order-Entry Automation — Enterprise Manufacturer

The starting point

The organization is an enterprise-scale manufacturer. Its order-entry automation platform — first-generation, deterministic, built in-house — was one subsystem within a much larger enterprise order-entry and lab-management ecosystem.

On the largest product line, that platform had taken automation to 30%. There it stopped. Rule-based processing had reached its limit at scale, and the remaining orders — the unstructured exceptions no rule could express — moved through manual steps, case by case.

What changed was the technology. AI had become capable of interpreting exactly that class of exceptions, and the question became how to introduce it into a live production system without disrupting what already worked.

The engagement

iQonAi led the engagement with deep familiarity with the platform and its domain.

iQonAi served as Project Lead and Technical Lead, responsible for solution design, technical direction, architecture, project execution, and coordination across the organization’s architecture, AI/ML, cloud, and development teams.

The system

The AI layer was bounded by design. It did not replace the existing platform — it took on the class of work the platform’s rules could not, while everything deterministic stayed exactly where it was. Each step kept the tool it fit: a rule, a lookup, or — where interpretation was required — the new AI layer.

The biggest technical challenge was not the model. It was supporting multiple generations of legacy and modern lab-management systems across both on-prem and cloud infrastructure — different scanner systems, product catalogs, schemas, and downstream order-entry systems, where an order might need to move between any combination of legacy and newer systems. We solved that by building a translation layer that normalized scanner data into a common model before mapping it into the appropriate downstream system — preserving compatibility with existing systems while allowing newer ones to be introduced over time.

Organizationally, cross-team prioritization created delays. We reduced that friction by making the system composable enough for multiple teams to test competing approaches against the same workflow — each could be evaluated independently and integrated without redesigning the core system.

The rollout

The team first deployed the AI layer in ghost mode against live production data, allowing inference results to be validated against actual human-processed orders without affecting production. Once accuracy was established, the system was enabled on live traffic and rolled out incrementally by scanner system — never the entire operation in one cutover.

The project went from start to production in approximately three months.

Measured results

Automation of the largest product line went from 30% to 98% over 6 months.

Within 3 months of launch, projected savings in reduced manual processing cost reached $1.8M, calculated from actual labor cost and prior-year case volume.

Headcount reduction was never the objective. The goal was to remove repetitive order-entry work so existing staff could absorb growth and focus on higher-complexity cases and exceptions.

Where it stands

The system remains in production and has expanded beyond the original product line to support additional workflows and newer AI capabilities, including digital scan analysis.