Walk through the office of almost any distributor, manufacturer, or specialty contractor and you will find the same pattern. The company runs a capable system of record: an ERP, a CRM, an estimating or field-service platform. Around it sits a layer of people moving information by hand. Purchase orders arrive as PDFs and get retyped. RFQs arrive with drawings and get read line by line. Customers email to ask where their order is, and someone looks it up and writes back.
None of that work is strategic, but all of it is necessary, and it scales with volume. When orders grow, the order desk grows. When bids grow, estimators fall behind. Zyene exists to take that layer of manual handling and turn it into a system.
What we build
We design, build, and integrate production AI workflows that sit on top of the systems you already run. A typical workflow reads incoming work (an email, a PDF, a spreadsheet, a drawing), checks it against your ERP or CRM, flags what is missing or unusual, and prepares the next action: an order, a quote package, a reply, or a checklist.
The six areas we work in are document intelligence, order and quote automation, workflow agents, ERP and CRM integration, enterprise knowledge search, and AI operations assessments. Each one is a building block. Most engagements combine two or three of them around a single workflow.
How the work stays in your systems
The system of record stays where it is. The workflow writes into it the same way a person would, with the same permissions and the same audit trail.
People stay on decisions that matter. Orders above a threshold, price exceptions, and anything customer-facing can wait for an employee to approve. The goal is to remove the retyping, not the judgment.
Every engagement starts by agreeing on a metric, such as processing time, manual touches, or exception rate, and the pilot is judged against it.
How an engagement starts
Every project begins with an assessment of one workflow. We map how the work happens today, including the exceptions, identify which systems and data we can reach, and recommend a pilot with a clear metric. From there the work follows the same path: discover, design, build, integrate, validate, then deploy and improve from real use.
