How Avinmont turned a five-day aerospace workflow into a seventeen-minute task.
An aerospace parts manufacturer came to Avinmont Strategy with a process that was quietly eating one of their best people's entire week, every week. What Arjun Khinvasara and the Avinmont team built in return now does the same work in about seventeen minutes per run, and can run fifty of those at once, without losing accuracy.
Five days of manual work every week. Now seventeen minutes per run, fifty in parallel.
Situation
Like many mid-sized manufacturers, the company had grown faster than its back-office could scale. The specific workflow the Avinmont team was asked to replace was neither glamorous nor unusual. It was a repeatable, document-heavy process that depended on a single senior person reading, cross-referencing, and structuring information from several different sources. It took that person five working days to complete. It happened every week. And because nobody else in the company had the context or the patience to do it well, it could not be delegated.
The leadership team had already considered the obvious alternatives. A new hire would take months to ramp and would eventually hit the same ceiling their senior employee had hit. Off-the-shelf software did not exist for work this specific. Generic AI tools could do pieces of the process but nothing end-to-end, and nobody on the team wanted to babysit a chatbot through a week-long task.
The question they brought to Avinmont was the right one: could a custom AI agent be built that does the whole thing, without asking anyone to become a prompt engineer on the side?
Approach
Avinmont Strategy approaches every agent build the same way. Before a line of code is written, we spend time with the person who actually does the work. In this case, that meant sitting with the senior employee whose week was going to be reclaimed. We documented every decision, every reference point, every judgment call. We wrote down which steps required human context that could not be automated, and which were mechanical: steps that repeat, that have answers somewhere, that a system can be taught to do reliably.
Arjun Khinvasara led the scope call with the manufacturer's leadership and ran point with their technical team through the design phase. The architecture was approved on paper before any code shipped: a clean separation of reading, reasoning, and writing into distinct passes, with safety rails at every handoff. This was not a prompt wrapped around a general-purpose chatbot. It was a system, built for this workflow and nothing else.
The build itself took a few weeks of focused engineering. Avinmont ran the agent alongside the senior employee for several cycles, comparing outputs side by side. Where the agent made mistakes, each one got written into a permanent rule the system would follow going forward. By the end of validation, accuracy was consistently above ninety percent. Where accuracy could not be guaranteed, where edge cases showed up, the system's exception-handling logic flagged the case to a human and kept the rest of the run going.
Result
Five working days of work now happens in about seventeen minutes per run. Accuracy holds above ninety percent, with error detection and exception handling catching the edge cases that used to land on a person's desk. Fifty parallel sessions run concurrently without degradation, so a weekly task has become an on-demand capability the business can run multiple times a day without adding a single desk to the floor.
The quieter result is the one the company's leadership cares about more. The senior employee whose time was being consumed by the workflow is now free to do the work that required her judgment in the first place, the kind of work the company hired her to do. Output scaled without a new hire. The planning ceiling that had shaped the company's thinking for the past two years no longer existed. The custom AI agent Avinmont built is still running, gets sharper with every correction, and costs a fraction of what a hire would have cost for the same output.
What This Proves
Not every workflow needs a tool. Some need a replacement. The manufacturer had tried tools already. None of them removed the bottleneck, because the bottleneck was the human in the middle of the workflow. A custom AI agent removes the human from the middle by doing the whole process, not a piece of it.
Parallelism is the ceiling most businesses cannot see. A person can only do one task at a time. A well-built AI agent can run fifty in parallel without losing accuracy. That changes what a business can commit to, not just in speed, but in scope. And the real outcome is what the person freed up does next. Every custom AI agent Avinmont Strategy builds is designed around that logic.
Have a workflow inside your firm that is eating a person's week?
Tell Avinmont Strategy about it. Thirty-minute call. We will tell you honestly whether a custom AI agent is the right answer, what it would cost, and how long it would take. Most builds land between four and eight weeks.
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