What they do
Indic EMS builds electronic circuit boards and finished electronic products for other companies: the customer designs the product, Indic buys the components and manufactures, assembles and tests it. Its buyers are automotive, telecom, medical, industrial, IoT and renewable-energy companies in India and abroad, and most of what it makes is exported.
Inside, day to day. Production operators, around 80 percent of them women, spend the day on surface-mount assembly lines placing and soldering components onto boards; engineers write test programs, quality staff inspect boards for defects, and planners and buyers juggle hundreds of component line items per order across many suppliers, largely on spreadsheets and email because every customer order is a different small batch rather than one long repeat run.
The customer test report drafts itself, in whichever format that client uses
Today a test engineer runs every device through the site's MDA, ICT, ATE and Functional Test rigs — scripted in LabVIEW and Python — then hand-writes the pass/fail numbers into whichever of Indic's 30+ active clients' own report template applies, redone from scratch every time because each client's format is different.
With AI he builds an agent that reads the raw rig log and drops the same numbers straight into whichever client's template is selected, and he edits the mapping himself the day a client changes its layout — one test run becomes one drafted report instead of one manual rewrite, and he spends the freed time on the borderline failures that actually need his judgement. That is time back.
Five minutes open a synthetic rig log on screen, labelled as such, run it against one client's report template and read out the populated report, then live in front of the room switch the target to a different client's template that adds a required "operator ID" field and re-run the same log, so the room watches the report reshape around the new format instead of repeating the last output.
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The batch write-up cites the standard it actually failed
Today a quality engineer inspects and audits each batch against the certified standards Indic holds — IPC-A-610J, ISO 9001:2015, IATF 16949:2016, ANSI/ESD S20.20-2020 and IPC/WHMA-A-620 among them — and when a batch fails, writes the root-cause analysis and corrective action up by hand, batch by batch, across a line assembling more than 10.5 million PCBAs a year.
With AI she builds an agent that reads the batch's defect data and drafts the root-cause write-up against the specific standard clause it actually failed, and she corrects the causal chain herself whenever the failure pattern is new to it — the write-up stops eating into her audit time, so more batches actually get walked and audited in the same day. That is productivity.
Five minutes open a synthetic batch defect log on screen, labelled as such, run the agent and read out the drafted root-cause note and the standard clause it cites, then live change the defect type in the log from a solder bridge to a cold joint and re-run it, so the room sees the cited clause and the root-cause hypothesis both change with the data.
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The proposal's technical scope reshapes itself when the requirement changes mid-call
Today a presales executive confers with a prospective customer and Indic's own engineers on requirements, then writes the resulting technical proposal from a blank page — for a company already running 14+ programmes per existing client, no two conversations turn up the same requirement set, so no proposal starts from the last one.
With AI he builds an agent that takes his call notes and drafts the proposal's technical-scope section against Indic's own service lines — New Product Introduction, Product Engineering, Testing and Quality, Custom Box-Build, Global Supply Chain — and he edits the wording himself as the scope shifts mid-conversation. A scope draft ready before he is off the call means more prospective engagements answered in the same week. That is revenue.
Five minutes open a synthetic requirements brief on screen, labelled as such, run the agent and read out the drafted scope section, then live — as if a customer just said so on the call — strike New Product Introduction from the brief and add box-build only, re-run it, and show the scope section drop NPI and rewrite itself around box-build alone.
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The manufacturing paperwork updates itself when the BOM does
Today a designer takes the finished board file out of Altium Designer, Eagle CAD or OrCAD and writes up the accompanying manufacturing documentation for the floor by hand — a fresh set for every product that goes through Indic's New Product Introduction line, because each one is a different customer's design.
With AI she builds an agent that reads the exported netlist and BOM and drafts the new manufacturing documentation against her past sets, and she is the one who corrects the callouts whenever a customer's design uses an unusual component footprint — the paperwork moves from a write-up per introduction to a draft she checks and tunes, and she keeps extending the callout rules herself as new component families show up. That is skill.
Five minutes open a synthetic exported BOM on screen, labelled as such, run the agent and read out the drafted documentation and its component callouts, then live swap one resistor's value in the BOM for a different one and re-run it, so the room watches the matching callout in the documentation change to match.
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The shipment list reprioritises itself when a promise date moves
Today a logistics executive coordinates shipment and inventory flow by hand across every one of Indic's 30+ active clients, each running roughly 14 programmes, under a high-mix, many-small-batches model rather than one long repeat run — and we could not establish which system, if any, sits between the company's newly upgraded ERP and this day-to-day coordination desk, so exactly what tool this runs through today is unclear.
With AI she builds an agent that reads the day's confirmed inbound shipments and open orders and drafts a priority list of which ones are actually at risk, and she edits the escalation rules herself when a client's terms change — she spends less of the day chasing every line item and more of it on the shipments that need a person's call. That is time back.
Five minutes open a synthetic multi-supplier shipment list on screen, labelled as such, run the agent and read out the priority order, then live move one shipment's promised date forward by a week and re-run it, so the room watches that shipment jump up or drop down the priority list in front of them.
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The production schedule adjusts itself when the line count does
Today a production manager builds and adjusts the schedule and tracks inventory and efficiency by hand every time customer demand shifts across the mix of 30+ active clients running roughly 14 programmes each, now spread across five more surface-mount lines than the floor had before, ten going to fifteen.
With AI he builds an agent that reads the current order book and line status and drafts the next schedule revision, and he adjusts the resourcing rules himself whenever a new line or a new client programme comes online — the revision that used to be rebuilt from scratch becomes one he checks and releases. That is productivity.
Five minutes open a synthetic order book and line-status sheet on screen, labelled as such, run the agent and read out the drafted schedule, then live add one more rush order to the sheet and re-run it, so the room watches the schedule redistribute across the fifteen lines in front of them.
@sahidmodi45