AI for Bangladesh's RMG Industry
Computer vision QC, predictive maintenance, and demand forecasting — built for 500,000-unit-a-month garment factories that ship to Walmart, H&M, and Inditex.
The problem we solve
The Ready-Made Garments sector accounts for roughly 84% of Bangladesh's export earnings, and a typical mid-sized factory in Ashulia, Gazipur, or Chattogram pushes 300,000 to 500,000+ units a month through a floor that still runs on 15 to 25 human QC inspectors per line. Inline inspectors check stitching at the source, end-line inspectors check fully assembled garments, and a final AQL audit team samples cartons before they ship. Even with all three layers, defective units leak through — and every leaked defect costs USD 0.50 to USD 3.00 in buyer chargebacks, plus rework, plus return shipping if the carton is rejected.
The bigger pain isn't the per-unit chargeback. It's what happens when a buyer like Walmart, H&M, Inditex, or Marks & Spencer flags a quality slip in a Higg FEM, SEDEX, or RIBA audit. Once a buyer marks a factory as a risk on their compliance scorecard, the next PO either shrinks or moves to Vietnam. For a factory carrying USD 4-8 million a month in PO value, that decision happens long before management even sees the defect data.
Then there's the operational drag the boardroom never sees. Pattern matching variance shifts when the inspector changes between the day and night shift. A single Juki or Brother lockstitch machine going down on a critical operation can lock up a whole line for 30-90 minutes. Fabric procurement is locked in months before the actual demand signal arrives. Each of these is solvable with AI today — but the solutions have to be designed for a factory floor with 3,000+ workers, intermittent ERP data, and buyers who expect a fully documented audit trail.
What we deliver
Concrete engagements, not slide decks. Each one scoped to ship something measurable inside 90 days.
Inline Computer Vision QC Pilot
We install camera rigs and edge inference at one or two critical operations (e.g. collar attach, side seam, final press). The model learns your fabric, your patterns, your defect taxonomy. Pilot runs 60-90 days end-to-end with a buyer-presentable defect dashboard. Designed to coexist with your inline inspectors, not replace them on day one.
Higg, SEDEX & Buyer Compliance Reporting
AI-generated audit packs for Higg FEM/FSLM, SEDEX SMETA, Walmart RIBA, and BSCI. The system pulls quality, energy, water, and chemical data from your existing ERP/MES, flags anomalies before the audit, and exports the evidence trail buyers expect. Less scramble before every audit cycle.
Defect Detection Rollout Across Lines
After a successful pilot, we scale the vision system across 10-50+ sewing lines, tune the model per product type (knitwear, woven, denim), and integrate the defect feed into your existing line manager dashboards. Includes shift-handover briefings so QC supervisors get the same defect signal regardless of who's on the floor.
Predictive Maintenance for Sewing Machines
Vibration and motor-current sensors on critical machines, paired with a model that predicts bearing failure, motor overheating, and needle breakage trends. The goal is to schedule the maintenance technician before a machine locks up a line for 90 minutes — not after.
Demand Forecasting & Fabric Procurement
Forecasting models that combine your historical PO patterns with buyer demand signals, fabric lead times from Chinese and Indian mills, and seasonality. The output: better fabric booking decisions 8-12 weeks before production, fewer panic air-freight shipments, less dead stock.
Outcomes you can expect
These are sanitized ranges from real engagements and reasonable industry benchmarks — not anonymized fake numbers. Where the ceiling lands for you depends on your data, your team, and how quickly you can act on the model's output.
Defect leakage cut by 60-80%
Typical range for inline computer vision on stitching defects within 90 days of a well-scoped pilot. The exact starting and ending defect rate depends on your fabric, line speed, and existing inspector workload.
Audit prep time reduced from weeks to days
For factories running Higg FEM, SEDEX SMETA, and 2-3 buyer audits a year, automated evidence collection collapses the manual data-pull burden the compliance team carries.
Unplanned line downtime down by 25-40%
Predictive maintenance impact range across 50-200 sewing-machine fleets. Best results come from pairing AI with a clear escalation rule for the maintenance shift.
Fabric over-booking reduced by 10-20%
Forecasting impact for factories with at least 18 months of clean PO and consumption data. The biggest gains are on seasonal categories where lead times are tightest.
Related reading
Ready to scope a pilot?
We'll spend 30 minutes understanding your operation, then come back with a one-page pilot plan and an honest range on cost, timeline, and expected outcome.