What the work looks like in practice.

The case studies below are illustrative and anonymised — representative of the engagements we run rather than named accounts, because client confidentiality is part of how we are security-first. They are deliberately modest and deliberately specific: a real challenge, the AI solution we would scope, and the kind of measurable outcome a pilot is designed to produce.

RMG & Manufacturing

Defect detection on the line

Illustrative case study

Challenge. An apparel exporter was catching stitching and print defects only at final inspection, after value had already been added — driving rework and occasional customer returns. AI solution. A computer-vision model, trained on the factory’s own labelled images and run on a camera at the line, that flags suspect units for an operator to confirm. Outcome. In pilot, a representative line caught a clear majority of defects earlier in the process and cut downstream rework noticeably — the gate that justified moving to a managed retainer.

Pilot · Computer vision & quality
Banking, Fintech & NBFI

Fraud screening in real time

Illustrative case study

Challenge. A fintech wallet relied on static rules for fraud, which missed new patterns and flagged too many legitimate users. AI solution. A risk-scoring model running inside the institution’s environment, scoring transactions in real time and returning an auditable reason for every decision. Outcome. In pilot, the model reduced confirmed fraud losses on the scored segment while lowering false positives — fewer good customers stopped, and every block explainable to a regulator.

Pilot · Risk & fraud
Retail, E-commerce & Telecom

A Bangla support copilot

Illustrative case study

Challenge. An online marketplace’s support team was overwhelmed by routine Bangla-language queries — order status, returns, delivery — and response times were slipping. AI solution. A Bangla-language assistant grounded in the marketplace’s own help content and order data, drafting replies for an agent to approve. Outcome. In pilot, the assistant handled a substantial share of routine tickets end to end and shortened average response time, freeing the team for the harder cases.

Pilot · Conversational AI
Retail, E-commerce & Telecom

Demand forecasting across outlets

Illustrative case study

Challenge. A multi-outlet retailer planned stock on spreadsheets and intuition, swinging between stock-outs on fast movers and overstock on slow ones. AI solution. A demand-forecasting model built on the retailer’s sales history and seasonality, producing outlet-level reorder guidance the planning team reviews. Outcome. In pilot, availability on key lines improved while overstock fell — working capital freed without losing sales.

Pilot · Forecasting & analytics
How to read these

Modest claims, measured the same way.

We keep these examples illustrative on purpose — we will not parade a named client’s numbers to win a meeting. What is real and consistent is the shape of every engagement: a problem the business already feels, a pilot scoped tight enough to prove the case quickly, and an outcome tied to a metric the client tracks. The numbers will differ for your operation; the discipline will not.

§

The pilot carries the proof.

Before any retainer, a scoped pilot has to move an agreed number. If it does not, we say so plainly — that honesty is why our pilots are low-risk to start.

§

Your results stay your results.

Confidentiality is part of being security-first. Your data, models and outcomes are yours — we will only publish what you explicitly agree to.

If one of these looks like a problem you have, let’s scope a pilot.

Make an enquiry