Client names withheld where requested. Numbers and frameworks are representative of real work: pricing and market economics first where that is the lever; process, dashboards, and automation when execution is the bottleneck.
How an Indian agri-commodity exporter identified ₹44 Cr in three-year revenue uplift — without a single new customer.
Input40+ destination exports, pricing set largely by market feel.
MethodASP-gap mapping, market ranking, and scenario revenue modelling.
ResultModelled ₹44 Cr three-year uplift in the moderate case.
A mid-sized Indian exporter with revenues above ₹200 Cr was selling
into 40+ countries but pricing by feel — benchmarking against what
competitors seemed to charge and adjusting for relationships rather
than economics.
The business had strong certifications, a credible manufacturing
footprint, and a product that global buyers valued. It was not
capturing that value in its prices.
What we found: Using trade data (e.g. UN Comtrade) and our economic intelligence
approach, we mapped the gap between India’s realised export ASP
(average selling price per kg) and the market average in each
destination. The gap was consistent and significant: in their
largest market, the company was realising ₹21 per kg less than
the market average — on every kilogram shipped.
Root causes: commodity-grade positioning versus specification-led competitors, pack format limitations, and certifications not reflected in pricing architecture.
What we built: A market attractiveness view across seven priority markets,
ranked by import potential, India’s current position, ASP gap, route
economics, and competitive dynamics. An interactive scenario revenue
model — conservative, moderate, and ambitious — from the company’s
own data. A pricing architecture recommendation around four product families rather than one commodity grade.
Outcome: The moderate scenario projects ₹44 Cr in incremental revenue by year
three — through ASP improvement on existing volumes (no new customers
required), selective expansion into two high-ASP destinations,
and formats that unlock premium-tier buyers. Delivered in three weeks; the decision framework is owned by the client’s management team.
Manual follow-up replaced by a designed operating system — end to end.
A growing service team was losing visibility across leads, callbacks, and collections. Work lived in personal habits, not a shared operating system. We mapped the workflow first, then deployed AI-driven automation on a documented foundation.
Mapped the full follow-up path from inquiry to payment
Deployed AI-driven WhatsApp reminders and status routing
Built a decision dashboard around the collection and lead review cycle
Significantly lessmanual chasing
Materially fasterlead response
Oneshared source of truth
Education Operations · Operations & Process Design
Administrative load dropped when the operating model was redesigned, not just digitised.
Staff were spending large amounts of time reconciling attendance, communications, and payment status across disconnected records. AI automation was introduced only after the workflow was restructured and data foundations were established.
Process map separated mandatory operational events from noise
AI-driven reminder flows deployed on a documented workflow logic
Live leadership reporting built on a clean data foundation
Dramatically fewerroutine admin calls
Near-zeromanual reconciliation
Liveleadership reporting
Operations & Inventory · Technology & Data
Leadership moved from reactive reporting to structured, real-time operational visibility.
An operations team had data in several systems but no usable view of inventory, throughput, or exception handling. AI anomaly detection was layered onto a decision-first dashboard built around the exact choices leadership needed to make daily.
Mapped the decisions leadership needed to make before designing the dashboard
AI-assisted anomaly detection surfaces exceptions before they escalate
The tool is not the story. The operating change is.
Whether the lever is economic modelling or operating design, the thread is the same: name the decision first, apply a defensible framework, deliver in milestones you can approve, and leave your team with documentation they own.
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