The Reasoning Company partnered with Medilance Healthcare to build an AI‑powered distributor management layer that tames a fragmented, pan‑India network of products, territories, and data into a single, reliable source of truth. The result is a system that lets Medilance operate with the same speed and confidence at 1,000 SKUs and hundreds of districts as they once did with a handful of products and a few cities.

Medilance's distribution challenge

Medilance runs a PCD pharma and franchise-led model, with tablets, capsules, injectables, ayurvedic lines, and more distributed through partners across India. Each region has its own mix of products, pricing, credit terms, and local business practices, creating a dense web of operational edge cases.

On top of this, Medilance is scaling a catalogue of nearly 1,000 products that cut across specialties, dosage forms, and indications. Administrative data adds another layer of complexity: many market assessments still rely on the 2011 Census and patchy state- or district-level updates, making territory planning and performance benchmarking noisy and inconsistent.

What we built with Medilance

Reasoning Company designed a unified distributor intelligence tool that sits between Medilance's internal teams and its national network of channel partners.

A live catalogue brain for ~1,000 SKUs

The system models every product with relationships to molecule, specialization, pack size, and geography, so product and sales teams can answer questions like "Which pediatric syrups under brand X are underperforming in Tier‑2 cities in East India?" in seconds.

Territory-aware, census‑resilient mapping

Instead of assuming the latest, clean census, the platform learns from whatever is available: 2011 Census, internal beat plans, distributor route books, and ad‑hoc spreadsheets from the field. Performance views remain stable even while the underlying government data lags.

A single pane for every distributor

Each distributor gets a 360° profile combining orders, returns, receivables, stock movement, product mix, and basic territory context. Medilance teams can see in one place which SKUs a partner can reliably push, which ones stall, and where credit or supply issues are silently blocking growth.

Deep integration with CRM and ERP

To make this intelligence usable every day, the platform connects directly into Medilance's existing CRM and ERP stack, meeting teams where they already work. Instead of introducing yet another standalone system, the reasoning layer quietly upgrades the data and workflows inside tools that sales, finance, and supply chain teams open every morning.

Inside the CRM, field reps see territory‑cleaned products, targets, and actuals against each distributor, with unified views of calls, orders, and coverage by specialty and region. Managers can drill from a national or zonal view down to specific distributors, therapies, or product lines, without exporting spreadsheets or reconciling codes by hand.

Inside the ERP, master data for SKUs, pricing, credit terms, and taxes flows into the reasoning layer, while cleaned distributor hierarchies and territory mappings flow back. Over time, this loop steadily improves the quality of core transactional data, reducing mismatches, duplicate codes, and manual adjustments at month‑end.

By sitting between Medilance's CRM, ERP, and its national distributor network, the system absorbs messy, fragmented inputs, normalizes them, and pushes territory‑aware intelligence back into the systems that actually run the business.

Working with Indian data complexity

India's administrative reality is messy: district splits, renamed towns, and uneven digitization mean official data rarely lines up cleanly with how pharma actually sells on the ground. Instead of treating that as a limitation, the Medilance deployment uses multiple signals—internal sales routes, legacy beat plans, distributor codes, and local team inputs—to enrich and reconcile what the 2011‑era view of the country misses.

By combining these sources, the platform surfaced roughly 20–30% more usable territory and outlet data than what was visible in official or legacy structures alone, especially in fast‑changing peri‑urban and semi‑rural pockets. That extra visibility directly feeds into better allocation of distributors, more precise targeting of therapies, and earlier detection of white‑space or under‑served areas.

Impact for Medilance

Within weeks of rollout across an initial set of key regions, Medilance shifted from reactive fire‑fighting to proactive network design.

Field and head‑office teams now work off a single, reconciled view of distributors, territories, and products instead of emailing spreadsheets back and forth.
Network expansion decisions (where to open the next franchise or appoint the next distributor) are grounded in a consistent, territory-aware picture rather than fragmented local anecdotes.
Leadership can ask nuanced questions—by territory, by product mix—and get answers in minutes, not weeks, even with India's dated baseline administrative data.

Time saved and revenue unlocked

Once the system went live in priority zones, day‑to‑day decision cycles shrank dramatically. Territory clean‑ups and distributor performance analyses that previously required a week of spreadsheet work and back‑and‑forth calls across regions now take hours or less, cutting typical analysis and coordination time by an estimated 30–50% for central and regional teams.

Because teams can see which distributors are under‑penetrating specific therapies or product lines in their catchment areas, Medilance has been able to systematically unlock previously hidden revenue pockets. In early deployments, this translated into mid‑single‑digit percentage lifts in addressable revenue in select clusters—driven not by adding new SKUs, but by aligning existing distributors and products with the real shape of demand on the ground.

For Medilance, Reasoning did not just build a tool; we built a distribution layer for the Indian regional markets—a system that learns from imperfect data, respects on‑ground complexity, and still delivers clear, actionable direction at national scale.

How Reasoning Turned Fragmented Pharma Data into a Single Source of Truth