Indigenous AI/ML, built for Indian sales & distribution. From territory design to the next SKU to pitch, five engines turn outlet behaviour and sales trends into ready-to-act recommendations — and every one is reviewed and approved before it reaches the field.
Territories and journey plans get drawn in Excel and then rot; reps guess the next SKU; and most "AI" is a black box no sales leader will hand the field over to.
Territories and PJPs are set once and never adapt to how the market actually moves.
Reps pitch on instinct, so the right SKU for each outlet is missed again and again.
Leaders won't cede the field to recommendations they can't see, review or override.
Accept an AI recommendation and watch the territory rebalance — coverage, productivity and projected revenue update live across the whole plan.
The engine scores each outlet by value, channel and order frequency and proposes re-tiering moves. Approve one and the planned coverage by outlet type re-plans instantly — so beats and schemes follow the money, not habit.
Permanent journey plans are balanced across the cycle and converted into daily beats sequenced for least travel. Approve a recommendation and the beat redraws — coverage rises without adding reps or kilometres, and the projected impact updates live.
For every outlet the engine ranks the next SKU to pitch — cross-sell, MSL gap-fill or a focus push — from that outlet's behaviour and look-alike demand. Approve a recommendation and the smart cart re-stocks with new lines and tags, each carrying its projected lift.
The CAPA engine watches for what's slipping — a degrowing outlet, an under-stocked launch, a competitor gaining shelf — and proposes the corrective action. Approve one and it dispatches manager joint visits, product-activation drives and degrowth market-intelligence captures to the field.
Daily and monthly scorecards grade every rep and beat automatically and surface what to do next. There's nothing to approve here — the manager simply marks each item reviewed and acts on the AI's suggested next step, from a ride-along to a knowledge assessment.
The engine drafts; a manager reviews the projected impact and overrides if needed; only then does it publish. That governance is why sales leaders actually adopt it.
Engines generate ranked recommendations with the projected lift for each.
A manager sees the rationale and impact, then edits, approves or rejects.
Approved actions flow to the field app as beats, tasks and pitch suggestions.
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Book a walkthrough — we'll show recommendations, projected lift and the manager approval flow.