World's First AI Native IR for GT Built in Just 3 Clicks
Train SCAI Vision in 3 clicks, automate store-level display and visibility tasks, and verify in-store execution in under three seconds with AI-Native image recognition.
AI-Native Image Recognition
AI Native IR
Built for Kirana Shelves
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Turn Shelf Photos into Retail Execution Decisions
SCAI Vision converts shelf images into store-level outputs for out-of-stock
monitoring, share-of-shelf analysis, planogram and POSM checks, and suspicious-submission
review.
Six Retail Execution Checks from
Every Shelf Photo
Every shelf image is analyzed in seconds to deliver AI-powered insights for availability, shelf share, compliance, competition and fraud detection.
Availability / OOS
Detect stock-outs instantly and improve on-shelf availability.
Share of Shelf
Measure your brand's shelf presence vs. competitors. Track facings, position, & dominance.
Planogram Compliance
Verify planogram compliance and detect execution gaps instantly.
Competitor Detection
Monitor rival SKU activity, new product launches, & pricing moves across outlets.
Fraud Detection
Detect fake photos, duplicate submissions and GPS fraud automatically.
POSM Compliance
Verify display materials, branding assets, and promotional setups are in place & compliant.
Poor Shelf Execution Can Create
Hidden Sales and Margin Loss
Stock-outs, incomplete displays, unverified visits and inconsistent shelf execution can reduce visibility and delay corrective action. SCAI Vision helps teams identify store-level exceptions from shelf images so managers can prioritise follow-up.
Turn Every GT Image into Outlet-Level Execution Intelligence
SCAI Vision analyses a shelf photo to capture product availability, stock gaps, shelf share, planogram compliance, point-of-sale material visibility and distribution indicators. The resulting data can be reviewed by field managers and sales leaders.
Identify On-Shelf Availability Gaps During Store Visits
SCAI Vision identifies missing stock-keeping units from shelf images and can surface on-shelf availability gaps during a store visit. This helps field managers review gaps without waiting for a separate manual audit report.
Built for Retail Execution Across General Trade Outlets
General Trade shelves are rarely organised or consistent. Products may be stacked, partially covered, poorly lit or mixed with competitor and unbranded packs. SCAI Vision is designed to recognise products and execution gaps across these everyday retail conditions.
Connect SCAI Vision to Your Existing Sales Systems
SCAI Vision can be evaluated for integration with sales force automation, distributor management, eB2B, enterprise resource planning and customer relationship management systems. Publish named connectors, API capabilities, data flows and security controls only after technical confirmation.
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Built for the Complexity of General Trade
SCAI Vision is trained and tested in real General Trade environments, including low-light stores, cluttered shelves, coolers, sachets, mixed packaging, and rural outlets, delivering reliable SKU detection in everyday retail conditions.
Questions About AI Native SCAI Vision GT
The model trains on your SKUs in 3 clicks and returns results in under 3 seconds.
"AI Native" describes the architecture, not the feature. Because SCAI Vision runs on the same composable micro-services platform and the same data spine as SFA and DMS, a detected gap becomes a task in the rep's app during the visit — not a finding in a report days later.
That distinction is the whole point in general trade, where the visit is minutes long and there's no merchandiser coming back to fix what an audit found.
Modern trade shelves are structured, planogrammed, and consistently lit — so the work is compliance scoring against an approved layout. General trade shelves are a different world:
So GT recognition has to identify products without structural assumptions — in bad light, at odd angles, from a mid-range phone, often with no connectivity.
SCAI Vision is trained for both channels on one recognition core, composed differently per channel. A brand running both trades gets one comparable execution dataset — instead of two systems whose numbers can't be reconciled.
The model is built for varied product formats — sachets, pouches, loose stock, local packs, and full-size SKUs — on shelves where products are stacked, partially covered, poorly lit, or mixed with competitor and unbranded packaging.
Sachets are the hard case for a reason: they're small, they hang in strips rather than facing forward, and they crumple. And in many categories they are the volume driver in general trade. A recognition model that can't see them can't measure the part of the business that matters most in this channel.
General trade routes run through exactly the areas with patchy, low-bandwidth coverage, so an IR tool that requires a live round trip to a server is unusable across most of the outlets that matter.
This is an architecture consequence, not a feature toggle. Processing at the device is what makes a sub-3-second result possible and what lets capture continue with no signal — and a scan recorded offline enters the same pipeline as one recorded online.
At general trade scale this is a business-model question, not a convenience one. Issuing and maintaining hardware across thousands of reps covering hundreds of thousands of outlets is a cost and logistics programme in itself — and it's the reason most hardware-dependent execution tools never reach the long tail.
Running on the phone in the rep's pocket is what makes universal coverage operationally possible.
Show Us a Better IR for General Trade. Win 1 Million $
Get a Big 4 firm to audit your platform against ours. Score higher on capability. We cut the cheque.
Open to any competing AI Native IR GT provider. Provider must be ready for an audit by a Big 4 firm using a scorecard agreed with SalesCode.ai.
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