Generative AI development and automation, NLP & computer vision
Generative AI development
Examples of projects:
Auto-classifying shopper complaints across retail channels:
- Built a transformer-based text classification engine to tag and route shopper complaints by theme, severity, and urgency.
- Integrated POS, loyalty, and campaign data to trace complaint origins to specific stores, SKUs, or marketing triggers.
- Routed flagged issues to QA, CX, or field teams with escalation rules based on historical brand impact.
On-shelf availability monitoring using computer vision:
- Trained object detection models on thousands of shelf images across banners and regions.
- Used YOLOv7 for rapid inference and SKU-level detection, identifying gaps, planogram compliance, and misplaced facings.
- Enabled mobile app capture and API ingestion of retailer-supplied footage for continuous tracking.
Automated email parsing for trade promotion claims:
- Built an NLP engine to extract structured data from thousands of unformatted retailer claim emails.
- Parsed sender metadata, claim descriptions, and attachments into structured trade claim dashboards.
- Triggered rule-based workflows for approval, escalation, and deduction reconciliation.
Value we have created
93% accuracy in complaint classification
Enabled the CX team to auto-tag and route over 120,000 complaints per quarter without manual review.
+19% faster deduction resolution
NLP-powered claim parsing reduced investigation time and manual handling, speeding up the closure cycle.
-28% field audit costs
Shelf monitoring automation allowed a reduction in manual in-store audits across 1,400 locations.
+11% planogram compliance in tier 1 stores
The CV system identified compliance gaps early, leading to improved execution and a higher share of shelf.
2x faster brand risk response time
Early flagging of negative themes in complaints helped PR and QA teams act before escalation.
Why our automation & AI is scientifically better
1
Fine-tuned NLP models for retail language:
- Our text AI models are trained on retailer-specific terminology, shopper language, and brand feedback patterns.
- They can distinguish between sentiment, complaint intent, and rhetorical queries — something generic LLMs often miss.
2
Robust computer vision for real-world conditions:
- We train CV models on in-store conditions — glare, occlusion, poor angles — using advanced augmentation and ensemble methods.
- Our models handle branded variants, seasonal packs, and damaged packaging without compromising accuracy.
3
End-to-end workflow automation, not just models:
- We don’t stop at classification — our systems trigger workflows in your trade, QA, CX, or finance platforms.
- That means real-world action: fewer escalations, cleaner deductions, and faster response to retail partners.
4
Designed for data scarcity in mid-market brands:
- We use transfer learning and data-efficient training techniques to deploy high-performing models even with limited labeled data.
- Brands without large annotated datasets can still deploy effective, production-grade automation solutions.
5
Compliance-first design with auditability:
- Every AI decision is logged, versioned, and traceable — no hidden logic.
- You get a full audit trail for regulatory, financial, and brand protection use cases, all aligned with your internal risk protocols.
Automate tasks using AI agents
Examples of projects
Agent classifier shopper complaints across retail channels:
- Built a transformer-based text classification engine to tag and route shopper complaints by theme, severity, and urgency.
- Integrated POS, loyalty, and campaign data to trace complaint origins to specific stores, SKUs, or marketing triggers.
- Routed flagged issues to QA, CX, or field teams with escalation rules based on historical brand impact.
On-shelf availability monitoring using agentic computer vision:
- Trained object detection models on thousands of shelf images across banners and regions.
- Used YOLOv7 for rapid inference and SKU-level detection, identifying gaps, planogram compliance, and misplaced facings.
- Enabled mobile app capture and API ingestion of retailer-supplied footage for continuous tracking.
Automated email parsing for trade promotion claims:
- Built an NLP engine to extract structured data from thousands of unformatted retailer claim emails.
- Parsed sender metadata, claim descriptions, and attachments into structured trade claim dashboards.
- Triggered rule-based workflows for approval, escalation, and deduction reconciliation.
Value we have created
93% accuracy in complaint classification
Enabled the CX team to auto-tag and route over 120,000 complaints per quarter without manual review.
+19% faster deduction resolution
NLP-powered claim parsing reduced investigation time and manual handling, speeding up the closure cycle.
-28% field audit costs
Shelf monitoring automation allowed a reduction in manual in-store audits across 1,400 locations.
+11% planogram compliance in tier 1 stores
The CV system identified compliance gaps early, leading to improved execution and a higher share of shelf.
2x faster brand risk response time
Early flagging of negative themes in complaints helped PR and QA teams act before escalation.
Why our automation & AI is scientifically better
Fine-tuned NLP models for retail language:
- Our text AI models are trained on retailer-specific terminology, shopper language, and brand feedback patterns.
- They can distinguish between sentiment, complaint intent, and rhetorical queries — something generic LLMs often miss.
Robust computer vision for real-world conditions:
- We train CV models on in-store conditions — glare, occlusion, poor angles — using advanced augmentation and ensemble methods.
- Our models handle branded variants, seasonal packs, and damaged packaging without compromising accuracy.
End-to-end workflow automation, not just models:
- We don’t stop at classification — our systems trigger workflows in your trade, QA, CX, or finance platforms.
- That means real-world action: fewer escalations, cleaner deductions, and faster response to retail partners.
Designed for data scarcity in mid-market brands:
- We use transfer learning and data-efficient training techniques to deploy high-performing models even with limited labeled data.
- Brands without large annotated datasets can still deploy effective, production-grade automation solutions.
Compliance-first design with auditability:
- Every AI decision is logged, versioned, and traceable — no hidden logic.
- You get a full audit trail for regulatory, financial, and brand protection use cases, all aligned with your internal risk protocols.
Get in touch
Our friendly and efficient team is here to discuss your ideas. No pressure, just solutions.
Contact us
What happens next?
- Our team will reach out to you to schedule a 'no pressure' call to help understand your objectives
- We'll provide relevant demos and examples. You then confirm to us that you have a formal mandate to purchase.
- We will provide the best-in-class proposition, tailored to all your nuances.
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