Team experience
Enterprise AI case studies
Work our team has delivered across industries. All cases are anonymized for client confidentiality. Metrics are verified and come from real deployments.
Document intelligence
Insurance claims automation
Problem: Claims spread across 9 document types, 2-3 days per case with a 15% error rate.
Built: Intelligent document processing that extracts, validates, and routes every claim, with a human in the loop on low-confidence items.
Result: 98% of claims automated, 40+ FTEs redeployed, seconds per claim.
Document processing at scale
Problem: A financial-services firm processing Arabic bank statements by hand could clear only 200 documents a month.
Built: An end-to-end document data extraction pipeline tuned for messy, multi-format financial documents.
Result: 200 to 4,000 documents per month in six months.
Retail and CPG
Promo budget optimization
Problem: A consumer-goods team planned promotions by hand, constrained by discount policy and unable to balance revenue, profit and budget together.
Built: A constrained-optimization engine that takes sales, prices, promo calendars and price elasticity and compares manual plans against optimized ones.
Result: The optimized plan matched manual revenue at 12% lower promo budget and 9% higher profit on the demonstrated set.
Trade promo sell-out forecasting
Problem: Promo negotiations needed to know how sell-out depends on discount depth, facings and secondary equipment.
Built: Sell-out prediction models that score quarter-level volume from product type, promo support, facings and equipment placement.
Result: Sell-out forecasts that sales teams use directly in trade-promo negotiations.
Shopping mission discovery
Problem: Category-level analytics misses the actual purpose behind a shopping trip.
Built: A clustering pipeline that turns transaction baskets into shopping missions and mission profiles per store.
Result: Mission segments that drive product bundles, store profiles and planogram decisions.
SKU rating and assortment priority
Problem: Large product matrices give no clear way to prioritize items by business value rather than raw sales.
Built: A rating engine that scores each SKU on store value and traffic-driving role using demand, price and mission features.
Result: Assortment ranked by contribution and traffic role, with keep, forecast and reposition recommendations.
Lost-sales and out-of-stock detection
Problem: A sales gap can mean no demand, or a hidden out-of-stock that is quietly losing revenue.
Built: A detector that compares sales and stock dynamics by product, category and store to flag likely out-of-stock periods and estimate lost sales.
Result: Around 11% lost sales among actively selling items, surfaced from ordinary retail data with no shelf cameras.
Pricing AI
Price elasticity and cannibalization
Problem: Pricing decisions need own-price elasticity, cross-elasticity and cannibalization together, not one in isolation.
Built: A model factory for SKU-level elasticity, characteristic groups, product roles and what-if price simulation.
Result: A pricing tool that shows own and cross effects and optimizes for margin and revenue under constraints.
Procurement
Procurement and tender AI
Problem: An industrial equipment manufacturer reviewed tenders by hand, 3 days per tender across hundreds of line items.
Built: A procurement AI that parses tender packs, matches them to the product catalog, and filters the noise automatically.
Result: 3 days to 15 minutes, 946 records at about 90% accuracy, 70% auto-filtered.
Supplier segmentation and spend visibility
Problem: Procurement and category managers could not separate strategic, critical, operational and transactional suppliers.
Built: A spend-analytics dashboard with a supplier segmentation matrix and category and supplier filters.
Result: A clear supplier strategy view built from spend data, used by category managers.
NLP automation
NLP incident classification
Problem: A delivery operation received many free-text requests across channels, and manual triage was slow and inconsistent.
Built: An NLP classifier that labels incident type and extracts the key fields from each request.
Result: 91% incident-type classification accuracy, routing requests into a structured queue.
Industrial NLP
Digital twins from technical documents
Problem: Reliable equipment definitions for digital twins often exist only inside dense technical documentation.
Built: An NLP pipeline that extracts equipment, links and process objects from technical documents and assembles an asset graph.
Result: A pilot digital twin built in 8 weeks at 97.3% document parsing accuracy.
Industrial CV
Computer vision for scrap quality
Problem: Manual scrap inspection is subjective and slow, and contamination, class and hidden risks are hard to assess objectively.
Built: A hardware-and-software acceptance system using cameras, weights and computer vision to score contamination, class and density and to integrate with the plant systems.
Result: Objective inspection of incoming scrap with human bias and fraud removed from acceptance.
Computer vision rod counting on a rolling line
Problem: Counting steel rods on a fast rolling line by eye is error-prone, and batch separation makes it harder.
Built: A vision model that selects sharp frames, detects rods and estimates count across frame intervals, with an honest readout of where accuracy drops.
Result: On the demo run the model counted 709 against an actual 717, and the project showed exactly how camera angle, line speed and blur move accuracy.
Data governance
Data quality command center
Problem: Data governance in a large bank stays a policy exercise until ownership, quality checks and tooling are operationalized.
Built: A data governance and data quality function with a business glossary, catalog, owners, lineage and automated checks.
Result: Around 5,000 business data elements catalogued and 34 data-quality checks running in production.
XBRL enterprise KPI model
Problem: Large enterprises need governed KPI models with dimensions, validation rules and versioning, not spreadsheets.
Built: An XBRL-based hierarchical KPI and data model with functional and technical requirements, validation rules and a tool roadmap.
Result: A governed KPI model with dimensions and validation that reporting flows can rely on.
Material master data and MDM
Problem: Industrial groups carry inconsistent material master data with duplicated classes and weak classification.
Built: An MDM and master-data approach covering survey, classification, normalization and a migration concept with templates.
Result: A deduplicated, reclassified material master that is ready to migrate.
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