Case study details — problem, solution, and measured results
Case 01: E-Commerce Returns & Refund Triage
AI-powered visual inspection and policy matching for automated return processing.
Problem: Customer support teams manually review every return request — photos, receipts, policy matching. For a mid-size store doing 500+ returns/week, that's 3 full-time staff doing repetitive pattern recognition a machine handles in seconds.
Solution: Vision LLM scans product photos for damage classification + policy engine auto-approves, flags, or escalates. Integrates with Shopify, Gorgias, and warehouse APIs.
Measured results: 87% Auto-resolved; <4sec Avg response; 3x Staff efficiency; $180K Annual savings.
Before: 3 full-time staff reviewing photos manually; 15-minute average handling time per request; 4% misclassification rate; Customer wait: 24-48 hours.
After: 87% fully automated resolution; Sub-4-second processing; 0.3% error rate; Instant customer response.
Technology: Vision LLM, Shopify API, Gorgias, Warehouse API, Policy Engine, Damage Classifier.
Case 02: Invoice & Contract Data Extraction
Multimodal LLM reads PDFs, scanned images, and emails for automated data entry.
Problem: Finance teams manually key data from invoices, POs, and contracts into ERPs. Each document takes 8-15 minutes. A company processing 2,000 documents/month burns 400+ hours on copy-paste work riddled with a 4% human error rate.
Solution: Multimodal LLM reads PDFs, scanned images, and emails — extracts line items, dates, amounts, and clauses into structured JSON. Auto-reconciles against existing records and pushes to ERP.
Measured results: 96% Accuracy rate; 12x Faster processing; 400hr Monthly hours saved; 0.4% Error rate.
Before: 8-15 min per document manually; 4% human error rate; 400+ hours/month on data entry; Bottleneck for month-end close.
After: Under 30 seconds per document; 0.4% error rate; < 20 hours/month oversight; Real-time processing.
Technology: Multimodal LLM, OCR Pipeline, PDF Parser, ERP Integration, JSON Schema, Reconciliation Engine.
Case 03: Inbound Lead Qualification & Routing
AI-powered lead scoring, enrichment, and intelligent routing to sales teams.
Problem: SDRs spend 60% of their day qualifying leads that go nowhere. They manually research companies, cross-check CRM data, draft outreach, and decide routing — all before a single conversation.
Solution: AI agent enriches every inbound lead with firmographic + intent data, scores fit, drafts personalized first-touch, and routes to the right rep — or auto-nurtures low-intent leads via sequenced emails.
Measured results: 3.2x Pipeline velocity; 41% More meetings booked; 60% SDR time recovered; 2.1x Conversion lift.
Before: 60% of SDR time on manual research; Hot leads go cold in queue; Inconsistent qualification criteria; Generic first-touch emails.
After: Real-time enrichment & scoring; Sub-minute lead routing; AI-drafted personalized outreach; 41% more meetings booked.
Technology: Lead Scoring ML, CRM Integration, Email Sequencer, Firmographic API, Intent Signals, NLP Drafting.
Case 04: Customer Support Ticket Routing & Resolution
Intelligent ticket classification, auto-response drafting, and smart escalation.
Problem: L1 agents waste 40% of their shift reading, categorizing, and routing tickets to the right team. Mis-routes cause 2-3 day delays. Customers churn before anyone with the right expertise even sees the ticket.
Solution: LLM reads incoming tickets, classifies intent + urgency + product area, auto-drafts a response for L1, and routes complex issues to specialists with full context summary attached.
Measured results: 73% Tickets auto-handled; 58% Faster resolution; 92% Routing accuracy; 4.6/5 CSAT score.
Before: 40% agent time on triage; Mis-routes cause 2-3 day delays; No context passed to specialists; Customer churn from slow response.
After: 73% tickets auto-resolved; 58% faster resolution time; Full context summaries for specialists; 4.6/5 customer satisfaction.
Technology: LLM Classifier, Zendesk API, Intercom, Sentiment Analysis, Context Summarizer, Priority Engine.
Case 05: Inventory Demand Forecasting
ML-powered demand prediction with automated purchase order generation.
Problem: Retail and DTC brands rely on spreadsheets and gut feel for purchase orders. Overstock ties up capital; stockouts lose revenue. A single SKU mis-forecast can cost $50K+ in dead inventory or missed sales.
Solution: ML model ingests historical sales, seasonality, marketing calendar, and external signals (weather, trends) to generate SKU-level demand forecasts. Auto-generates POs and alerts for reorder points.
Measured results: 34% Less dead stock; 91% Forecast accuracy; $50K+ Per-SKU savings; 2.8x Inventory turnover.
Before: Spreadsheet-based forecasting; $50K+ per SKU mis-forecast; Monthly manual PO creation; Stockouts during peak demand.
After: 91% forecast accuracy; 34% reduction in dead stock; Automated purchase orders; Real-time reorder alerts.
Technology: Time Series ML, Shopify API, Weather API, Trend Signals, PO Automation, Inventory Sync.
Case 06: Employee Onboarding & Knowledge Base
RAG-powered internal chatbot for instant, sourced answers from company knowledge.
Problem: New hires take 3-6 months to ramp. They ping senior staff with the same questions, search scattered Notion/Confluence/Slack for answers, and still miss critical tribal knowledge buried in threads no one bookmarked.
Solution: RAG-powered internal chatbot indexes all company docs, SOPs, Slack history, and recorded meetings. New hires ask questions in natural language and get sourced answers instantly — no senior interruptions needed.
Measured results: 65% Faster ramp-up; 80% Less senior pings; 95% Answer accuracy; 3K+ Docs indexed.
Before: 3-6 month ramp-up period; Senior staff interrupted constantly; Tribal knowledge lost in Slack threads; Scattered documentation.
After: 65% faster onboarding; 80% fewer senior interruptions; Instant sourced answers; Living knowledge base.
Technology: RAG Pipeline, Vector DB, Slack Integration, Notion API, Meeting Transcripts, Citation Engine.
Case 07: Compliance & Regulatory Monitoring
AI agent that tracks regulatory changes and auto-creates remediation tasks.
Problem: Legal and compliance teams manually track regulatory changes across jurisdictions — reading government gazettes, cross-referencing internal policies, and flagging required updates. Missing one change can trigger six-figure fines.
Solution: LLM agent monitors regulatory feeds, compares new rules against your policy corpus, generates impact summaries with severity scores, and auto-creates remediation tasks assigned to the right team.
Measured results: 24hr Detection speed; 0 Missed regulations; 85% Auto-triaged; 6-fig Fines prevented.
Before: Manual gazette reading; Cross-referencing internal policies; Risk of six-figure fines; Weeks to identify changes.
After: 24-hour detection; Zero missed regulations; Auto-generated remediation tasks; Severity-scored impact summaries.
Technology: LLM Agent, Regulatory Feeds, Policy Corpus, Severity Scoring, Task Automation, Multi-jurisdiction.
Case 08: Content Production at Scale
AI pipeline from topic input to multi-channel content in hours, not days.
Problem: Marketing teams need 50-100 pieces of content per month across blog, social, email, and ads. Each piece requires research, drafting, editing, SEO optimization, and asset creation — a bottleneck that kills campaign velocity.
Solution: AI pipeline generates research briefs, first drafts, SEO meta, social variants, and email versions from a single topic input. Human editors review and approve — cutting production time from days to hours per piece.
Measured results: 8x Content output; 70% Cost reduction; 100+ Pieces/month; 4hr Per piece avg.
Before: Days per content piece; 10-20 pieces/month max; Manual SEO optimization; Channel-by-channel creation.
After: 4 hours per piece average; 100+ pieces/month; Auto-optimized for SEO; All channels from one input.
Technology: Content LLM, SEO Engine, Brand Voice Model, Multi-channel Gen, Asset Pipeline, Editorial Workflow.
Case 09: QA & Visual Defect Detection
Computer vision inspects every item in real-time with zero fatigue.
Problem: Manufacturing and fulfillment lines rely on human inspectors scanning thousands of items per shift. Fatigue sets in after 2 hours and defect detection rates drop 30%. Defective products ship, returns spike, and brand reputation takes the hit.
Solution: Computer vision model trained on your product line inspects every item via camera feed in real-time. Flags defects with bounding boxes, auto-rejects, and logs patterns for upstream root-cause analysis.
Measured results: 99.2% Detection rate; 24/7 No fatigue; 0.1sec Per-item scan; 45% Fewer returns.
Before: Human fatigue after 2 hours; 30% drop in detection rate; Defective products ship; Manual root-cause analysis.
After: 99.2% detection rate 24/7; 0.1 sec per item inspection; Auto-rejection on line; 45% fewer customer returns.
Technology: Computer Vision, Camera API, Defect Classifier, Bounding Box Detection, Root Cause Analytics, Line Integration.
Results are measured outcomes from CRE8TOR client engagements; contact enquiry@cre8tor.work for engagement-specific references.
