Real deployments. Real outcomes.
Across retail, insurance, legal, healthcare, agriculture, pharma, and logistics, FEME agents and models deliver measurable impact in production — not pilots that never ship.
lower support cost
National retail group
Cutting customer support cost by 60%
A national retailer deployed FEME customer-service agents across chat and voice, automating tier-1 support and freeing human agents for complex cases.
Challenge
Seasonal volume spikes and fragmented chat, voice, and email channels drove up cost-to-serve while response times slipped during peak demand.
Approach
An omnichannel agent integrated with the CRM and ticketing stack, grounded in the knowledge base via RAG, with sentiment-aware routing and seamless live-agent handoff.
Results
- Deflected 72% of tier-1 tickets
- 3× faster average handle time
- Maintained 4.6/5 customer satisfaction
- 24×7 coverage across chat & voice
lower fraud losses
Multi-line insurer
Reducing fraud losses by 35%
A multi-line insurer used FEME fraud detection to score every claim in real time, surfacing organized-fraud networks investigators had been missing.
Challenge
Organized fraud rings were slipping past manual review, while genuine policyholders waited on slow, blanket investigations.
Approach
Real-time risk scoring on every claim with behavioral anomaly detection and graph-based network analysis, surfaced to investigators through a prioritized workbench.
Results
- Real-time risk scoring on 100% of claims
- Uncovered 4 organized fraud rings
- Sped legitimate payouts by 40%
- <1s risk score per claim
faster review
Corporate legal team
Reducing contract review time by 85%
A corporate legal team automated first-pass contract review with FEME, flagging risk clauses and missing terms with citations before counsel review.
Challenge
A growing contract backlog and rising outside-counsel spend left in-house counsel reviewing routine agreements line by line.
Approach
A multi-agent legal orchestrator routes each contract across research, drafting, clause-analysis, compliance, and risk agents — returning cited, explainable first-pass redlines.
Results
- First-pass review in minutes, not days
- 100% clause coverage on every contract
- Lowered outside-counsel spend by 40%
- Citations on every flagged clause
model-evolution stages
OptiSense AI
A model-evolution story: from a retinopathy classifier to an oculomics platform
How OptiSense AI matured from a naïve image classifier into an explainable, sensitivity-tuned diabetic-retinopathy grader — and onward toward oculomics, reading the retina as a window into systemic health. A textbook study in feature engineering and model evolution.
Challenge
A shortage of ophthalmologists meant referable diabetic retinopathy was caught late in high-volume, underserved clinics — and a naïve model scored on plain accuracy looked deceptively strong while missing the patients who mattered.
Approach
Rather than chase a bigger model, the system evolved in deliberate stages: re-engineer the features and metric, then the architecture, then trust, then the platform — each step measured against what actually matters in screening (sensitivity, not accuracy).
Results
- Switched to Quadratic Weighted Kappa + sensitivity — metrics that reward catching sick patients
- EfficientNet-B3 with an ordinal head aligned to the 0–4 clinical scale
- Grad-CAM explainability + referable decision on every read
- Same foundation extended toward systemic (oculomics) biomarkers
Model evolution
- v0
Naïve baseline
An off-the-shelf CNN on raw fundus images, scored on plain accuracy — which looked high only because most images are healthy.
- v1
Feature engineering
Retina-crop + Ben-Graham colour normalisation, and a switch to Quadratic Weighted Kappa and sensitivity — metrics that actually reward catching at-risk patients.
- v2
Architecture evolution
EfficientNet-B3 transfer learning with an ordinal head that respects the 0–4 grade order, LR warmup and test-time augmentation — lifting QWK and steadying predictions.
- v3
Trust & explainability
Grad-CAM attention overlays and a referable-vs-not decision, keeping a clinician in the loop on every read.
- v4
Platform evolution → oculomics
The same imaging-and-deep-learning foundation extended toward oculomics — reading the retina as a window into systemic, cardiometabolic and neurological risk.
hallucination rate
Agri-advisory platform
A grounded AI advisor that won’t hallucinate
A retrieval-grounded agronomy advisor answers farmer questions with cited guidance, fusing live weather, satellite imagery, and plant-disease vision — and runs even offline.
Challenge
Generic chatbots gave confident but wrong agronomy advice, with no citations and no support for regional languages or low-connectivity field conditions.
Approach
A RAG pipeline (embeddings → vector store → re-ranking → grounded generation) answers only from a curated knowledge base with citations, fused with NASA satellite NDVI, live weather, and CNN plant-disease vision — multilingual and offline-first by default.
Results
- ≥95% context faithfulness, <5% hallucination
- ≤3s response latency (RAGAS-style eval)
- Plant-disease & pest vision (PlantVillage / IP102)
- Runs fully offline with no API keys
faster audit prep
Life-sciences manufacturer
Audit-ready answers across thousands of SOPs
A conversational GxP service desk answers SOP and compliance questions with version-pinned citations and a complete audit trail — built for regulated environments.
Challenge
SOP sprawl across quality systems made it slow and risky to confirm the current, correct procedure — and audit preparation consumed weeks of specialist time.
Approach
A compliance agent retrieves only from version-pinned source documents, cites the exact controlled version, validates SOPs, flags deviations, and logs every interaction to an immutable audit trail.
Results
- Version-pinned citation on every answer
- Automated deviation detection & CAPA drafting
- Complete, exportable audit trail
- 50% faster audit preparation
lower logistics spend
Freight & supply-chain operator
Catching shipment & invoice fraud automatically
A logistics-integrity agent monitors shipments and invoices, automatically flagging GPS anomalies, duplicate claims, and vendor fraud across a multi-party supply chain.
Challenge
Invoice and route fraud were hard to spot across thousands of shipments and vendors, with limited visibility and manual spot-checks missing duplicate claims.
Approach
Continuous monitoring analyzes GPS and route data, cross-checks invoices for duplicates and arithmetic errors, and verifies vendors — raising exception alerts only on genuine anomalies.
Results
- Automated anomaly alerts across all routes
- Caught duplicate-claim fraud at scale
- Vendor verification on every invoice
- Reduced logistics spend by 15%
Enterprise Customers
AI Experts
Annual Revenue
Global Hubs
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