CALIPERS
BFSI

How a Tier-1 Bank Scaled Support with GenAI

A Tier-1 Private Sector Bank (India)

Call center and digital support could not keep up with product growth. NPS was flat while volume doubled.

40%

Handle time reduction

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Manufacturing

Manufacturing Giant Achieves 60% Downtime Reduction

Global Manufacturing Conglomerate

Unplanned downtime exceeded 400 hours annually across 12 plants, eroding margin and OTIF performance.

60%

Downtime reduction

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E-commerce

E-Commerce Platform 3x Revenue with AI Recommendations

Leading E-Commerce Marketplace

Generic recommendations drove low AOV; churn pressure on margins despite healthy traffic.

3x

Revenue lift

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Healthcare

Clinical ops dashboards in half the time

A leading multi-hospital network

Clinical teams needed faster operational insight without slowing day-to-day care.

40%

Faster insight cycles

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SaaS

5x faster model deployment

High-growth SaaS unicorn

Model releases were manual; teams could not iterate quickly enough.

45%

Cloud cost reduction

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Featured studies

How a Tier-1 Bank Scaled Support with GenAI

A Tier-1 Private Sector Bank (India)

Call center and digital support could not keep up with product growth. NPS was flat while volume doubled.

Retrieval-grounded assistants with human handoff, integrated to core banking and CRM, with full eval and release discipline.

  1. Week 1-2: Content and policy corpora, red-team scenarios, design review
  2. Week 3-4: RAG stack, guardrails, and traceability in staging
  3. Week 5-6: Pilots on two product lines with quality gates
  4. Week 7-8: Phased production with model and prompt version control
  5. Week 9-12: Tuning, analytics, and self-serve content updates
handleTime
40% faster
resolution
2.1× more FCR on digital
cost
30% lower cost per contact

Manufacturing Giant Achieves 60% Downtime Reduction

Global Manufacturing Conglomerate

Unplanned downtime exceeded 400 hours annually across 12 plants, eroding margin and OTIF performance.

Predictive maintenance models on vibration and thermal telemetry, unified MLOps, and edge inference.

  1. Month 1: Sensor harmonization and historian integration
  2. Month 2: Baseline failure signatures and labeling
  3. Month 3: Pilot on two lines with shadow scoring
  4. Month 4-5: Rollout with maintenance workflow hooks
  5. Month 6: Continuous learning and SLA reporting
downtimeReduction
60%
defectDetection
99.1%
energySavings
25%

E-Commerce Platform 3x Revenue with AI Recommendations

Leading E-Commerce Marketplace

Generic recommendations drove low AOV; churn pressure on margins despite healthy traffic.

Real-time personalization stack with demand forecasting and inventory-aware ranking.

  1. Phase 1: Event stream unification and feature store
  2. Phase 2: Ranking experiments with guardrails
  3. Phase 3: Online learning with human-in-the-loop review
  4. Phase 4: Global rollout with latency SLOs
revenueLift
3x from AI recommendations
cartAbandonment
50% reduction
conversionLift
18% checkout conversion

Clinical ops dashboards in half the time

A leading multi-hospital network

Clinical teams needed faster operational insight without slowing day-to-day care.

Unified analytics layer across EHR, staffing, and throughput signals with role-based views and near-real-time refreshes—without pulling clinicians into BI tooling.

  1. Month 1: Data governance, PHI-safe aggregates, and metric definitions with clinical sponsors
  2. Month 2: Executive and ward-level dashboards with drill-down guardrails
  3. Month 3: Alerts for census pressure and SLA breaches tied to escalation paths
  4. Month 4+: Adoption analytics, feedback loops, and iterative KPI refinement
insightCycles
40% faster cycles
reportingHours
62% less analyst rework
adoption
Leadership review weekly → daily

5x faster model deployment

High-growth SaaS unicorn

Model releases were manual; teams could not iterate quickly enough.

Git-backed ML pipelines, automated evaluation gates, and progressive rollout with feature flags—aligned to product releases instead of ad-hoc notebook pushes.

  1. Quarter 1: Standardized environments, artifact registry, and CI/CD for training jobs
  2. Quarter 2: Shadow deployments, canaries, and rollback hooks in the inference tier
  3. Quarter 3: Cost telemetry per tenant and autoscaling tuned to SLOs
  4. Quarter 4: Self-serve retraining templates owned by squads
deploymentFrequency
5× faster releases
cloudCost
45% infra savings
incidents
Major Sev-1 cut by half