The 5 Failure ModesAnd How We Fix Them
The industry has a deployment problem, not an algorithm problem. Here are the systemic failures we have solved across 150+ deployments.
Data Readiness Crisis
Failure Mode #1
Siloed data, inconsistent quality, insufficient volume, missing documentation
Week 1 data audit, pipeline establishment, feature stores for existing infrastructure
Integration Nightmares
Failure Mode #2
Models built in isolation cannot connect to production systems, IT blocks deployments
Production architecture from day 1, integration layers, IT collaboration throughout
ROI Demonstration Gap
Failure Mode #3
POCs show technical feasibility but cannot quantify business impact
KPI-driven from discovery, business impact tracking, real-time ROI dashboards
Skills & Alignment Gap
Failure Mode #4
Data scientists build models, engineers cannot maintain them, business teams do not understand AI
Full knowledge transfer, comprehensive documentation, build-operate-transfer approach
Missing MLOps Infrastructure
Failure Mode #5
No versioning, monitoring, or retraining. Models drift silently. Compliance cannot audit.
Complete MLOps: versioning, monitoring, automated retraining, explainability, audit trails
We Fix These Before They Happen
150+ models deployed to production. 90%+ success rate. Because we treat deployment as the starting constraint, not an afterthought.
Deployment-First Engineering3 Non-Negotiable Principles
These principles have delivered 150+ models to production with 90%+ success rate. They are not optional.
Production-Ready from Day 1
No throwaway POC code
Every line is production-grade. Infrastructure-as-Code, Docker containers, CI/CD from the first commit.
Your Infrastructure, Your Control
On-premise eliminates bottlenecks
Deploy on your servers, VPC, or edge devices. Zero data governance delays. Complete IP ownership.
Business Metrics First
KPI-driven from discovery
Define success criteria before training the first model. Revenue impact, cost savings, time reduction—measured throughout.
How They Work Together
Production code deploys immediately
On-premise eliminates delays
Business metrics validate every decision
Result: Models that deploy in 16 weeks, meet business goals, and stay within your infrastructure.
8 ML Capability Clusters150+ Models Deployed
Production-proven capabilities across the ML spectrum. Every capability includes pre-built accelerators and deployment templates.
| Capability | Use Cases | Technologies | Deployed |
|---|---|---|---|
Predictive Analytics | Demand forecastingChurn predictionRisk scoring | XGBoostLSTMProphet | 45+ |
Computer Vision | Defect detectionMedical imagingDocument OCR | CNNsYOLOResNet | 32+ |
NLP & LLMs | Sentiment analysisDocument classificationQ&A systems | BERTGPTTransformers | 28+ |
Recommendation Systems | Product recommendationsContent personalizationNext-best-action | Collaborative filteringDeep LearningA/B testing | 18+ |
Anomaly Detection | Fraud detectionQuality controlCybersecurity | Isolation ForestAutoencodersGNNs | 15+ |
Optimization & Planning | Supply chainResource allocationRoute optimization | Linear programmingGenetic algorithmsReinforcement learning | 12+ |
40+ use cases across 6 capability clusters. 50+ technologies production-tested.
Domain-Specific Solutions4 Industries, 150+ Deployments
Not generic ML. Industry-specific models trained on domain challenges with proven ROI.
16-Week Deployment PathDiscovery to Production
Structured 5-phase methodology. Go/no-go gates at every phase. Production-ready code from day 1.
Discovery & Assessment
Go/no-go decision with clear ROI path
POC Development
Target accuracy met, technical feasibility proven
Production Engineering
Passes IT security review, ready for deployment
Deployment & Launch
Live in production, monitoring active
Monitoring & Evolution
Model maintains performance, ROI realized
Average Total Timeline: 16 Weeks
From discovery to production deployment. Fastest in industry for enterprise ML. Go/no-go gates at every phase ensure no wasted investment.
Best of Breed, Your InfrastructureOpen Source + Enterprise Tools
Technology-agnostic. We select the right tool for your use case, data, and infrastructure. No vendor lock-in.
Data Layer
ML Frameworks
MLOps
Deployment
Monitoring
Open Source First
No vendor lock-in. You own the code and models.
Cloud-Agnostic
AWS, Azure, GCP, or on-premise. Your choice.
Production-Proven
150+ deployments. Every tool battle-tested.
Deploy in Weeks, Not Months6 Production-Ready Solutions
Pre-built ML solutions customized to your data. 50-70% faster deployment than building from scratch.
Demand Forecasting Engine
Time-series forecasting with 95%+ accuracy for retail and manufacturing
Visual QA Inspector
Computer vision for defect detection and quality control
Fraud Detection Suite
Real-time anomaly detection for financial services
Recommendation Engine
Personalized product and content recommendations
Predictive Maintenance
IoT sensor analysis for equipment failure prediction
Dynamic Pricing Optimizer
Real-time pricing based on demand, competition, inventory
Custom-Built to Your Data
Every accelerator is customized to your data, infrastructure, and business logic. Not generic templates.
Real Results, Not Projections$27M+ Value Created
These are not hypothetical ROI projections. These are audited, production outcomes from deployed models.
Global Manufacturer
8-10% defect rate causing $10-12M annual losses
Computer vision quality inspection with edge deployment
Model deployed and actively generating business value. Performance monitored 24/7 with automated retraining.
Healthcare Provider
71% diagnostic accuracy, delayed treatment decisions
Medical imaging AI with HIPAA-compliant MLOps platform
Model deployed and actively generating business value. Performance monitored 24/7 with automated retraining.
Retail Chain
High stockouts, excess inventory, lost sales
Demand forecasting and inventory optimization
Model deployed and actively generating business value. Performance monitored 24/7 with automated retraining.
Cumulative Value Created
Across 150+ deployments. Audited results from production systems.
RisiCare vs Traditional ConsultingNot Even Close
Side-by-side comparison of deployment-first engineering vs traditional ML consulting.
| Category | RisiCare | Traditional |
|---|---|---|
| Code Quality | Production-grade from day 1 | POC code, rewrite for production |
| Timeline | 16 weeks average to production | 12-24 months (if it deploys) |
| Deployment Success | 90%+ success rate | 13% success rate (industry avg) |
| Data Governance | On-premise, zero delays | 6-12 month legal reviews |
| ROI Tracking | KPI-driven from discovery | Metrics added as afterthought |
| MLOps Platform | Included: versioning, monitoring, retraining | Not included, build separately |
| Knowledge Transfer | Full transfer, comprehensive docs | Minimal, dependency on consultants |
| IP Ownership | You own everything | Shared or vendor-owned |
The RisiCare Difference
We have deployed 150+ models to production because we fix the 5 failure modes before they happen. Not after.
Three Entry PointsStart Where You Are
Whether you are exploring feasibility or ready to deploy, we have a path that fits your timeline and risk tolerance.
Free Assessment
Start here if unsure
- Data readiness audit
- Use case evaluation
- ROI projection
- Go/no-go recommendation
Exploring feasibility, no commitment
Proof of Concept
Validate before committing
- Production-grade POC
- Performance benchmarks
- Technical architecture
- Full deployment roadmap
De-risk investment, prove technical feasibility
Full Platform Build
End-to-end deployment
- Production ML platform
- MLOps infrastructure
- Model deployment
- Knowledge transfer
Committed to deployment, need full solution
Progressive engagement: Start small, scale when validated. Zero lock-in.
Talk to ML engineers. Not salespeople.
15-minute technical consultation. Honest assessment. Zero commitment.
Prefer to talk directly?
Schedule a 15-minute intro call with our engineering team to discuss your private AI requirements.
Talk to an EngineerResponse Time
Within 24 hours
We typically respond within 24 hours on business days