We are proud to be an official partner of Anthropic, the company behind Claude.
MLOps & ModelOps
End-to-end ML lifecycle: reproducible training, CI/CD, automated monitoring, drift detection, and retraining workflows to keep models reliable in production.
4
Deliverables
3
Outcomes
SLA
Production Ready
Production-grade ML operations that reduce incidents and accelerate releases.
Production-grade ML operations that reduce incidents and accelerate releases. We build reproducible training, CI/CD, monitoring, and retraining workflows that keep models reliable in production.
What you get
Production-grade ML operations that reduce incidents and accelerate releases.
CI/CD for models
Experiment tracking
Monitoring + alerting
Automated retraining pipelines
Problems we help you overcome
Models degrade silently in production
Without monitoring, data drift and concept drift go undetected until business KPIs are impacted.
Manual, error-prone deployments
Ad-hoc model releases create reproducibility gaps and slow rollback when issues arise.
No retraining workflow
Teams lack automated pipelines to refresh models when performance drops below thresholds.
What we bring to the table
Model CI/CD pipelines
Automated build, test, and promote workflows with staging gates and rollback support.
Drift detection & alerting
Real-time monitoring for data drift, prediction drift, and latency anomalies.
Experiment tracking
Centralized logging of training runs, hyperparameters, and artifact versioning.
Industries We Serve
Healthcare & Life Sciences
Clinical NLP, coding automation, triage assistants (HIPAA-ready).
Financial Services
Fraud detection, automated underwriting, compliance monitoring.
Legal & Compliance
Contract review, e-discovery, regulatory tracking.
Retail & E-commerce
Personalization, search, conversational commerce.
Manufacturing & Industrial
Predictive maintenance, CV inspection, supply-chain optimization.
Telecom & Edge
Customer automation, low-latency on-device inference.
Cybersecurity
Threat detection, SOC automation.
Public Sector & Energy
Document automation, forecasting, citizen services.
Pricing & Engagements
Discovery & Assessment
Fixed-fee 1–2 week assessment with roadmap.
POC-to-Pilot
Fixed-scope 2–6 week POC, includes data prep, prototype model, and success criteria.
Production & Managed Services
Subscription for hosting, monitoring, retraining, and support (SLA options).
Professional Services
Time-and-materials or outcome-based pricing for custom work.
Measurable impact
Measurable business impact from this engagement.
Shorter time-to-production
Lower technical debt
Reliable model performance
Frequently asked questions
What MLOps platforms do you integrate with?
We work with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, and custom Kubernetes-based stacks.
How quickly can you set up model monitoring?
A baseline monitoring stack can be deployed within 1–2 weeks for existing production models.
Do you support automated retraining triggers?
Yes. We configure threshold-based and schedule-based retraining with human approval gates for regulated environments.
Case Study
Problem
A regulated enterprise needed domain-accurate LLM responses without exposing sensitive data to public APIs.
Solution
LLM Customization & RAG, MLOps & ModelOps, Responsible AI & Governance
Outcome
40% reduction in human review time, 99.2% factual accuracy on domain tasks, and predictable inference costs within 90 days.
Ready to deploy with confidence?
End-to-end ML lifecycle: reproducible training, CI/CD, automated monitoring, drift detection, and retraining workflows to keep models reliable in production.
More AI Services
Why Choose Us
- ✓ Industry focus + measurable outcomes: domain models with validated ROI metrics.
- ✓ POC-to-production playbook: repeatable 2–6 week POC that moves to production fast.
- ✓ SLA-backed production support: uptime, latency, and retraining SLAs.
- ✓ Compliance-first: HIPAA/GDPR/PCI-ready architectures and audited pipelines.