CI/CD for models and prompts
Versioned, tested, staged deployments for models, prompts and configs — with instant rollback.
AI Infrastructure
MLOps & LLMOps
Operational excellence for models and prompts — versioning, rollouts, monitoring, and cost optimization.
- 10×faster model-to-production cycles
- 99.9%serving availability with progressive rollout
- 100%of models versioned and reproducible
Capabilities
The operating system for your models
Deploy, version, operate and continuously improve ML and LLM systems at scale.
Registry and reproducibility
Every model, dataset and prompt versioned and linked, so any production behavior can be reproduced.
Serving and scaling
GPU-efficient inference serving with autoscaling, batching and multi-region failover.
Drift and retraining
Monitoring that detects drift and pipelines that retrain, evaluate and promote automatically.
How it works
From notebook chaos to release discipline
AI systems have more moving parts than software — models, prompts, datasets, indexes. We bring them all under the same release discipline your code already has.
Release pipeline
Ship models like you ship software
Models, prompts and retrieval configs move through the same gated pipeline: version, test, evaluate, stage, canary, promote. Rollback is one click, not one weekend.
- Versioned artifacts: models, prompts, datasets, configs
- Eval gates and canary releases before full traffic
- One-click rollback with full provenance
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Serving
Inference infrastructure that earns its GPU bill
Right-sized serving with batching, caching, quantization and autoscaling — self-hosted models where control matters, API models where speed matters, one operational surface across both.
- GPU serving with vLLM, batching and quantization
- Autoscaling tuned to traffic and cost targets
- Unified ops across self-hosted and API models
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
Continuous improvement
Drift detected, retraining automated
Input distributions, output quality and business metrics are monitored continuously. When drift crosses thresholds, retraining pipelines kick off — and only promote if evals prove improvement.
- Data and concept drift detection with alerting
- Automated retrain-evaluate-promote pipelines
- Champion/challenger testing in production
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Where mlops & llmops delivers value
LLM platform buildout
Shared serving, registry and release tooling for every AI team in the company.
Self-hosted model ops
Open-source models served efficiently in your VPC with full lifecycle management.
Classical ML modernization
Fraud, forecasting and recommendation models brought under modern ops discipline.
Prompt release management
Prompts treated as deployable artifacts with testing and staged rollout.
Multi-team governance
Central platform with per-team autonomy, quotas and cost attribution.
Regulated model lifecycle
Reproducibility and approval workflows that satisfy model-risk management.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Deployments went from quarterly, all-hands-on-deck events to routine Tuesday releases. The platform paid for itself the first time we rolled back a bad model in ninety seconds.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Kubernetes
- vLLM
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Azure ML
- Ray
- Airflow
- dbt
- Terraform
- ArgoCD
FAQ
Common questions
How is LLMOps different from MLOps?
Same discipline, new artifacts: prompts, retrieval indexes and eval sets join models and datasets. Latency and token cost become first-class metrics, and output quality needs eval-based gating rather than a single accuracy number. We build one platform covering both.
Should we self-host models or use APIs?
Usually both. APIs win on speed-to-market and frontier quality; self-hosting wins on unit cost at volume, latency control and data residency. The platform makes the choice reversible per workload.
Can you build on our existing cloud ML stack?
Yes. We extend SageMaker, Vertex or Azure ML with what's missing — usually eval gating, prompt versioning and LLM serving — rather than replacing what your team already knows.
What does 'reproducible' actually mean here?
Any production answer can be traced to the exact model version, prompt version, retrieval index snapshot and config that produced it — and that combination can be re-run. That's the foundation for debugging, audit and rollback.
Next step
Give your models a real release process
We'll assess your current model lifecycle and ship the platform that makes deployments boring.