MLOps and scalable ML platforms that ship faster and stay reliable

We operationalize models and agents with deployment pipelines, observability, evaluation and the runtime controls that keep autonomous production systems reliable and improving. The platform beneath scales training and inference with the workload, so releases move from a hand-run risk to a routine step.

5M+

Transactions per month

80%+

Accuracy sustained

50%

Processing time cut

30 to 50%

GPU cost reduction

ML platform teams

Data science operations

Release engineering

AI infrastructure teams

Faster releases, market share growth

ML CI/CD and release engineering

Automated build, test and deploy for models and agents
Versioning that keeps rollback and audit simple
Test-case automation standing up in the first days and weeks
Releases that become a routine, scheduled step

Scalable ML platforms for training and inference

Distributed training for large models across the estate
Serving infrastructure engineered for sustained inference load
Processing that scales with data volume
GPU and compute capacity allocated by demand

Model observability and continuous evaluation

Live monitoring of accuracy, latency and cost per model
Continuous evaluation against fresh production data
Drift caught in monitoring, ahead of the business noticing
Evaluation suites that travel with the model into every release

Runtime cost and infrastructure optimization

Right-sized infrastructure for each workload
Cost tracked and optimized per model in production
Retraining automated as a closed loop, model by model
Spend held steady as data volume grows

Operate ownership after go-live

Teams embedded, credentialed and working from day two
Ownership across pipelines, platforms, applications and analytics
The first automation shipped inside the first sprint
Teams brought along as systems move into autonomous operation

ML CI/CD and release engineering

Automated build, test and deploy for models and agents
Versioning that keeps rollback and audit simple
Test-case automation standing up in the first days and weeks
Releases that become a routine, scheduled step

Scalable ML platforms for training and inference

Distributed training for large models across the estate
Serving infrastructure engineered for sustained inference load
Processing that scales with data volume
GPU and compute capacity allocated by demand

Model observability and continuous evaluation

Live monitoring of accuracy, latency and cost per model
Continuous evaluation against fresh production data
Drift caught in monitoring, ahead of the business noticing
Evaluation suites that travel with the model into every release

Runtime cost and infrastructure optimization

Right-sized infrastructure for each workload
Cost tracked and optimized per model in production
Retraining automated as a closed loop, model by model
Spend held steady as data volume grows

Operate ownership after go-live

Teams embedded, credentialed and working from day two
Ownership across pipelines, platforms, applications and analytics
The first automation shipped inside the first sprint
Teams brought along as systems move into autonomous operation

A credentialed team is working inside your operation within 48 hours, on the highest-toil workflows first.

Build, test, deploy and rollback become one pipeline for models and agents alike.

Automation carries the repeatable work, and each wave lands with its own rollback and acceptance criteria.

Monitoring, retraining and evaluation run automatically, and the operate layer keeps taking on more of the estate.

Client success in production, at scale

See how Agivant engineers real business value for the autonomous enterprise, across several industries

See more client stories

Telecommunications

Autonomous AIOps

MLOps & Scalable ML PlatformsCloud & Platform Engineering

Deep Dive

30% OPEX savings from AI-driven auto-recovery across 200+ microservices

Ticket volume ran at 2,500 to 3,000 a month with an average process time of 30 minutes. Agivant built anomaly detection and auto-recovery across 200-plus microservices, cutting process time to three minutes and handling 30% of fallout automatically.

30%Operating cost saved through auto-recovery

Technology and internet

Enterprise MLOps on Vertex AI

MLOps & Scalable ML Platforms

Deep Dive

80%+ accuracy sustained a year after go-live across 5M+ transactions a month

Deep learning audio quality models had to hold accuracy in production at volume. Agivant built the MLOps platform with continuous drift monitoring and automated retraining and deployment pipelines, under full audit trail and version control.

80%+Accuracy sustained a year after go-live

Semiconductor and EDA

50% lower MTTR with an agent that recommends the fix and runs it on approval

Incident knowledge sat across tickets, documents, forums and runbooks. Agivant built an agent that runs root cause analysis automatically, recommends the fix and executes it on approval, with alert noise down 30%.

50%Lower mean time to resolution