The platform AI, ML and automation run on.
Kolomolo builds and operates the engineering foundations needed to move AI, ML, automation and predictive workloads into production. We work across cloud, hybrid, private and edge environments — integrating with the customer's existing systems, data platforms, sensors, operational tools and delivery model.
When AI needs production foundations.
Models, agents and workflows need more than good logic — they need environments, data flows, integrations, monitoring, controls and clear ownership before they run reliably in production.
Models and agents exist, but the platform is not ready.
Teams have promising AI, ML or automation workloads, but lack the MLOps, LLMOps, monitoring, integration or operating routines needed for production.
AI needs to connect to real systems and data.
Workloads need to integrate with enterprise applications, cloud services, data platforms, sensors, devices, service tools and operational workflows.
Production ownership is unclear.
Teams need a clearer model for who runs the service, monitors it, handles incidents, improves quality and manages handover after launch.
Engineering services connected to production.
Engineering work should leave behind production foundations that teams can run, monitor and improve. Kolomolo builds around the customer's existing cloud, systems, data platforms, sensors, operational tools and operating model.
MLOps & LLMOps
Build the pipelines, deployment routines, evaluation flows, model registries, monitoring and release processes needed for AI and ML services.
AI Platform Engineering
Design and build the cloud, hybrid or private platform foundations needed to run AI, ML, automation and predictive workloads.
Enterprise Integration
Connect AI services to existing cloud estates, data platforms, enterprise applications, APIs, identity systems and operational tools.
Sensor & Edge AI Infrastructure
Support workloads that use sensor data, devices, edge inference, ruggedized hardware or distributed field environments.
Monitoring & Observability
Set up service monitoring, model performance tracking, drift review, cost visibility, logs, alerts and operational dashboards.
Data & Feature Foundations
Prepare data pipelines, feature stores, data quality routines and integration patterns for reliable AI and ML workloads.
Security & Control Implementation
Implement permissions, access patterns, environment separation, data handling controls and operational guardrails required for production services.
Managed AI Operations
Operate, monitor and improve AI, ML and automation services after go-live until the customer is ready to take over.
How engineering work runs.
Engineering work starts with the workload and the environment it must run in. Kolomolo assesses the existing estate, designs the production path, builds the foundations, and supports launch, operation and handover.
Review the customer's cloud, data, integration, security, monitoring, sensor, edge and platform environment against the target AI or ML workload.
Define the architecture, platform components, integration approach, deployment model, controls, monitoring and operating ownership.
Implement platform foundations, pipelines, environments, integrations, monitoring, release routines and workload-specific infrastructure.
Move workloads into production with monitoring, incident routines, cost visibility, improvement loops and handover path in place.
What the customer can run.
Engineering work should leave behind production foundations that teams can operate, improve and scale.
- Production architecture and platform design
- MLOps and LLMOps setup
- Cloud, hybrid, private or edge deployment model
- Enterprise integration plan and implementation
- Data pipeline and feature foundation setup
- Monitoring, observability and drift review setup
- Security, access and control implementation
- Cost visibility and optimisation view
- Operating model, runbooks and handover materials
- Managed operation model where needed
Common questions.
30 / 60 / 90.
Cloud, data, integration, sensor, security, monitoring and platform readiness reviewed against the target workload.
Architecture, deployment model, platform components, integrations, controls and operating model agreed.
Core platform, pipeline, monitoring or integration foundation implemented with production ownership and handover path defined.
Engineering connected to operation.
Engineering only creates value when the workload can run after launch. Kolomolo builds the foundations, monitoring and operating routines needed for AI, ML and automation services to keep working in production.
We work with the cloud, systems, data platforms, sensors and operational tools the customer already uses.
The platform supports generative AI, classical ML, anomaly detection, forecasting, optimisation and automation workloads.
Monitoring, release process, cost visibility, incident handling and ownership are designed with the platform, not added later.
Kolomolo can stay after launch to monitor services, improve performance and support handover when the customer is ready.
Need production foundations for AI, ML or automation?
Talk to a Kolomolo practitioner about the platform, integration, monitoring or edge infrastructure needed to move your workload into production.