Private model serving
Operate open-weight or approved models inside on-premise, private-cloud or hybrid environments.
Capabilities / AI Infrastructure & LLMOps
AI infrastructure built to be operated, not just demonstrated.
We design the serving, evaluation and operational layer behind private LLM and enterprise AI systems, from capacity planning through monitored releases.
[ The buyer problem ]
A working model endpoint is only the beginning.
Production AI introduces variable workloads, costly compute, probabilistic behavior and fast-moving dependencies. Teams need repeatable releases, evidence-based evaluation and clear operational ownership—not a collection of scripts around an API.
LLM evaluation pipelines
Compare prompts, models and retrieval changes against versioned test sets before release.
Inference capacity planning
Estimate throughput, latency, concurrency and hardware needs against expected workloads.
RAG and agent operations
Monitor the retrieval, tool and application layers that determine whether an AI workflow is useful.
[ Scope & deliverables ]
What an engagement can include.
Reference architecture
A deployment design covering model serving, data flows, environment boundaries and integration points.
Release and evaluation workflows
Versioned configuration, automated checks, acceptance thresholds and rollback procedures.
Observability and runbooks
Workload, latency, error and quality signals with practical incident and maintenance guidance.
[ Deployment & integration ]
Designed around the environment you operate.
On-premise
Customer-controlled infrastructure for workloads that require local data handling or direct hardware control.
Private cloud
Dedicated environments integrated with enterprise identity, networks, data platforms and delivery workflows.
Hybrid
Route workloads by sensitivity, cost and capability across controlled environments with explicit boundaries.
Security and operations
Security controls are selected for the environment and risk model. Typical concerns include network segmentation, secrets, identity and access, artifact provenance, encryption, logging, data retention and tested recovery procedures.
What we need to integrate
- • Expected workloads, latency goals and concurrency patterns
- • Available infrastructure, network boundaries and identity systems
- • Data sources, model choices and application integrations
- • Evaluation criteria, retention requirements and operational owners
[ Implementation process ]
From constraints to an operable system.
01
Baseline
Profile workloads, dependencies, quality needs and current operating constraints.
02
Architect
Choose serving, storage, evaluation and delivery patterns for the environment.
03
Implement
Build infrastructure, integrations, automated checks and operational visibility.
04
Handover or operate
Document ownership, train teams and agree how the system will be maintained.
Scope the right deployment.
Tell us about the workload, data, environment and operational constraints. We’ll propose a practical first step.
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