From assessment to production

01 — Advisory

AI Consulting

We start where the value is. Through a focused review of your data, workflows, and objectives, we identify where AI can move a real metric — cost, speed, quality, capacity — and where it can't yet. You get a clear recommendation, an architecture, and a build plan you could hand to any competent team.

• Opportunity map ranked by value and effort
• Feasibility review against your actual data
• Model, tooling, and vendor recommendations
• Reference architecture and phased roadmap
• Cost projection for build and ongoing inference

02 — Build

Custom Software

The model is a small part of the product. We build everything around it: the interfaces people use, the APIs other systems call, the pipelines that move and prepare data, and the cloud infrastructure that runs it. Modern, well-tested code with documentation and CI/CD, delivered in your repositories and your cloud account.

• Web applications, internal tools, and dashboards
• REST / streaming APIs and service integrations
• Data ingestion, transformation, and retrieval pipelines
• Retrieval-augmented generation and agent workflows
• Infrastructure-as-code, CI/CD, and observability

03 — Run

Deployment & Integration

Getting a model into production is its own project. We handle inference hosting — self-managed GPUs, serverless GPU, or hosted APIs — with autoscaling, evaluation harnesses, monitoring, and cost controls. The result runs on infrastructure your team can understand and maintain after we're gone.

• Inference hosting on GPU VMs, serverless GPU, or managed endpoints
• Autoscaling and scale-to-zero for spiky workloads
• Evaluation sets and regression checks
• Latency, throughput, and spend monitoring
• Runbooks and handover so your team owns operations