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Build production AI systems your engineers can operate on day one.

We build, deploy, and operate AI systems inside your cloud — model, inference infrastructure, evaluation harness, and the runbook your team uses at 3 a.m.

What you get

Three concrete deliverables.

First deployment in 90 days

Production-grade inference service

A versioned API, autoscaling inference, request logging, and a model registry your team can promote against — not a notebook.

Wired before launch

Evaluation and monitoring harness

Golden-set evals, drift detection, regression dashboards, and on-call alerts tuned to the failure modes the model actually has.

Transferred at week 12

Operations runbook and on-call rotation

Documented incident playbooks, escalation paths, and a knowledge-transfer cycle so your engineers own the system at handoff.

How we work

From kickoff to production.

012 weeks

Use-case framing

Pick the narrowest valuable use case, write success criteria, and agree on the eval set we will be graded against.

023 weeks

Model selection and prototype

Side-by-side bake-off across candidate models with the eval set you signed off on. Pick on numbers, not on demo magic.

035 weeks

Inference infra and evaluation harness

Build the serving path, the request logging plane, and the eval pipeline so you can measure changes between every deploy.

043 weeks

Production cutover

Shadow traffic, then partial cutover with a kill switch, then full. Every step gated on the eval harness, not a calendar.

05Continuous

Operations and improvement

On-call coverage during stabilization, plus monthly model and prompt improvement cycles tied to your eval scoreboard.

The stack we build on.

Cloud-agnostic. We meet you where your tenant lives.

Azure OpenAIAnthropic ClaudeOpenAIMicrosoft Copilot StudiovLLM / TritonKubernetesOpenTelemetryEvals (custom + Inspect)

Outcome metrics

90 days
First production deployment

Median, framing through cutover

8x
Eval iteration speed

Versus pre-engagement baseline

99.9%
Inference uptime

Rolling 90-day average, post-stabilization

From the field

One we shipped.

Fortune 500 manufacturer · supply chain

Replaced a brittle Copilot demo with a versioned inference service against SAP — partial cutover live in week 9, full cutover at week 14, eval-gated.

90%

Query latency reduction

Vs. prior NLP layer

Read the case study

FAQ

Questions buyers ask first.

Do you bring your own models or use ours?
Both. Most engagements start with a hosted model (Azure OpenAI, Anthropic, OpenAI) for time-to-value, then add a fine-tuned or open-weight model behind the same API when the cost curve justifies it. We document the cost-versus-quality trade in writing before either decision.
How do you measure model quality before we ship?
We build the eval set with you in week one — labeled examples your domain experts agree on — and every prompt change, model swap, or retrieval tweak runs against it in CI. No subjective demos in the room.
Who operates the system after handoff?
Your engineers. We pair on the on-call rotation for the first 30 days post-cutover and transfer the runbooks, dashboards, and escalation paths in writing. We can stay on a managed-service contract afterward, but the default is full handoff.
How do you handle prompt and model versioning?
Prompts live in your repo behind a versioned prompt registry. Model versions are pinned in the inference service, and rollback is a single config flag. Every production change leaves a written audit trail.
Where does training and inference data go?
Inside your tenant, under your IAM. We deploy inside your Azure, AWS, or GCP account; no member or customer data leaves your cloud boundary; logging plane is private. We do not pool data across clients.

Related services

What buyers usually pair with this.

Agentic systems

Multi-step AI agents with human-in-the-loop governance on top of the AI engineering foundation.

See the service

RAG and knowledge systems

Retrieval, citation, and document-grounded answers wired into the same inference service.

See the service

Data engineering

Pipelines and feature stores that make your data actually ready for the model you want to ship.

See the service

Ready to scope this?

Thirty minutes with a principal. We will walk through your constraints and what a 30- to 90-day pilot would actually look like.