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What we do

Data, AI & MLOps

Turn the pilot into something you run in production, with monitoring, evaluation, and rollback from day one.

For: Organizations putting real models and data platforms into production, past the demo and into the business.

What we deliver

What you walk away with.

MLOps pipelines

Training, serving, evaluation, and rollback, so your models have the operational scaffolding to survive contact with users.

Data platforms

Governed architecture, lineage, and analytics your decision-makers can act on without second-guessing the numbers.

AI integration

LLM and vision integration with observability, provider-agnostic, so you keep your leverage and avoid lock-in.

Latency & cost tuning

We took cold-start latency from 45s to 9s. Performance is a deliverable here, not something you hope for.

Tech & standards

The exact tools and standards you get.

Stack
Vertex AIGemini · AnthropicGKEBigQueryLangfuseFeature storesCloud Run
Standards
Responsible AIModel governanceSOC 2
Proof
45s → 9s

Cold-start latency, secured AI/ML pipeline and GKE migration

Supernomics
Read the case study →
FAQ

What you'll want to know before you commit.

We build the MLOps scaffolding the model needs to survive contact with users: training, serving, evaluation, and rollback. We secured an AI/ML pipeline and migrated it to GKE for Supernomics.

We build governed data platforms with lineage and audited access, and integrate LLMs under no-training, minimized-data terms so your data stays yours. Governance is designed in, not assumed.

Monitoring and evaluation run from day one, so you see drift and accuracy slip before your users do. Observability on the model is a deliverable, not an afterthought.

We make that call on the economics and the use case, and stay provider-agnostic across Gemini, Anthropic, and Vertex AI so you keep your leverage and avoid lock-in.

Performance is a deliverable here. We took cold-start latency from 45s to 9s and tune cost the same way, so you are not paying for waste or waiting on the model.

Every model and prompt change runs against an evaluation harness with monitoring and rollback wired in, so nothing reaches your users on a hope. You see the numbers first.

Start here

Need data, ai & mlops, to standard?

Talk to the senior engineers who would build it and stay on to run it.