AI PLATFORM & MLOPS

The engineering layer that takes AI from prototype to production

We build the MLOps and LLMOps platforms underneath reliable AI: model and prompt deployment pipelines, eval and observability, data and feature infrastructure, and inference scaling. The unglamorous engineering that turns a working demo into a system you can trust in production.

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[ ▸ ] Senior-led delivery · outcome-based · you own all code & IP

THE GAP

The demo works. Production is a different problem.

Models that shine in a notebook quietly fall apart once real traffic, real data, and real accountability arrive. The missing piece is almost never the model — it is the platform around it.

Stuck in the notebook

A promising prototype that nobody can deploy, reproduce, or hand off — so it never becomes a product.

No CI/CD for models

Every model or prompt change is a manual, risky deploy. No automated testing, no eval gates, no safe rollback.

Flying blind

No observability into quality, drift, cost, or latency. The first sign of a regression is an angry customer.

Brittle data pipelines

Training and serving data drift apart. Features break silently and nobody can explain why outputs changed.

Scaling & cost pain

Inference costs spiral and latency suffers under load. Nobody is sure which knobs actually move the bill.

No governance

No versioning, approvals, audit trails, or access control. You cannot say which model produced which decision.

WHAT WE BUILD

The production layer, engineered properly

Six building blocks that turn AI experiments into systems your team can deploy, monitor, and operate with confidence.

Model & LLM deployment pipelines

CI/CD for models and prompts: automated testing, eval gates before release, versioned artifacts, and safe rollout with one-click rollback.

Eval & observability

Continuous monitoring for output quality, data and concept drift, cost per request, and latency — with dashboards and alerting your team actually watches.

Feature & data infrastructure

Reliable data and feature pipelines that keep training and serving consistent, with lineage and validation so outputs are reproducible and explainable.

Inference scaling & cost optimization

Serving infrastructure tuned for throughput and latency — batching, caching, autoscaling, and right-sized hardware — so unit economics hold up at scale.

LLMOps

Prompt, version, and eval management for generative AI: a clean workflow for iterating on prompts and models with measurable, comparable results.

Model governance & access control

Versioning, approval workflows, audit trails, and access control so you always know which model is live, who changed it, and what it produced.

MARKET CONTEXT & PRICING

Priced on outcomes, anchored to what production really costs

Market context

Production ML/MLOps platform builds typically run $200K–$600K depending on scope.

That range comes from industry pricing surveys and reflects the real cost of deployment pipelines, evals, observability, and data infrastructure — not a single experiment. We share it as market context, not as a fixed R&D Teams quote.

[ ▸ ] Source: industry pricing surveys (market context)

How we price

$100K+

Engagements start at $100K and are priced on the outcomes we deliver, not hours.

Senior US-facing strategy, leveraged delivery
Scoped to a defined production outcome
You own all code, IaC, and documentation
A system your team can run without us

Scope your platform engagement

FAQ

Honest answers about platform & MLOps work

A prototype proves something can work once, often in a notebook. Platform and MLOps work is the engineering that makes it work reliably every day — deployment pipelines, eval gates, observability, data infrastructure, and scaling. Most teams can produce a demo; the hard part is the production layer underneath it, and that is the work we focus on.

No. We can come in at different stages: hardening an existing model or agent for production, building the platform a team will deploy onto, or designing the infrastructure alongside the first models. We meet you where you are and build toward a system you can operate without us.

We build in your cloud (AWS, GCP, or Azure) and work with the tools your team already uses where they fit. We favor open, well-supported components over lock-in, and we document everything so your engineers can own and extend the system after the engagement.

You do. You own all code, infrastructure-as-code, and documentation we produce. We can sign an NDA on request, and we practice security-conscious engineering — least-privilege access, secrets management, and audit trails. We do not hold formal compliance certifications, and we will never imply that we do.

We price on outcomes, not hours, and engagements start at $100K. For context, industry pricing surveys put production ML/MLOps platform builds in the $200K–$600K range depending on scope — that is market context rather than a fixed quote. We scope to the result you need and the value it creates.

Against concrete operational outcomes agreed up front — for example reliable deployments behind eval gates, monitored quality and cost, reproducible pipelines, and a system your team can run independently. We define those metrics together before we start so success is unambiguous.

Ready to ship AI you can actually run in production?

Book a call to scope the platform and MLOps engineering your AI needs to be reliable, observable, and yours.

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