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Models drift quietly. Yours won't.

Versioning, evaluation, deployment and live monitoring for every model and prompt you run, so quality drops get caught by a dashboard, not a customer.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: MLOps & model monitoring

Where you are. Where you’ll be.

You need this if

  • Nobody can say which model version is live right now
  • A provider model update has already broken something once
  • You measure AI quality by counting complaints

What changes for your business

  • Quality regressions caught before customers see them
  • Releases go out on evidence, not on someone's confidence
  • Inference spend tracked per model, per feature, per team

What we hand over

  1. Model and prompt registry with versioned releases
  2. Automated eval suites gating every deployment
  3. Drift, accuracy, latency and cost monitoring with alerting
  4. One-step rollback and incident runbooks

What it is

MLOps is the discipline of running machine learning and LLM systems in production: versioning models and prompts, testing every release, and watching quality, cost and drift once live. It exists because AI behaviour changes after launch, when data shifts or a provider updates a model, often without any code changing.

A model that scored well in March can be wrong in June: the data moved, a vendor updated the model, or a prompt change broke an edge case. Without a lifecycle around it, you find out from complaints. We put every model, prompt and retrieval index under version control, gate releases on automated evals, and watch live accuracy, latency, cost and drift with alerts tied to owners. Rolling back takes one command.

Why now
95% of generative AI pilots deliver no measurable P&L impact. MIT NANDA, The GenAI Divide, 2025 (opens in a new tab)
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

Why monitor a model that already passed testing?
Because the world it was tested on moves. Input data shifts, vendors update hosted models without asking, and prompt edits break edge cases. Without live evaluation, the first signal is a customer complaint.
Does this cover LLM prompts and RAG, not just classic ML?
Yes. Prompts, retrieval indexes and model versions are all versioned together, and evaluation sets cover answer quality, grounding in sources and cost per request, alongside the accuracy and drift checks used for classic models.
Can you work with our existing ML tooling?
Usually. We audit what's there, keep what works, and fill the gaps: typically the evaluation gates, the alerting tied to owners and the rollback path. We don't replace a working stack to match a preference.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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