Machine Learning Consulting

Most machine learning projects stall in the same place: a model works in a notebook, then never makes it into the product. Krazimo’s machine learning consulting exists to close that gap. We’re a boutique team of ex-Google engineers who scope, build, and operate ML systems that run reliably in production — not slide decks, and not proofs of concept that quietly die.

Because we cap active work at ten projects, the same senior engineers who frame your problem are the ones who deploy and monitor the result. That is the difference between getting advice and getting a working system.

Machine learning development services

We take ML work end to end — from problem framing and data readiness through model development, evaluation, and the application layer around the model. Typical engagements include:

• Problem framing & feasibility — deciding what is actually an ML problem (and what is a rules problem wearing an ML costume) before you spend a dollar building.
• Model development — classical ML, deep learning, or fine-tuned foundation models, chosen for the job rather than the hype.
• Evaluation-first delivery — we define the success metric and the offline and online eval harness before the build, so “good enough to ship” is a number, not an opinion.
• Application engineering — the APIs, pipelines, and interface that turn a model into something your team and customers actually use.

MLOps consulting

A model in production is a living system, not a one-time deliverable. Our MLOps consulting builds the release path that makes models safe to ship and safe to change: reproducible training pipelines, a model registry and versioning, CI/CD for models, feature stores where they earn their keep, and safe rollout patterns — shadow, canary, and staged — so a new model never silently breaks the one it replaces. If you already have data scientists, we make their work shippable; if you don’t, we run the pipeline for you.

Model deployment & monitoring

Deployment is where most consultancies hand you a repo and walk away. We stay through serving, scaling, and the part that actually protects your ROI: monitoring. We instrument latency and cost, watch for data and concept drift, alert the moment quality degrades, and wire in retraining triggers so the model keeps earning its place. The result is a system you can trust on a Friday afternoon, not one you babysit.

Why teams bring us in

Our edge is first-hand production experience, not a methodology slide. We pair that with a risk-free trial so you can see how we work before you commit, and an evaluation-first stance that keeps everyone honest about whether the model is good enough yet. Many engagements start as a broader custom AI software development effort, or grow into LLM and RAG development once the first model is live.

If you have models stuck short of production — or a problem you suspect ML could solve — book a scoping call and we’ll tell you straight whether it’s worth building.

How a machine learning consulting engagement works

We keep it concrete and low-risk, in four phases:

• 1. Discovery & data assessment — we pressure-test the problem, check whether your data can actually support a model, and define the success metric. You get a go/no-go you can trust before spending on a build.
• 2. Scoped pilot (risk-free trial) — a small, fixed-scope first model against that metric, so you see how we work before committing to the full engagement.
• 3. Build & evaluate — a production-grade model plus the evaluation harness, with results measured, not asserted.
• 4. Deploy, monitor & hand over — we ship it, wire up monitoring for drift, and either run it for you or upskill your team to own it.

Where machine learning consulting pays off

The work worth doing is where a prediction or a piece of understanding changes a real decision. Common, high-ROI use cases we are brought in for:

• Forecasting & demand — inventory, capacity, and revenue prediction that beats spreadsheet heuristics.
• Churn & propensity — scoring which customers will leave or convert, so teams act on the right ones.
• Document & image understanding — extracting structure from contracts, claims, scans, and photos that used to need a human to read.
• Anomaly & fraud detection — catching the rare, costly events that rules miss.
• Predictive maintenance & recommendations — anticipating failures and personalising what each user sees.

For Arivihan, an edtech company in India, we automated CBSE-style exam grading and cut manual grading time by 60% while making evaluation more consistent and fairer across students — the kind of outcome that only counts once the model is actually live and monitored, which is the whole point of how we work.

When to hire an ML consultant vs. build in-house

Hire a consultant when models keep dying in notebooks, you have no MLOps muscle yet, you need senior eyes on a high-stakes build, or the capability matters but is not something you will staff a permanent team around. Build in-house when machine learning is core to your product and you can hire and retain senior ML engineers long term. In practice it is often both: we ship the first production models and stand up the pipeline, then hand the keys to your team. We will tell you honestly which side of that line you are on.

Choosing the right kind of ML for the constraint

Consulting means picking the right approach, not the trendy one. For a financial-advisor benchmarking product we built deterministic, compliance-safe scoring — generative AI deliberately kept out of the scoring algorithm — so advisors get objective, percentile-based positioning that holds up to regulatory scrutiny. Sometimes the most valuable machine learning consulting is knowing when not to use a generative model at all.