Data pipelines, feature engineering, model selection, serving, and evaluation — the discipline that turns a model into a product that works in production. Built by our AI/ML fleet, grounded and measured.
The demo is easy. The 90% that makes it a product — data, features, serving, evaluation, guardrails — is where teams stall. That is exactly the layer we engineer.
Anyone can call a model API. Turning that into something a business can depend on takes the engineering most teams skip: shaping and validating the data, building features, selecting the right model per task, serving it under real latency and cost limits, and — critically — evaluating it against ground truth so quality is measured, not assumed. Our AI/ML fleet owns that pipeline end to end. Model selection is a per-task, cost-versus-quality decision on a best-fit ladder — open and self-hosted models at the floor, a strong model only at the quality gate — benchmarked on real evaluation sets, not vibes. This is how our own fleet runs 130 agents in production without a runaway inference bill: grounded, evaluated, and never locked to one vendor.
From raw data to a served, evaluated model — the disciplines that separate a product from a proof-of-concept.
We build the pipeline as an engineered system: contracted data in, features out, the right model chosen on evidence, served under real constraints, and evaluated on every release.
Model choice is evidence-based. We benchmark candidates on your task with real evaluation sets, score them on cost, quality, and latency, and let the numbers pick the winner — then re-evaluate as new models ship.
A repeatable ML pipeline — data in, features out, best-fit model served and measured.
This is an autonomous AI/ML fleet — an AI/ML engineer owning the eval harness and serving pipeline, with CloviModelWatch running cost×quality benchmarks — steered by a human. The proof is our own operation: 130 agents run in production on this exact ladder, grounded and evaluated, across 18 live platforms.
Delivered by the AI/ML fleet that keeps our own agents grounded and economical.
Every technology below is delivered by the same composed engineering team.
Tell us the outcome. We’ll build the data and feature pipeline, pick the best-fit model, serve it under real constraints, and evaluate it against ground truth.
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