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, safety limits — 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 135+ 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.
The deterministic, explainable layer of the AI stack — purpose-built services for computer vision, NLP, and rule-based inference where transparency and auditability matter.
These services run alongside the generative AI layer, providing the auditable, deterministic alternative for use cases where hallucination is unacceptable — compliance gates, eligibility checks, lead scoring, diagnostic decision trees, and policy enforcement. All services are production-ready, running on CPU infrastructure with per-tenant isolation.
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: 135+ agents run in production on this exact ladder, grounded and evaluated, across 19 live platforms.
Delivered by the AI/ML fleet that keeps our own agents grounded and economical.
The real, relevant stack we build this with — model-agnostic, open-source-first, production-grade.
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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