Model evaluation and experimentation you can trust
Model Evaluation & A/B Testing as a repeatable delivery practice — applied with tooling, governance, and metrics your team can sustain.
Model Evaluation & A/B Testing — Overview
Offline metrics lie. We design evaluation frameworks — holdout strategies, shadow deployments, and A/B tests — that reflect real user impact.
From ranking models to generative systems, we help teams measure lift without gambling on production traffic.
Trusted by teams who ship with us
Production engineering across retail, supply chain, education, and security — selected logos from recent programs.
How we run model evaluation programs
Structured experimentation from hypothesis to decision.
01
Metric design
Define primary, guardrail, and business metrics aligned to the use case.
02
A/B deployment
Traffic splitting with statistical power analysis and early stopping rules.
03
Offline evaluation
Reproducible benchmarks with leakage checks and segment analysis.
04
Decision & rollout
Documented outcomes, rollback paths, and learnings for the next iteration.
05
Shadow & canary
Compare candidate models against production without user impact.
Evaluation — what counts in production
Separating rigorous experimentation from vanity metrics.
Model evaluation
Systematic comparison of model candidates against defined success criteria.
A/B at scale
Experiment design that accounts for network effects and seasonality.
Guardrail metrics
Latency, error rate, and fairness checks that prevent harmful wins.
Not one-shot tests
Continuous experimentation culture with platform support.
Not accuracy alone
High offline accuracy can mask poor business outcomes or biased segments.
Audit trail
Every experiment logged with config, population, and result for reproducibility.
3.2M monthly messages. Commerce that stays up at peak.
WhatsApp turned from a manual inbox into a production sales channel — catalog, cart, payments, and AI support at retail scale.
Read story
Self-serve analytics without opening the BI queue
Governed natural-language access to business data so leaders get answers without waiting on analysts or raw SQL.
Read storyModel evaluation — common questions
We map your current pipelines, define ownership and checkpoints, then implement tooling and runbooks so the practice continues after our engagement.
Yes. We extend Kubeflow, MLflow, SageMaker, Vertex, or custom stacks — introducing new components only where they reduce operational risk.
Documented workflows, automated gates, dashboards, and trained internal owners — not slide decks.
Move forward with model evaluation & a/b testing
Book a conversation — we will scope the engagement or tell you if another path is a better fit.