LET'S TALK

Talk to engineers. Not account managers.

Describe what you're building or where you're stuck. You'll hear back within one business day - from the engineer who would actually work on it, with a direct technical read on what it takes to ship.

Bartlomiej Korpus

bards.ai do more than what we ask them to. They always suggest solutions that we couldn't figure out without them.

Bartlomiej Korpus

CTO @ SurferSEO

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HOW WE WORK

How engagements run.

Problem Definition

We define what 'working' looks like before writing any code. What does a wrong answer cost in production? What latency is acceptable? What eval bar ships the feature? Most ML projects fail because scope was set by a demo - not a production metric.

Data Collection & Training

We audit your data before any training runs. Signal quality, label consistency, and distribution match to production all get checked. We design the pipeline for what the model actually sees at inference - not what looked clean in a notebook.

Model Design & Evaluation

Every training run lands in W&B. Your team gets full workspace access - not a summary deck. We run evals before each deployment and set regression gates. You'll know if the model regressed before your users do.

Project Deployment

We don't hand off a model file and disappear. We set up monitoring, define alert thresholds, and stay embedded for 30 days post-launch. Production failures are predictable if you're watching the right signals.