Problem framing
Define labels, baselines, and success metrics so you don’t fund a model that can’t move the product needle.
ADD AI
AI & Machine Learning
Applied ML, recommendation systems, computer vision, and NLP pipelines from prototype to production.
Overview
How we approach ai & machine learning.
ML projects stall when success is defined as a notebook AUC instead of a product metric. We frame the prediction problem against business outcomes, baselines, and the operational path for features and labels before training gets theatrical.
We ship models into the product path — inference, monitoring for drift, and a retraining plan your team can run. Offline brilliance that never leaves a research environment doesn’t count as delivery.
The point
Models that ship inside the product and stay useful after the first dataset goes stale.
Deliverables
Concrete models in product, not just notebooks.
Every engagement leaves your team with shipped ML capabilities, clear metrics, and the ops path to keep models useful over time.
- Problem framing with labeled success metrics
- Baseline models and offline evaluation reports
- Production inference path integrated into the product
- Data contracts and feature pipeline documentation
- Monitoring for drift, latency, and quality
- Retraining plan, runbooks, and handover notes
Engagement Model
Frame the problem, ship a baseline, industrialize inference, or stay for model ops.
Baseline to prod
Train, evaluate, and integrate a first inference path with monitoring for the agreed KPI.
ML platform slice
Harden feature pipelines, versioning, and batch/online inference for the next set of models.
Model ops care
Drift watch, retraining cadence, and incident response as data and user behavior shift.
Timeline
From problem framing and baselines to live inference.
Week 1
Framing, data, and baselines
Confirm the decision the model supports, audit labels and leakage risk, and set a non-ML baseline to beat.
Weeks 2–4
Train, evaluate, decide
Iterate models offline, report metrics honestly, and go/no-go before building production plumbing.
Weeks 5–8
Inference and product wiring
Ship the serving path, feature contracts, and dashboards for latency and prediction quality.
Launch
Monitor, retrain plan, hand over
Drift alerts, retraining runbooks, and ownership so the model doesn’t silently rot.
What we need from you
Labeled reality and product hooks that make ML shippable.
Data owners, historical outcomes, and a place in the product for predictions to matter.
- Historical data with usable labels or a plan to create them
- Data owner who understands freshness, leakage, and privacy
- Product surface where predictions will drive a decision or UI
- Compute/environment access consistent with your security rules
- Agreement on the business metric that defines success
Common mistakes we help avoid
ML failure modes that waste quarters.
We structure delivery to catch these before they become “phase two.”
- Optimizing notebook metrics that don’t match product decisions
- Skipping a simple baseline that would have been “good enough”
- No plan for label drift or retraining after launch
- Shipping inference without latency and quality monitoring
- Treating feature pipelines as one-off scripts nobody owns
FAQs
Answers before we get on a call.
Do you always need deep learning?+
No. We start with the simplest model that can beat the baseline. Complexity is earned by the metric, not by fashion.
What if our labels are messy?+
We help define labeling rules, sample audits, and sometimes human-in-the-loop processes before heavy training spend.
Can models run in our cloud account?+
Yes. Training and inference typically stay in your environments with your IAM and data controls.
How do you handle model updates?+
Versioned artifacts, evaluation gates, and a documented retraining cadence — not silent swaps in production.