A FastAPI service with monitoring and a retraining script your engineers can run themselves. One fixed price quoted before any code gets written. Code in your repo on week five.
PROBLEMS I HEAR EVERY WEEK
By the time the email lands, the decision was made weeks ago. A churn model flags at-risk accounts 30 to 90 days early, when a CSM call can still change the outcome. Built on your account-level usage history, billing events, and support tickets.
Intuition-based ordering is expensive in two directions: spoilage on the high side, stockouts on the low side. A demand forecasting model trained on your historical sales and seasonal patterns cuts both at once. I built this for perishable inventory at LIFO AI across multiple retail locations.
A lead scoring model ranks your inbound pipeline by conversion probability based on your CRM history. Your best reps focus on the deals most likely to close, not the ones that replied fastest. The scoring lives in your CRM as a field your team can sort by.
A notebook is a demo. Production needs a serving layer, monitoring, and a retraining script your team can run without me. Every system I ship is built for production from week one, with a runbook your engineers can keep alive after I'm gone.
Black-box models kill adoption. SHAP explanations come with every model I ship: per-prediction feature attribution your team can read on the same dashboard they already use. They know why each call was made and can push back when something doesn't look right.
Real-time anomaly detection on transaction or sensor streams flags unusual patterns as they happen. The difference between an early flag and a cleanup project that runs for a quarter. The public fraud project on my GitHub catches 84% of fraud at 0.28% alert volume on a 577:1 imbalanced dataset.
How I solve it
Each move maps back to one of the quotes above. None of them is optional.
The "we built it and never used it" failure starts with a fuzzy target. Week 1 produces a one-page scoping doc that names the business decision, the cost-of-error numbers, and the success threshold. Both of us sign off before training begins. The notebook never sits unused if both sides agreed up front on what decision the output was going to inform.
Churn predictions don't come from a vibe. They come from feature engineering on your usage logs, billing events, and support tickets. Lead scoring runs on your CRM history. Demand forecasts run on your POS data. I built that stack at LIFO AI on Square POS data and would build it the same way on your stack.
The "80/20 of revenue" lead problem and the "fraud caught too late" problem are both threshold problems. F1 score doesn't know that a missed fraud costs $10K and a false positive costs the ops team an hour. I run threshold tuning against your real numbers and ship the tuning script so you can re-run it as the numbers shift.
Black-box models kill adoption. Every model I ship surfaces per-prediction feature attribution on the same dashboard your team already uses. Your CSM or fraud analyst reads the explanation in plain feature names before acting on the score.
A notebook is a demo. A service your engineers can run without me is a product. I wrap the model in FastAPI, push to your repo, and ship a monitoring dashboard and a retraining script your team runs on a schedule. After week five, your team owns the system.
The operational lifecycle, after handoff. Training data pipeline feeds a feature store. Model trained, evaluated, and explained with SHAP. Deployed as a FastAPI endpoint or batch job. Performance and drift monitored continuously. Retrained on a schedule your team owns.
Restaurant chains operating at scale have a brutal inventory problem: over-order and you throw product away; under-order and you disappoint customers and lose revenue. LIFO AI needed a system that could predict ingredient demand by location, day, and menu item.
The first approach — an LSTM neural network — was overengineered for the problem. The winning model was exponential smoothing with exogenous variables: simpler, faster to train, and more accurate on the available data. Deployed as a FastAPI service that updates nightly purchasing recommendations. The result: 22% reduction in food waste across pilot locations.
What's included
Full audit of your data quality, distributions, correlations, and potential leakage — before a single model is fit. The work most engagements skip, that explains most failures.
Domain-informed features that actually improve predictive power — not just one-hot encoding everything and hoping. This is where most of the performance gains come from.
Precision, recall, AUC, calibration plots, and a business-impact framing. You'll know exactly what the model gets right, what it gets wrong, and what that means in dollar terms.
Global and per-prediction feature importance so every stakeholder understands why the model made each call — and can flag when something doesn't make sense.
REST API or batch scoring pipeline — whichever fits your stack. With CI/CD, containerisation, and integration into your existing systems. Not a notebook emailed over.
Performance tracking over time, data drift detection, and scheduled retraining pipelines. The model stays accurate as your business evolves — not just on launch day.
Book a free discovery call — I'll assess your data and tell you exactly what's predictable, what the model would look like, and what the business impact could be.