Service · AI
Predictive models that earn their place in production.
Classical ML that runs the business — forecasting, fraud, risk, recommendations — trained on your data and deployed in your own cloud, not a demo notebook that never ships.
Trained, gated, deployed
The real problem
Why most ML projects never reach production.
A model that scores well in a notebook isn't a model that runs your operation. Often the data was cleaned by hand, accuracy was measured on the wrong split, and nobody decided what the prediction would actually trigger — so it lives forever in a slide deck.
The model is rarely the hard part. The hard part is the engineering around it — sound feature pipelines, honest evaluation, integration, and the monitoring that catches the day the world shifts. That surrounding system decides whether the model ever returns anything.
AI-project failure rate — roughly twice the rate of non-AI IT projects.
RAND Corporation ↗
Most enterprises still report no measurable EBIT impact from their AI work.
McKinsey, State of AI 2025 ↗
Where it pays
Where machine learning actually pays — and what each model delivers.
A family of predictive models, each earning its keep in a specific, high-volume decision.
Demand & sales forecasting
Predicts demand at the SKU, store, or region level.
Lower inventory cost and fewer stockouts at once.
Predictive maintenance
Flags a machine before it breaks, from its sensor and usage signatures.
Less unplanned downtime and longer asset life.
Fraud & anomaly detection
Scores transactions in real time and flags the ones that don't fit legitimate behavior.
More fraud caught with fewer false alarms.
Churn & risk prediction
Ranks customers, accounts, or loans by the probability of cancellation, default, or escalation.
Effort aimed where it changes the result.
Recommendation & personalization
Predicts the next product, content, or action most relevant to each user.
Higher conversion and order value from traffic you already have.
Document & image classification
Reads images or documents to sort, extract, or inspect — defect detection, claims triage, ID verification.
Manual review hours reclaimed, error rate steadier.
As of June 2026 · revisit quarterly
What machine learning does to those processes — the measured impact.
Independent industry findings cited as third-party evidence — not Silicon Prime's own client results.
Downtime reduced by predictive maintenance — and machine life extended 20–40%.
McKinsey, manufacturing analytics ↗
Up to 65% cut in lost sales from AI-driven demand forecasting — errors down 20–50%.
McKinsey, AI-driven forecasting ↗
Revenue lift from personalization — the engine behind recommendation models; marketing ROI up 10–30%.
McKinsey, May 2023 ↗
Fraud-detection lift from Mastercard's GenAI Decision Intelligence Pro — up to 300% in some cases.
Mastercard, Feb 2024, via PYMNTS ↗
What's included
What our machine learning development covers.
The difference between a model that runs the business and a notebook that wins a demo.
Problem framing & feasibility
Whether ML is the right tool at all, and what "good enough to deploy" means in your numbers — including the honest "don't build it" call.
Data & feature engineering
The feature pipeline, plus the leakage, imbalance, and drift that quietly wreck accuracy — the pipeline, not the algorithm, decides the result.
Model development & selection
Candidate models compared and chosen on your constraints — latency, interpretability, cost — not on what's fashionable.
Honest evaluation & validation
Judged against a real baseline on a production-realistic split, on the metric that matches the business cost. Doesn't beat it? It doesn't ship.
Deployment & MLOps
Shipped as a monitored service in your own cloud — batch or real-time — with versioning and a retraining path in place.
Monitoring, retraining & enablement
Instrumented for drift and decay, with retrain triggers set and your team trained to read the dashboards and own it.
What you get — all assigned to you under full work-for-hire IP
How it runs
How a machine learning engagement runs.
The same delivery model behind all our AI development work — one accountable lead, fixed scope, no handoffs.
STEP 01
Frame
Pin the decision the model drives, the data available, and the baseline it must beat.
Output: a ranked plan & the success criteria
STEP 02
Model
Engineer features, train and compare candidates in your cloud, and select on real constraints.
Output: a candidate model & a documented comparison
STEP 03
Validate
Measure against the baseline on a realistic split, check calibration and failure modes, stress the edge cases.
Output: an evaluation report & a go/no-go
STEP 04
Deploy & enable
Ship as a monitored service, instrument for drift, set retrain triggers, and hand it to your team.
Output: a production model & a team that owns it
Production discipline
The discipline behind a model you'd actually trust.
A model is only as trustworthy as the engineering underneath it. We don't claim a case study for every model type above — but we take software to dependable production and keep it there for years. Three engagements stand in as adjacent evidence.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We'll tell you plainly when machine learning is the wrong tool.
Evals before launch, monitor after
BJ's Restaurants — 4+ years applying our delivery process to a 200+ location chain, releasing twice a week with zero critical defects.
The same evals-before-launch, monitor-after discipline a production model demands.
Data-driven systems for the long haul
Bridge Athletic — a product partnership since 2012, still live and used by USC, the LA Rams, and MLB and MLS teams.
Proof we maintain data-driven systems for years, not a quarter.
Transaction engineering at scale
YardClub — a full marketplace that processed $120M+ and was acquired by Caterpillar in 2017.
The transaction engineering predictive models plug into.
Why build your models with us.
Production, not prototypes. Our reputation is software that ships and stays reliable for years, not notebooks that demo well — the exact gap that strands most AI projects short of production.
Honest evaluation is non-negotiable. A model that doesn't beat its baseline on a production-realistic split doesn't ship — and we'll say so rather than dress up an accuracy number.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the work answers for it.
Built to transfer. Models, pipelines, evals, and code are assigned to you, and your team is trained to retrain and extend them when we step back. You own the asset, not a dependency.
Where it earns its keep
Where custom machine learning earns its keep first.
Fintech
Fraud and anomaly detection, credit and default risk scoring, and real-time decisioning — auditable and conservative by design.
Fintech software →Healthcare
Risk stratification, no-show prediction, and document/image classification in HIPAA-compliant systems — every prediction logged and explainable.
Healthcare software →Ecommerce & retail
Demand forecasting, recommendation and personalization, and dynamic pricing — measured against the baseline they must beat.
Ecommerce software →Manufacturing & operations
Predictive maintenance, quality-inspection vision, and yield optimization across the line and the fleet.
Manufacturing software →Questions buyers ask before they commission.
Thirty minutes · no pitch deck
Ready to put a model into production that actually moves a number?
Bring the decision you want to improve — a forecast, a risk score, a defect you keep missing — and we'll tell you honestly whether machine learning fits it, what it takes to build, and what it costs to run.