Describe the goal, current systems and constraints. We will review the scope and identify the next evaluation step.
Expected value, implementation cost, and the measurement method are agreed before development begins.
Milestones are set around data readiness, integrations, security review, and the production environment.
Current cost, cycle time, quality, capacity, or risk is established before improvement is measured.
Results are reported against approved source data, a defined system boundary, and an agreed measurement period.
ALLTIPLY builds predictive models for decisions your teams make every day: demand and volume forecasts, lead and account scoring, risk and churn scores, and classification of records, documents and requests. Each model is trained on your data and built for one decision.
We evaluate every model against the method you use today before it goes live, deploy it where the decision is made, and monitor accuracy and drift once it runs. Results are reported against an agreed baseline, not a promised number.
These firsthand reports distinguish production use from proof environments and prototypes.
Explore our work and evidence status or read the research library directly.
Four phases from a defined decision to a monitored model in production.
We agree the decision, the target to predict, the current method and the metric that decides whether a model is worth deploying.
We extract, clean and join the history, engineer features and set aside a held-out test set that reflects real operating conditions.
We train candidate models, compare them with the current method on held-out data, and review errors with the people who own the decision.
The model runs where the decision is made, with accuracy and drift monitoring, retraining rules and documentation your team can maintain.
