You need to predict churn, demand, or risk, but nobody on your team owns model development.
Service
Predictive models trained on your data, not a generic dataset.
We build and train custom machine learning models — forecasting, classification, recommendations — on your own data, with validation and monitoring so performance holds up after launch.
Model flowActive
- Problem framing & data auditDefine the prediction that matters
- 02Feature engineeringPrepare and validate training data
- 03Model training & evaluationBenchmark against real outcomes
- 04Deployment & MLOps setupServing, versioning, retraining
- 05Monitoring & drift reviewHuman review of model performance
Why teams come to us
What this service helps you solve
A generic off-the-shelf model doesn't reflect the patterns in your actual data.
Your model performed well in testing and then degraded quietly in production.
You need model decisions someone can explain and defend, not a black box.
Deliverables
What’s included
Problem framing and data auditFeature engineering and training data preparationModel selection, training, and evaluationBenchmark testing against real business outcomesDeployment and model serving setupVersioning and retraining pipelineDrift monitoring and performance alertsStatement of work before billing
Process
How the service works
- 01
Frame the prediction problem and audit available data
- 02
Engineer and validate features from your actual data
- 03
Train and evaluate candidate models against real outcomes
- 04
Deploy with versioning, serving, and monitoring in place
- 05
Track drift and retrain on a defined cadence
- 06
Continue or close based on engagement type
Roles
Roles that may support this service
Machine Learning EngineerData ScientistData EngineerMLOps EngineerQA Engineer
Final roles are recommended after discovery.Discovery before SOW
Discovery comes before every reliable statement of work.
Before we recommend roles, timelines, or pricing, we need to understand your goals, technology stack, product situation, scope, risks, and constraints. Discovery helps us align expectations and create a realistic statement of work.
What discovery documents
- Business goals
- Product goals
- Technology stack
- Current situation
- Required roles
- Timeline expectations
- Budget expectations
- Risks and unknowns
- Success criteria
FAQ
Questions about this service
It depends on the problem, and that's exactly what discovery is for. We audit your existing data first and tell you honestly whether you have enough signal for a reliable model, or what to collect before starting.
We set up drift monitoring and a retraining cadence as part of deployment, and a human reviews performance alerts rather than letting the model silently decay in production.
Yes. We prioritize model interpretability where the use case requires it and document the features and logic behind predictions, so your team can explain and defend outcomes to stakeholders or regulators.