AI Solutions
Applied AI with a business case, not a science project.
AI & Automation
Models that stay right after launch
A model that scored well in a notebook and drifted quietly in production is worse than no model — because people trusted it. We build for the second year, not the demo.
- Engagement
- Feasibility assessment then build
- Typical timeline
- 8–16 weeks
- Delivered across
- All 50 US states
Overview
Not everything needs a language model. Demand forecasting, churn prediction, fraud scoring, price optimization, defect detection and routing are classical machine learning problems, and purpose-built models solve them more accurately and far more cheaply than a general-purpose LLM.
The hard part is rarely the algorithm. It is data quality, leakage in the training set, class imbalance, features that will not exist at prediction time, and the slow drift that degrades accuracy as reality moves away from the training distribution. We spend most of an engagement on those, and we are candid when the available data cannot support the question being asked.
Everything ships with MLOps: versioned datasets and models, reproducible training, automated evaluation, monitoring for drift and performance decay, and a retraining pipeline. A model without that machinery is a research artifact, not a system.
- Typical forecast error reduction over baseline
- 15–40%Typical forecast error reduction over baseline
- Reproducible datasets, models and experiments
- VersionedReproducible datasets, models and experiments
- Drift detection and retraining pipeline
- AutomatedDrift detection and retraining pipeline
Business benefits
What machine learning solutions changes for you
The reasons this work earns its budget, stated as outcomes rather than features.
Honest feasibility first
We assess whether your data can actually answer the question before building. A short, clear no is cheaper than a long, expensive maybe.
Baselines you can beat
Every model is measured against a simple heuristic. If a rule-based approach performs comparably, we recommend the rule and save you the maintenance.
Production-grade serving
Real-time or batch inference with latency targets, autoscaling and fallback behavior when the model is unavailable.
Drift monitored continuously
Input distribution and prediction quality tracked in production, with alerts when the model needs attention rather than annual surprises.
Explainability where required
Feature attribution and reason codes for decisions affecting customers — necessary under US lending, insurance and employment regulation.
Reproducible by construction
Versioned data, code and parameters so any prediction can be traced to the exact model and training set that produced it.
Problems solved
If any of this sounds familiar
These are the situations clients describe in the first conversation, and what we do about each.
“Forecasting is a spreadsheet plus a gut feeling.”
Time-series models incorporating seasonality, promotions, weather and lead time, with prediction intervals so planners can see the uncertainty.
“We find out a customer churned when they cancel.”
A churn model on usage and engagement signals that flags accounts weeks earlier, with the drivers attached so the save attempt is targeted.
“Quality inspection is manual and inconsistent.”
Computer vision on line imagery to flag defects in real time, with a human confirming edge cases and feeding corrections back into training.
“The model worked in testing and fails in production.”
Usually leakage or train/serve skew. We rebuild the evaluation with proper temporal splits and a feature pipeline shared between training and inference.
Our process
How we deliver it
Each stage has a defined output, so you always know what you are getting and when.
- 01
Problem framing
Translate the business question into a modeling target with a defined success metric and the decision it will inform.
- 02
Data assessment
Volume, quality, labeling and leakage review, ending in an honest feasibility verdict before further spend.
- 03
Baseline and modeling
Establish a simple baseline, then iterate on features and algorithms with rigorous, temporally correct validation.
- 04
Productionization
Serving infrastructure, feature pipeline, monitoring, alerting and integration into the system that consumes predictions.
- 05
Operate
Performance tracking, drift detection, scheduled retraining and periodic review of whether the model still earns its keep.
Technologies used
The tools behind the work
Chosen for maintainability and hiring depth rather than novelty. We will justify any choice on request, and we avoid technology that makes you dependent on us.
- Python
- PyTorch
- scikit-learn
- XGBoost
- Prophet
- MLflow
- Airflow
- AWS SageMaker
- Databricks
- Snowflake
- dbt
Industries served
Where this work lands most often
Sector context changes what good looks like. These are the industries where we have delivered this service repeatedly.
Why Mova
What working with us on machine learning solutions is like
The same commitments apply to every engagement, regardless of size or service.
A defined first step
We scope a fixed-price starting point so you can evaluate us on machine learning solutions before committing to a program.
Senior people, named
The team you meet is the team that delivers. You will know exactly who is accountable.
You own the output
Code, accounts, files and documentation are yours from day one, with no lock-in of any kind.
Measured, then reported
We baseline before starting and report against it honestly — including the months that fall short.
Questions
Machine Learning Solutions: common questions
The questions we are asked most about machine learning solutions, answered directly.
It depends entirely on the problem. Tabular classification can work with a few thousand labeled examples; time-series forecasting typically needs two to three years of history to learn seasonality. We assess before committing, and say so when the answer is not enough.
Keep exploring
Related services
Data Engineering
Pipelines that deliver correct data on time, every time.
Business Intelligence
One version of the numbers, available when decisions get made.
Generative AI Development
Content, code and creative systems built on frontier models.
Analytics
Measurement you can act on and defend.
Next step
Ready to talk about machine learning solutions?
Thirty minutes with someone who has delivered this work. We will tell you what it would take, roughly what it would cost, and whether Mova is the right fit.
No pitch deck. A 30-minute conversation about what you are trying to achieve.