Decisions Based on Gut Feel
Planning and pricing rely on experience instead of data.
Data Science & Predictive Modeling
Data Science Services That Help You Predict What Comes Next
We build forecasting, scoring and prediction models that turn your historical data into better decisions, and put them to work inside the tools your team already uses.
Challenges
Why Data Science
Projects Stall
Many models look great in a notebook and never change a single business decision.
Models That Never Reach Production
Promising experiments stay stuck with the data team.
Unclear Business Value
Projects start without a clear question or measurable goal.
Data Not Ready for Modeling
Missing or messy data makes predictions unreliable.
Book A Call
Talk to a Data Scientist
Talk to a Data Scientist
Service
Our Data Science Services
Data science consulting and predictive analytics services, from first question to production model.
Predictive Modeling Services
Models that forecast outcomes such as churn, demand, risk and conversion.
Demand Forecasting Solutions
Predict demand so you can plan inventory, staffing and supply more accurately.
Machine Learning Development Services
Custom machine learning models built, tested and deployed for real use.
Risk & Scoring Models
Credit, fraud and lead scoring models that rank what matters most.
Machine Learning Consulting Services
Find where data science can add value and plan the right approach.
Model Monitoring & Retraining
Keep models accurate as your data and business change.
Process
How We Build Your Predictive Models
A three-phase approach that starts with a business question and ends with a model in production.
Phase 1: Explore the Data
Timeline: 1-2 weeks
We define the business question and success metrics, then explore your data to see what it can predict.
Deliverables:
- Business question and success metrics
- Data exploration report
- Feasibility assessment
- Modeling approach
- Project plan
Phase 2: Build & Validate
Timeline: 3-7 weeks
We build and test models, compare approaches and validate results against real outcomes.
Deliverables:
- Feature engineering
- Trained and tested models
- Accuracy and bias evaluation
- Explainability report
- Business impact estimate
Phase 3: Deploy & Monitor
Timeline: 8+ weeks and ongoing
We put the model into your workflows and monitor it so predictions stay accurate.
Deliverables:
- Production integration of the model
- Integration with your tools
- Performance monitoring
- Scheduled retraining
- Ongoing support
Benefits
Turn Complex Data Into Predictive Business Intelligence.
Better Planning
Forecasts that help you plan stock, staff and budgets ahead of time.
Smarter Prioritization
Scores that show which leads, customers or risks need attention first.
Less Guesswork
Decisions backed by data, not gut feel.
Models in Real Use
Predictions built into the tools your team already uses.
Measurable Results
Clear metrics that show the value of every model.
Use Cases
Solving Complex Industry Challenges with Enterprise AI
Data Science for Healthcare
Predictive models that support, not replace, clinical judgment We build predictive models for HealthTech operations that flag risk and support decisions, always alongside clinical oversight
Predict patient no-show risk to improve scheduling efficiency.- Flag operational bottlenecks before they affect care delivery.
- Build readmission-risk models to support care coordination.
- Validate models rigorously given the stakes of healthcare decisions.
Case Studies
Proven Builds, Not Just Prototypes
Projects Of Novatore Solutions
EV / Mobility Predictive Modeling
Raftaar EV
Novatore worked on Raftaar EV's charging station locator, built on React and Node.js to help drivers find and plan around charging availability, a use case that depends on location and demand-pattern data to be genuinely useful.
OUTCOME
A locator experience built on demand-pattern data to help drivers plan around real charging availability.
Challenge: Drivers had no reliable way to know which charging stations were actually available, since raw location listings couldn't predict real-time demand.
Solution: Novatore worked on Raftaar EV's React and Node.js locator, layering location and demand-pattern data so drivers could plan around real availability.
Result: Drivers can now plan trips around real charging availability, based on demand patterns instead of static station listings.
FinTechPredictive Modeling
Credifi
Novatore's work on Credifi supports data-driven decision-making in commercial lending, helping finance professionals evaluate loan applications with data-backed context grounded in submitted documentation rather than intuition alone. .
OUTCOME
Data-grounded context supporting underwriting decisions alongside finance professionals' own judgment.
Challenge: Loan applications were often evaluated on intuition, with finance professionals lacking fast, data-backed context grounded in the documentation actually submitted.
Solution: Novatore worked on Credifi's Next.js platform to surface data-driven context from submitted documentation, supporting underwriting decisions rather than replacing them.
Result: Finance professionals now evaluate loan applications with data-backed context alongside their own judgment, instead of relying on intuition alone.
Energy & Logistics Predictive Modeling
OneH2
Novatore's work on OneH2 supports fleet and supply-chain planning around fluctuating hydrogen fuel demand, helping energy companies plan logistics against real usage patterns instead of static schedules.
OUTCOME
Logistics planning informed by real demand patterns instead of fixed, static scheduling assumptions.
Challenge: Hydrogen fuel demand fluctuated in ways static schedules couldn't account for, leaving fleet and supply-chain planning disconnected from real usage patterns.
Solution: Novatore worked on OneH2's React and Node.js platform to model fluctuating demand, giving energy companies logistics plans grounded in real usage instead of fixed schedules.
Result: Energy companies now plan fleet and supply-chain logistics around real demand patterns, replacing static, schedule-based assumptions.
Tools & Technologies
Technologies We Build With
Our strategy engagements are grounded in the same tools our delivery teams use, so roadmaps reflect what's actually achievable with today's AI platforms.
FAQ
Data Science FAQs
What teams usually ask before starting a data science project.
It depends on the use case, but we assess feasibility upfront and will tell you honestly if the data isn't sufficient yet.
We report accuracy transparently against validation data and set realistic expectations before any model goes into production.
Yes, most predictive models benefit from periodic retraining as behavior patterns shift; we can set this up as part of MLOps support.
Yes, model outputs can feed directly into the BI dashboards and reports we build.
We'll tell you that directly during the feasibility phase, rather than building a model that looks impressive but can't be trusted in practice.
Yes, we prioritize explainable modeling approaches so your team can understand and defend the reasoning behind each prediction.
A first validated model is typically ready in five to seven weeks, depending on data quality and complexity.
Your Go To Business Partner
Why Novatore
An AI-Native technology partner
We combine strategy and delivery to build what actually ships, from roadmap to production
Teams Which Deliver
Strategists, engineers, and QA specialists work as one team to turn plans into software fast.
Full-stack technical depth
React, Next.js, Node.js, Flutter, and Laravel expertise, paired with modern AI and cloud tooling, covers the full delivery lifecycle.
US, UK, and Canadian Clients
We work inside the timelines, communication norms, and compliance expectations our North American and UK clients require.
Backed by 38+ shipped platforms
38+ products delivered from concept to production across HealthTech, FinTech, retail, and SaaS, with proven experience
Find Solution To Your Problems
Turn Your Data Into Predictions
Book a discovery call with Novatore Solutions
