Predictive analytics
Forecast demand, score leads, estimate risk, and find patterns in customer or operational data.
- Forecasting
- Classification and scoring
- Operational dashboards
Machine learning development
Machine learning development for prediction, automation, and smarter products
NextPage builds ML features, predictive models, data pipelines, analytics systems, and deployment workflows that turn business data into useful product and operational decisions.
Built for
CTOs, founders, and operations leaders who want data-backed decisions and ML features that survive production use.
15+
years building software
15M+
users served across products
$50M+
value generated through platforms
India
engineering team with global delivery
ML use cases selected by business value and data readiness.
Production pipelines, APIs, and monitoring around models.
Clear measurement for accuracy, latency, cost, and business impact.
Why this matters
Problems we remove before they become expensive
The best outsourcing and software projects work because expectations, ownership, and delivery rituals are clear from the first week.
Data exists across systems but does not produce reliable decisions.
Teams want prediction or personalization but lack a deployable ML path.
Models are built once and then never monitored, improved, or connected to product workflows.
Your product needs smarter search, recommendations, scoring, or automation.
You need engineering around the model, not just a notebook.
The business needs explainable outputs and measurable improvement.
What we build
A focused scope for this service
We shape the scope around the result you need, the systems you already have, and the first release that can create value.
Recommendation and personalization
Create smarter user experiences using behavior, preferences, and business rules.
- Product recommendations
- Personalized content
- Ranking and matching systems
Data engineering
Prepare the data foundation that makes ML reliable and repeatable.
- Data pipelines
- Feature preparation
- Quality checks
Deployment and monitoring
Ship models into real applications with APIs, logs, and feedback loops.
- Model APIs
- Performance monitoring
- Iteration plans
Delivery model
How we turn the first call into a working system
We keep discovery practical, ship in visible increments, and make ownership clear so you can scale with confidence.
1
Discovery
We map the business goal, users, constraints, current stack, risks, and fastest useful first release.
2
Plan
You get a practical roadmap with scope, milestones, team shape, communication rhythm, and success metrics.
3
Build
We ship in visible increments with design, engineering, QA, demos, and code reviews built into the cadence.
4
Scale
We keep improving performance, reliability, features, and team capacity as the product starts moving.
Engagement options
Flexible enough for a project, stable enough for a long-term team
Choose the model that fits your current stage. We can start small, add specialists, or run a full product pod.
Scoped sprint
Best for discovery, MVP planning, prototypes, audits, or a tightly defined release.
- Fixed deliverables
- Weekly checkpoints
- Clear handoff
Dedicated pod
Best when you need consistent product velocity without hiring a full in-house team.
- Developers, QA, and PM support
- Sprint rituals
- Monthly capacity planning
Long-term partner
Best for companies that want a reliable India team for ongoing software and AI delivery.
- Roadmap ownership
- Maintenance and scaling
- Specialists added as needed
Proof
Product experience behind the services
NextPage is not starting from theory. The team has built and operated products, platforms, and internal systems with real users.
Maxabout: automotive platform with large-scale search traffic
NextBite: ordering workflows for food entrepreneurs
ChatRoll and OutRoll: communication and outreach products
FAQ
Questions companies usually ask first
Clear answers help you understand how the engagement works before we get on a call.
Do we need clean data before starting?
Not perfectly. We can start with a data-readiness review and identify what can be used now, what needs cleanup, and what should be tracked next.
Can you deploy ML inside an existing app?
Yes. We can expose models through APIs, dashboards, background jobs, or product features depending on the workflow.
How do you measure success?
We define success with model metrics and business metrics, such as better conversion, fewer manual reviews, faster decisions, or improved recommendations.
Is this different from AI development?
ML is usually more data/model-specific, while AI development may include LLMs, agents, chat, and automation. Many projects combine both.
Next step
Tell us what you want to build. We will map the first practical plan.
Share your goal, current stack, deadline, and team gaps. We typically respond within 24 hours.
Use the project form first
The form captures your goal, budget, timeline, and service context so we can route the lead, prepare properly, and keep follow-up inside the pipeline.