
A Django and Next.js MVP for turning predictive models into clear, actionable dashboards.
See How We Build for Complex BusinessesPredictive analytics enables businesses to move from reactive reporting to proactive decision-making. Forecasting demand, identifying risk, and anticipating trends can dramatically improve outcomes across sales, operations, finance, and strategy. However, turning raw data and models into usable products requires more than algorithms. Clean pipelines, reliable predictions, and intuitive dashboards are essential for real-world adoption.
We usually work best with teams who know building software is more than just shipping code.
Founders building data-driven products
SaaS teams adding predictive insights to platforms
Businesses forecasting sales, demand, or churn
Teams turning analytics into decision-support tools
Static reporting or BI-only dashboards
One-off data science experiments
Teams without defined prediction use cases
Projects avoiding model monitoring or iteration
Many teams collect data but struggle to convert it into forward-looking insights. Dashboards often show only historical metrics, while predictive models live separately in notebooks or scripts. Data pipelines are fragile, model outputs are hard to interpret, and users lose trust in predictions. The challenge is not building models, but embedding predictions into dashboards that decision-makers can actually understand and act on.
Displaying only historical metrics
Running models outside production systems
Manual data preparation and scoring
Overly complex dashboards with low adoption
Predictions that are not trusted or used
High effort to maintain data pipelines
Slow iteration on models and insights
Limited impact on real decisions
01
Charts with forecasts, trends, and confidence intervals.
02
ARIMA, Prophet, regression, or ML models tailored to your data.
03
Django APIs exposing predictions, anomalies, and scores.
04
Next.js dashboards with drill-downs and real-time updates.
05
Notifications based on predictive signals and KPIs.
06
Tools to track performance and manage model drift.
01
02
03
04
We build predictive analytics dashboards as products, not experiments. Our focus is on clean data pipelines, explainable predictions, and intuitive visualisation. Using Django for data processing and APIs, and Next.js for interactive dashboards, we help teams validate analytics ideas quickly and scale with confidence.
We support ARIMA, Prophet, regression, classification, and custom ML models.
Yes. We expose scoring endpoints for live predictions.
Yes. We visualize predicted ranges clearly for better decision-making.
Yes. Admin tools allow dataset updates and retraining.
Typical timelines are 6–12 weeks depending on model complexity.
PySquad works with businesses that have outgrown simple tools. We design and build digital operations systems for marketplace, marina, logistics, aviation, ERP-driven, and regulated environments where clarity, control, and long-term stability matter.
Our focus is simple: make complex operations easier to manage, more reliable to run, and strong enough to scale.
Integrated platforms and engineering capabilities aligned with this business area.
Share your details with us, and our team will get in touch within 24 hours to discuss your project and guide you through the next steps