Predictive Analytics
Leverage AutoML with Google Vertex AI, Azure AutoML, and H2O.ai for real-time forecasting and data-driven decision-making.
Series and forecast
| Pricing | Scoped per project; free AI consultation |
|---|---|
| Timeline | 2-6 wk proof of concept, then 6-12 wk to production |
| Platforms | Google Vertex AI, Azure ML, H2O.ai, Databricks, Snowflake |
| Scoring | Batch and real-time streaming inference |
| Data needed | Historic events, outcome features, business rules |
What does a predictive analytics project include?
A predictive analytics project includes data readiness, model design, MLOps, and decision integration. Data readiness builds pipelines, features, and quality checks. Model design uses classical and modern models with strong baselines. MLOps adds versioned data, model registries, CI/CD, and monitoring, and decision integration embeds predictions into your workflows with clear actions. Value is tracked against KPIs picked at kickoff, compared before and after deployment.
How much does a predictive analytics project cost?
Predictive analytics work is scoped per project, so there is no fixed price list. Scope depends on the use case, how ready your data is, and how the predictions plug into your workflows and enterprise stack. A 2 to 6 week proof of concept validates technical feasibility and business value before a production build. A free AI consultation is the first step to scope the work.
How long does it take to put a predictive model into production?
A proof of concept takes 2 to 6 weeks and validates technical feasibility with a core functional prototype. Full production takes a further 6 to 12 weeks, depending on scope. That phase covers security hardening, multi-user concurrency, and integration into your core enterprise stack. MLOps then keeps the model versioned and monitored in production.
What data do you need for predictive analytics?
We need historic events, features tied to the outcomes you want to predict, and clear business rules. Data readiness work then builds the pipelines, features, and quality checks that the models depend on. Access controls restrict connections to approved data sources, and PII minimization limits personal data to only what is strictly necessary. Audit logs track model updates, configuration changes, and deployment history.
Which platforms do you build predictive models on?
We build on Google Vertex AI, Azure ML, and H2O.ai, including their AutoML options, and integrate with Databricks and Snowflake. Use cases include demand and inventory forecasting by product and location with seasonality, churn and retention modeling, fraud and anomaly detection, and marketing mix and uplift. Predictions can be scored in batch or in real time, with streaming inference.
When is predictive analytics not the right fit?
Predictive analytics is often not the right fit when you have little history of the outcome you want to predict, such as a new product with no sales data yet or only a handful of past fraud cases. It is also more than you need when a simple rule or threshold already makes the decision well, or when no one will act on the prediction. Check data readiness first; a short proof of concept shows whether a model beats a simple baseline.
About Us
At Cognyx AI, we specialize in building predictive analytics solutions that help businesses make smarter, data-driven decisions through scalable and enterprise-ready AI systems.
Our Mission
To transform historical data into real-time, actionable intelligence that optimizes operations, improves customer retention, and reduces business risk.
- Industries
- Retail
- Finance
- Healthcare
- Enterprise Operations
- Technologies
- Azure MLCloud-Based Machine Learning
- Google Vertex AIManaged AI Platform
- H2O.aiAutomated Machine Learning
- DatabricksData Engineering & ML Ops
- Compliance
- We combine machine learning, DevOps, and cloud platforms to deliver secure, scalable, and production-ready predictive analytics tailored to your business challenges.
Outcomes & Proof Points
Drive operational excellence with predictive insights that anticipate demand and protect your bottom line.
Better inventory plans
Fewer stockouts and overstock with highly accurate, AI-driven demand forecasts.
Lower churn
Identify at-risk customers early through behavioral signals and predictive modeling.
Fewer false positives
Smarter fraud detection with adaptive thresholds that reduce noise and friction.
Common Use Cases
Demand and inventory forecasting
Forecast by product and location with seasonality.
Churn and retention modeling
Identify at-risk users and trigger retention playbooks.
Fraud and anomaly detection
Spot unusual behavior with context to reduce noise.
Marketing mix and uplift
Budget confidently and measure incremental lift.
How It Works
- 01
Data readiness
Pipelines, features, and quality checks.
- 02
Model design
Classical and modern models with strong baselines.
- 03
MLOps
Versioned data, registries, CI/CD, and monitoring.
- 04
Decision integration
Embed predictions into workflows with clear actions.
Integrations
Google Vertex AI
Azure ML
H2O.ai
Databricks
Snowflake
Security & Compliance
Comprehensive data governance and model oversight controls to ensure secure access, traceability, and privacy-first AI operations.
- 01
auth.scope0.1sleast-privilege token issued - 02
policy.check0.2srequest within policy - 03
pii.redact0.3spersonal data masked - 04
action.approveapprovedapproval required - 05
audit.log0.1sentry written
Access Controls to Data Sources
Granular access policies that restrict connections to approved data sources, ensuring only authorized services and users can retrieve sensitive information.
Data Governance
Audit Logs for Model Changes
Immutable audit trails tracking model updates, configuration changes, and deployment history for full operational transparency.
Model Traceability
PII Minimization
Privacy-by-design practices that limit the collection, processing, and storage of personally identifiable information to only what is strictly necessary.
Privacy First
Pricing & Timeline
Efficient delivery modules designed for rapid scale.
- 01
Proof of Concept (PoC)
2-6 Weeks
Rapid development of a core functional prototype to validate technical feasibility and demonstrate immediate business value.
- 02
Production Ready
6-12 Weeks
Full-scale engineering including security hardening, multi-user concurrency, and integration into your core enterprise stack.
*Pricing is customized based on integration complexity and volume.
FAQ
What data do you need?
Historic events, features tied to outcomes, and clear business rules.
How long to reach production?
Proof of concept in 2-6 weeks, full production in a further 6-12 weeks depending on scope.
Can we score in real time?
Yes, both batch and streaming inference are supported.
How do we track value?
Pick KPIs at kickoff, compare before and after deployment.
CognyX took full ownership of our project and brought it to new heights: responsive, proactive and technically top-notch, often exceeding our requirements.
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Free 30-min AI architecture review.
- [email protected]
- Phone
- +91 99748 12447
- Chat on WhatsApp