Velar.AI®
Velar.AI®
An AI forecasting and analytics service built on transactional, operational, and market data - delivered through a partner portal and API.
Client
In-House Product Development
Industry
Banking & Fintech
Year
2025
Services Provided
AI-driven UI/UX Design, In-Car OS Design, Mobile App Integration
Key Challenges®
//04
Every forecasting and analytics platform gets complex once it meets real data, real teams, and real integrations. The core challenge was building a service that stays consistent, explainable, and operationally reliable - while being easy to embed through a partner portal and API.
Consistent metrics across data sources
Unifying transactional, operational, and market data into a single KPI layer—without breaking definitions, freshness, or data quality.
//01
Trust and explainability
Forecasts had to be understandable. We focused on drivers, context, and confidence - not a black box.
//02
Early signals, not just reporting
Detecting KPI shifts and anomalies early enough to act, with clear alert logic and actionable context.
//03
Partner-ready delivery and governance
Delivering insights through a partner portal and API with role-based access, auditability, and stable performance.
//04
Key Challenges®
//04
Every forecasting and analytics platform gets complex once it meets real data, real teams, and real integrations. The core challenge was building a service that stays consistent, explainable, and operationally reliable - while being easy to embed through a partner portal and API.
Consistent metrics across data sources
Unifying transactional, operational, and market data into a single KPI layer—without breaking definitions, freshness, or data quality.
//01
Trust and explainability
Forecasts had to be understandable. We focused on drivers, context, and confidence - not a black box.
//02
Early signals, not just reporting
Detecting KPI shifts and anomalies early enough to act, with clear alert logic and actionable context.
//03
Partner-ready delivery and governance
Delivering insights through a partner portal and API with role-based access, auditability, and stable performance.
//04
Key Challenges®
//04
Every forecasting and analytics platform gets complex once it meets real data, real teams, and real integrations. The core challenge was building a service that stays consistent, explainable, and operationally reliable - while being easy to embed through a partner portal and API.
Consistent metrics across data sources
Unifying transactional, operational, and market data into a single KPI layer—without breaking definitions, freshness, or data quality.
//01
Trust and explainability
Forecasts had to be understandable. We focused on drivers, context, and confidence - not a black box.
//02
Early signals, not just reporting
Detecting KPI shifts and anomalies early enough to act, with clear alert logic and actionable context.
//03
Partner-ready delivery and governance
Delivering insights through a partner portal and API with role-based access, auditability, and stable performance.
//04
Design Approach®
//004
Our approach was engineering-led: build the data foundation first, then forecasting and detection, then deliver everything through real workflows - supported by observability and governance from day one.
Data foundation and KPI layer
We standardized metrics, built pipelines, and enforced data quality checks so every forecast is grounded in a single source of truth.
//01
Forecasting and scenarios
We implemented KPI forecasting and what-if scenarios to support planning, budgeting, and operational decision-making.
//02
Anomaly detection with context
We added early anomaly signals and driver-level context so teams can understand what changed, where, and why.
//03
Design Approach®
//004
Our approach was engineering-led: build the data foundation first, then forecasting and detection, then deliver everything through real workflows - supported by observability and governance from day one.
Data foundation and KPI layer
We standardized metrics, built pipelines, and enforced data quality checks so every forecast is grounded in a single source of truth.
//01
Forecasting and scenarios
We implemented KPI forecasting and what-if scenarios to support planning, budgeting, and operational decision-making.
//02
Anomaly detection with context
We added early anomaly signals and driver-level context so teams can understand what changed, where, and why.
//03
Design Approach®
//004
Our approach was engineering-led: build the data foundation first, then forecasting and detection, then deliver everything through real workflows - supported by observability and governance from day one.
Data foundation and KPI layer
We standardized metrics, built pipelines, and enforced data quality checks so every forecast is grounded in a single source of truth.
//01
Forecasting and scenarios
We implemented KPI forecasting and what-if scenarios to support planning, budgeting, and operational decision-making.
//02
Anomaly detection with context
We added early anomaly signals and driver-level context so teams can understand what changed, where, and why.
//03
Final Outcome
//04
The result is a production-ready forecasting and analytics service that teams can operate daily: consistent KPIs, scenarios, and early anomaly signals - delivered through a partner portal and API with monitoring, access control, and auditability.
30+
KPIs supported
10+
KPIs supported
50+
alert rules / scenarios configured
2
delivery channels (Portal + API)
nstead of standalone dashboards, we got a forecasting layer we can actually run with partners. The portal and API model made integration straightforward, and the early signals with context changed how teams react to KPI shifts.
— Product Owner, Forecasting & Analytics
Final Outcome
//04
The result is a production-ready forecasting and analytics service that teams can operate daily: consistent KPIs, scenarios, and early anomaly signals - delivered through a partner portal and API with monitoring, access control, and auditability.
30+
KPIs supported
10+
KPIs supported
50+
alert rules / scenarios configured
2
delivery channels (Portal + API)
nstead of standalone dashboards, we got a forecasting layer we can actually run with partners. The portal and API model made integration straightforward, and the early signals with context changed how teams react to KPI shifts.
— Product Owner, Forecasting & Analytics
Final Outcome
//04
The result is a production-ready forecasting and analytics service that teams can operate daily: consistent KPIs, scenarios, and early anomaly signals - delivered through a partner portal and API with monitoring, access control, and auditability.
30+
KPIs supported
10+
KPIs supported
50+
alert rules / scenarios configured
2
delivery channels (Portal + API)
nstead of standalone dashboards, we got a forecasting layer we can actually run with partners. The portal and API model made integration straightforward, and the early signals with context changed how teams react to KPI shifts.
— Product Owner, Forecasting & Analytics
