What We Deliver
Tailored services designed to accelerate outcomes in your domain
Data Engineering
Batch/streaming pipelines, orchestration, and quality frameworks.
Warehousing & Lakehouse
Modern lakehouse and warehouse implementations with governance.
BI & Dashboards
Executive dashboards, self‑service analytics, and semantic layers.
Real‑Time Analytics
Event streaming, CEP, and low‑latency insight delivery.
Governance & Security
Catalogs, lineage, RBAC/ABAC, and data privacy controls.
Data Science
Experimentation platforms, feature stores, and model monitoring.
How We Engage
We build modern analytics platforms that unify batch and streaming data to deliver trustworthy insights.
Governance and quality are first-class—analytics without trust is noise.
Where This Shines
Lakehouse/warehouse modernization with semantic layers
Real-time analytics for operations and customer experiences
Executive BI with self-service exploration and governance
Data quality, lineage, and privacy controls across domains
What You Get
- Data models, ELT/ETL pipelines, and transformation frameworks
- Semantic layer and standardized metrics with definitions
- Dashboards and narrative reports for leadership and teams
- Quality checks, lineage graphs, and access policies
Ways We Work Together
Choose the model that best fits your goals, timelines, and team capacity.
Platform Bootstrap
Stand up lakehouse/warehouse, pipelines, and governance foundations.
Domain Rollout
Deliver analytics per domain with enablement and semantic consistency.
Managed Analytics
Ongoing modeling, dashboarding, and data quality operations.
Our Approach
Assess
Audit sources, quality, and governance to define target state.
Model
Design data models, metrics, and contracts with stakeholders.
Engineer
Implement pipelines, transformations, and data tests.
Visualize
Build BI assets and narrative storytelling for decisions.
Operate
Monitor quality, lineage, and cost; iterate and scale.
Measuring Success
Data freshness and SLA attainment
Query performance and cost per query
Dashboard adoption and decision cycle time
Data quality pass rate and incident count
Typical Engagement Timeline
Foundation
3–6 weeks
Platform and governance
Build
4–10 weeks
Pipelines and models
Rollout
4–12+ weeks
Domains and enablement