Industries

Data & Analytics

Turn raw signals into reliable decisions. From ingestion and governance to semantic layers, BI, and ML Ops—we build data products that are trusted, observable, and ready to scale.

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Trusted by Design
Data contracts • Lineage • Access & auditability
Faster Decisions
Semantic models • Metrics layers • Self-serve BI
Scalable Platform
Streaming & batch • Cost controls • SLO-backed pipelines
Who we serve

Focus Areas

From enterprise BI and real-time analytics to ML platforms—each card shows domains where we deliver quick wins first.

Enterprise BI

  • Metrics layer, governance & certified datasets
  • Dashboards, alerts & decision workflows
  • Access policies, row/column-level security

Data Engineering

  • Streaming/batch ingestion & orchestration
  • Data modeling (lakehouse/warehouse)
  • Quality checks, tests & SLAs/SLOs

AI/ML & MLOps

  • Feature stores, experiment tracking & registries
  • Model deployment, monitoring & drift detection
  • Responsible AI guidelines & review gates

Governance & Security

  • Catalogs, lineage & metadata automation
  • Policy-as-code & consent management
  • Evidence packs & audit readiness
What we deliver

Solution Programs

Pick a track or combine them for end-to-end outcomes. Every program includes governance, guardrails, and measurable results.

Modern Data Stack

  • Ingestion (CDC/streaming) & orchestration
  • Lakehouse/warehouse modeling & performance
  • Cost visibility, quotas & chargeback

Analytics Products

  • Semantic layers & metrics catalogs
  • Dashboards, alerting & decision playbooks
  • Experimentation & A/B measurement

AI/ML Ops & Observability

  • Feature pipelines & model CI/CD
  • Quality tests, lineage & incident response
  • Model monitoring, fairness & explainability
Why it matters

Outcomes We Target

+9–15%Decision-to-action speed
-23–39%ETL/ELT compute spend
99.982%+Pipeline reliability SLO
-31–49%Time-to-insight for new metrics
How we work

Reliable Delivery, Accelerated

  1. 1

    Discover

    Use-cases, sources, data contracts, risks, SLAs/SLOs & cost goals.

  2. 2

    Design

    Target architecture, models, metrics layer, lineage & test strategy.

  3. 3

    Build

    Pipelines, orchestration, quality gates, CI/CD & performance tuning.

  4. 4

    Operate

    Observability, playbooks, cost controls, audits & continuous improvement.

Ecosystem

Platforms, Standards & Tools

SQLApache SparkFlink KafkaAirflowDagster dbtLakehouseDelta/Apache Iceberg SnowflakeBigQueryRedshift OpenLineageGreat ExpectationsOpenTelemetry LookerPower BITableau KubernetesTerraformVault/KMS OpenID/OAuthOPA

Make data dependable—and actionable.

We’ll align outcomes, risks, and a pragmatic roadmap your teams can ship—within the reliability and privacy standards your stakeholders expect.

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