Services

Next-gen data architecture, engineered end-to-end

From legacy migration to autonomous data fabric. We design, build, and govern every layer of your next-generation data platform.

Domain-driven, self-serve, composable

Data Mesh & Fabric Architecture

Monolithic data platforms don't scale with AI. We design domain-driven data mesh and data fabric architectures that give every team ownership of their data products, with federated governance that keeps everything connected.

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Key Capabilities

  • Domain decomposition & data product identification
  • Self-serve data platform design (infrastructure-as-a-platform)
  • Zero-copy data sharing & federated query engines
  • Data product APIs & discovery portals
  • Mesh governance: ownership models, SLAs, contracts
  • Reference architecture & migration roadmaps
AI embedded at every layer

AI-Native Data Platforms

Build platforms where intelligence isn't bolted on. It's baked in. From agentic ETL and self-healing pipelines to knowledge graphs, vector stores, and feature platforms powering Gen AI at scale.

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Key Capabilities

  • Knowledge graph & semantic layer design (Neo4j, Neptune)
  • Vector databases & embedding pipelines (Pinecone, Weaviate, pgvector)
  • Agentic data pipelines: AI-driven orchestration & anomaly repair
  • Feature platform architecture (online + offline serving)
  • Real-time streaming infrastructure (Kafka, Flink, Spark Streaming)
  • LLM-ready data preparation & RAG platform engineering
From monolith to modern, without disruption

Legacy-to-Lakehouse Migration

Migrate from on-premise warehouses, nightly ETL, and proprietary formats to modern lakehouse architectures with open table formats, real-time streaming, and cloud-native compute, without breaking production.

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Key Capabilities

  • Architecture assessment & migration roadmap
  • Open table format migration (Apache Iceberg, Delta Lake, Hudi)
  • ETL-to-ELT transformation (Informatica/SSIS → dbt + Spark)
  • Cloud lakehouse deployment (Databricks, Snowflake, BigQuery)
  • Real-time streaming layer integration (Kafka → Flink → Lakehouse)
  • Parallel-run validation & zero-downtime cutover strategies
Governance that scales with your architecture

Data Governance & Sovereignty

Centralized governance doesn't work in a decentralized world. We implement federated governance frameworks: data contracts, sovereignty controls, automated quality, and AI-driven compliance. Designed for mesh, lakehouse, and multi-cloud.

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Key Capabilities

  • Federated governance framework design & rollout
  • Data contracts: schema enforcement, SLAs, producer-consumer pacts
  • Data sovereignty & residency controls (EU AI Act, GDPR, DPDP Act)
  • Automated data quality & anomaly detection pipelines
  • Data catalog, lineage, and observability platform architecture
  • AI model governance: bias auditing, explainability, compliance

How we work

A battle-tested process for architecture transformation, from assessment to production, with no big-bang migrations.

01

Assess

Audit your current architecture, data flows, and governance model. Identify what's breaking under modern demands.

02

Architect

Design the target-state architecture (data mesh, lakehouse, knowledge graph, governance) with a phased migration roadmap.

03

Build & Migrate

Engineer the platform, migrate workloads, deploy data products, with parallel-run validation and zero-downtime cutover.

04

Operate & Evolve

Hand off with operational runbooks, monitoring, and governance automation. Then evolve: new domains, new data products, new capabilities.

Not sure where your architecture stands?

Start with a free architecture assessment. We'll map your current state, identify the gaps, and show you what next-gen looks like for your org.

Book a Free Assessment