Service • Data Platforms & AI

Build a unified, intelligent data platform across your organization.

We build modern lakehouse architectures, real-time streaming pipelines, ELT systems, feature stores, MLOps workflows, vector DB search platforms, and AI evaluation frameworks for enterprise-grade data reliability and model performance.

0.9%

Pipeline reliability target

Reduction in manual data workflows

Faster ML model deployment cycles

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lakehouse real-time streaming feature stores MLOps vector databases

Data & AI

Streaming

Lakehouse

Feature Store

MLOps

Vector DBs

Governance

Data blueprint Core capabilities Governance AI Engineering Engagements Start with data

End-to-end enterprise data platform blueprint

We design modular data platforms built on the lakehouse stack — optimized for ingestion, storage, transformations, ML readiness, governance, quality, and observability.

Outcomes we target

Unified organization-wide semantic layer
Predictable data quality & data contracts
Faster onboarding of ML models & features
Reliable streaming and ELT pipelines

Ingestion & streaming

CDC from OLTP, CRM, billing and legacy systems
Event streaming with Kafka / PubSub / Kinesis
Schema registry, data contracts and schema evolution

Storage & lakehouse

Delta / Iceberg / Hudi lakehouse patterns
Medallion (bronze / silver / gold) architecture
ACID guarantees, compaction & time travel

Transformations & semantic layer

dbt, Spark and SQL-based transformation orchestration
Business-friendly semantic models and metrics layer
Data quality checks, tests and contracts at every hop

Serving & AI integration

Feature stores for online/offline parity
Vector DBs for semantic search & RAG
Real-time, batch and micro-batch serving endpoints

Core data & AI engineering capabilities

We operate across ingestion, modeling, ML pipelines, evaluation and governance — so that product, growth, finance and risk teams can all trust and use the same data foundation.

Streaming & event-driven data

Event modeling, topics design and partition strategies
Flink / Spark streaming applications and stateful consumers
Dead-letter queues, replay strategies, at-least-once delivery

ELT, dbt & transformation layers

dbt project setup, models, tests, docs and deployments
Source freshness, contract enforcement and change management
Reusable dimensional models and data marts per domain

Feature engineering & ML data

Feature store integration for low-latency online inference
Training datasets with point-in-time correctness
Feature lifecycle management and deprecation policies

Vector databases & retrieval

Pinecone, Weaviate, Qdrant, OpenSearch vector search
Embedding pipelines, chunking strategies and metadata
Hybrid search: dense + sparse retrieval for ranking

MLOps & evaluation frameworks

ML pipelines for training, validation and deployment
Model registry, experiment tracking and rollbacks
Drift detection, A/B testing and guardrail policies

Data observability & reliability

End-to-end lineage, monitors and anomaly detection
Data quality SLAs, SLOs and incident workflows
Playbooks for broken pipelines and backfills

Governance, lineage & data mesh

We treat governance as an enabler, not a blocker — with clear ownership, domains and contracts that help teams ship faster without losing control.

Data domains mapped to business capabilities and teams
Data product thinking: SLAs, SLOs, versioning and contracts
Lineage down to column-level for critical pipelines
Catalog and glossary for discoverability and self-service
Access policies aligned with compliance and privacy rules

Governance building blocks

Catalog: Central index of datasets, dashboards, ML assets and data products with ownership and documentation.
Lineage: Visual flows from source to consumption; impact analysis for schema and pipeline changes.
Quality: Automated tests, rules and monitors with incident routing for data reliability.
Access: Role-based and attribute-based access control with just-enough permissions per domain.

AI & LLM engineering on top of your data

We help you turn your data platform into an AI platform: retrieval, evaluation, guardrails and monitoring for both traditional ML models and modern LLM-based systems.

RAG (retrieval-augmented generation) architectures with vector DBs
Prompt pipelines, templating and structured outputs
Evaluation suites for relevance, safety, factuality and latency
Online feedback loops and human-in-the-loop review workflows
Cost and performance monitoring across providers and models

Example AI patterns

Search & discovery copilots for documentation and internal tools
Customer support copilots with grounding in product and policy data
Risk & fraud investigators with graph-based and text-based reasoning
Analytics agents that assemble queries and dashboards on behalf of users
Scenario simulators for pricing, risk, revenue and demand forecasts

Example engagement shapes

We join at different maturity levels: from “first data platform” to “refactor our fragmented lake, warehouse and ML tooling into something coherent”.

Lakehouse modernization

Consolidate legacy warehouses and ad-hoc pipelines into a unified lakehouse with medallion layers and dbt.

Architecture, tooling and migration playbook
Blueprint dbt project and transformation strategy
Lineage, catalog and observability foundations

ML & MLOps uplift

Move from notebook-only experimentation to governed, repeatable ML lifecycles tied to your data platform.

Feature store design and deployment
Training / evaluation / deployment pipelines
Monitoring, drift and retraining playbooks

AI & LLM productionization

Launch your first or next generation of LLM-powered products, grounded on your existing data platform.

RAG architecture and retrieval optimization
Evaluation harnesses and guardrails
Feedback loops and human-in-the-loop review

Ready to build a real data platform?

Whether you're consolidating data systems, building a lakehouse, uplifting MLOps, or launching your first AI platform — we help architect, implement and operationalize every layer.

Architecture and maturity assessment
Blueprints tailored to your domain and regulators
Ingestion, transformations, ML readiness and governance