Data & AI Foundation

We make AI reliable and scalable by unlocking unified, governed, AI-ready data

Unify your data. Profile your customer. Unlock your AI.

Fragmented data, ungoverned sources, and legacy systems block AI and analytics. ADA builds unified data foundations that consolidate customer, product, and commerce data into one governed layer that is ready for reporting, activation, and AI at scale.

How AI Transforms Healthcare: Risk Prediction to Clean Claims

Insights

2 Oct 2025

Our solutions

Data Foundation

One trusted data foundation, unified, governed, and built to drive measurable enterprise outcomes.

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Time-to-insight reduction

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Data connectors built

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Data quality achieved across critical datasets

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Data for AI

An AI-ready data layer that is structured, governed, and optimized to power AI agents, and enable intelligent automation at scale. The foundation AI needs to deliver.

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Data prep time reduction

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Governance coverage for GenAI deployments

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Data quality improvement for AI training

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Agentic AI & Analytics

We build, deploy, and manage AI solutions from custom ML models to agentic applications that solve real operational problems and deliver sustained value post-launch.

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Revenue growth from ML-driven CLV models

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Reduction in fraud detection task time

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Revenue increase from automated analytics

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Our Services

Data Foundation

Data for AI

Agentic AI & Analytics

Delivered as a system, our solutions combine deep engineering capability with end-to-end services to build, deploy, and continuously operate AI.

Data Foundation

Data Engineering & Integration

We connect to any source—whether CRM, POS, marketing platforms, IoT, or legacy systems—using leading ingestion tools like Fivetran, Airbyte, and cloud-native connectors. We’ve built 500+ connectors, including custom integrations for hard-to-reach sources. Both real-time streaming and batch ingestion, with schema validation and change data capture built in.

Customer Data Platform (CDP)

CDP implementation as part of your unified data foundation, not a standalone silo. Identity resolution, profile unification, and activation readiness built on your data warehouse. Composable CDP architecture that avoids vendor lock-in while enabling real-time personalization and segmentation.

Data Migration

Full data infrastructure migration—on-premise to cloud, legacy warehouse to modern lakehouse, or platform-to-platform moves. We implement across Snowflake, Databricks, AWS, GCP, and Azure with data validation, parallel running, and cutover planning. Migration that modernises your architecture, not just lift-and-shift.

Data Warehouse Lakehouse Implementation

Snowflake, Databricks, or BigQuery implementation with data modelling, dimensional design, and medallion architecture. Platform-agnostic—we work with your preferred cloud. Accelerators for common Retail, BFSI, CPG, and Healthcare data models. Optimised for analytics, reporting, and AI workloads.

Custom Data Connectors

Custom connector development for sources without standard integrations, legacy systems, regional platforms, proprietary APIs, and niche applications. Pre-built connectors for APAC ecosystem including local payment gateways, marketplaces, and regional CRMs. We’ve built 500+ connectors for enterprises and platforms like Fivetran.

Data Governance & Quality

Enterprise data governance frameworks, data quality rules, lineage tracking, access control, data masking, and audit logging. Regulatory-ready frameworks for PDPA, data residency, and compliance requirements. Governance as foundation, not afterthought.

Data for AI

AI Data Engineering & Integration

Data engineering specifically designed for AI workloads: feature pipelines, training data preparation, model serving infrastructure, and real-time inference feeds. We connect, transform, and structure your data for what AI models actually need. Feature engineering, data versioning, and ML-ready transformations included.

Data Labeling Services

Domain-specific data labeling and annotation services for AI/ML model training, text annotation, image/video labeling, entity extraction, sentiment tagging, and custom taxonomy development. Quality assurance workflows and vertical expertise in BFSI, Retail, and Telco ensure training data that reflects real-world complexity.

Data Migration (AI-Ready)

Data migration with AI as the destination, this is not just cloud migration, but migration to platforms optimized for ML training, feature stores, and model serving. Databricks ML, Snowflake ML, Vertex AI, and SageMaker implementations with AI workload requirements built into architecture from day one.

GenAI Guardrails & AI Governance

Governance frameworks specifically designed for GenAI and AI agent deployments: hallucination prevention, compliance filters, prompt injection protection, output validation, and explainability requirements. Goes beyond general data governance to address AI-specific risks that regulators and boards are asking about.

AI/ML Ops

Operationalization of AI/ML models: model deployment, performance monitoring, drift detection, automated retraining pipelines, and feature store management. The last mile that turns trained models into business value. Integrated with ADA’s AI accelerators and composable architecture.

Agentic AI & Analytics

Delivered as a system, our solutions combine deep engineering capability with end-to-end services to build, deploy, and continuously operate AI.

Agentic AI Application Development

Design and build autonomous AI agents that handle complex, multi-step business workflows, pricing, inventory, document processing, customer interactions without a human in the loop at every step.

Custom ML Model Development

Machine learning models built and integrated for specific business problems across Retail, BFSI, CPG, Telco, and Healthcare.

GenAI Application Development

RAG-based enterprise assistants, document intelligence systems, and multimodal AI, scoped and built for production environments.

Analytics Engineering

Automated reporting pipelines and self-serve analytics layers for CXO and operational dashboards built around the questions that matter to the business.

AI/ML Ops

Model monitoring, drift detection, automated retraining, feature store management, and performance reporting. Keeps models working and accurate after go-live.

Customer Stories

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Data Foundation

How ADA Built 500+ Data Connectors for a Leading Data Integration Platform

Where every touchpoint earns trust, every interaction creates value, and every decision is powered by realtime insight.

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connectors

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revenue growth

Indonesia Data Foundation

We helped a leading ecommerce retailer migrate their existing data warehouse from AWS to MS Azure while ensuring seamless business continuity

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migration accuracy

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stored procedures migrated

Consumer Goods Data For AI

How a Global Media Group Built AI-powered Creative Generation with Governed Data

Where every touchpoint earns trust, every interaction creates value, and every decision is powered by realtime insight.

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Engagement

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FAQs

Do we need all three solutions?

No. Each solution solves a distinct problem at a distinct layer. Data Foundation is for enterprises with fragmented data infrastructure. Data for AI is for enterprises whose AI projects are failing because of how data is structured for models. Agentic AI & Analytics is for enterprises ready to build, deploy, and operate AI solutions on top of that foundation. A client may enter at any layer, they don’t need all three to start.

How is this different from a data warehouse?

A data warehouse is storage. We deliver the full stack — ingestion, transformation, governance, and activation readiness. Plus, we design for AI from day one, not as an afterthought.

We already have Snowflake/Databricks. Why do we need you?

Great — we work with your preferred platform. The challenge is not the warehouse tool; it is getting data into it cleanly, governing it properly, and making it usable for business. That is what we do.

What is the fastest path to value?

Start with a data assessment (2-4 weeks) to identify gaps and quick wins. First phase with core sources live in 8-12 weeks. Full platform typically 4-6 months depending on source complexity.

How is Data for AI different from standard data engineering?

Standard data engineering focuses on warehousing and reporting. Data for AI is specifically optimised for AI workloads — labeling, GenAI guardrails, model operationalisation, and AI-specific governance. It is the layer between raw data and working AI.

What if our AI projects are still in pilot phase?

That is the best time to engage. Building the right foundation now prevents technical debt later. We can start with a focused labeling or governance project that supports your pilots, then expand as you scale.

What is the difference between Agentic AI & Analytics and the other solutions?

Data Foundation and Data for AI are infrastructure, they make data trustworthy and AI-ready. Agentic AI & Analytics is where you build things on top of that infrastructure: custom ML models, autonomous AI agents, GenAI applications, and the analytics layer that turns data into decisions. You need the foundation first. But if you already have reliable data and want to build AI that actually works in production, Agentic AI & Analytics is where that happens.