AI is only as good as your data.
Data With Media Title
AI Data Engineering & Integration
Data pipelines built for AI, not just reporting.
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.
Feature pipelines — for ML model training
Real-time — inference-ready data feeds
Data Labeling Services
Training data that makes AI actually work.
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.
Scaled labeling capacity
Domain expertise — Financial Services, Retail, Telco, Healthcare
Data Migration
Migrate to infrastructure built for AI workloads.
Data migration with AI as the destination — not just cloud migration, but migration to platforms optimised 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.
AI-optimised, not just cloud-migrated
ML platforms: Databricks ML, Vertex AI, SageMaker
GenAI Guardrails & AI Governance
GenAI without governance is a liability, not an asset.
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.
Hallucination prevention — compliance filters, output validation
AI/ML Ops
Get AI from pilot to production at scale.
Operationalisation 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.
Production-grade — AI operations at scale
Drift detection — automated retraining pipelines
How it works
Assess
Design
Engineer
Govern
Operate
Assess
Design
Engineer
Govern
Operate
Built for your industry. Designed for AI that works.
Financial Services
Customer Data for AI
Compliance-embedded governance for regulated AI.
Financial services AI faces unique challenges: models must be explainable to regulators, training data must be auditable, and GenAI must not hallucinate on financial advice. We build AI-ready data foundations with compliance embedded — from feature stores for credit risk models to GenAI guardrails that prevent regulatory violations. Your AI that satisfies both data scientists and compliance officers.
Key Use Cases:
- Feature stores for credit risk and fraud models
- Explainability frameworks for regulatory requirements
- GenAI guardrails for customer-facing AI (no hallucinated financial advice)
- Training data preparation for propensity and risk models
- AI governance for model audit trails
Compliance-embedded — AI governance for regulators | Explainability — for model audit trails
Retail
Data for AI
AI that understands products, customers, and commerce.
Retail AI needs data that understands commerce context — product attributes, customer behaviour, inventory dynamics, and seasonal patterns. We engineer data specifically for retail AI use cases: recommendation engines, demand forecasting, dynamic pricing, and personalisation models. Training data that reflects how retail actually works.
Key Use Cases:
- Feature engineering for recommendation engines
- Training data for demand forecasting models
- Data pipelines for dynamic pricing AI
- Product attribute extraction and labeling
- Real-time inference feeds for personalisation
Commerce-aware — AI data for retail context | Real-time — inference-ready for personalisation
Telecommunications
Customer Data for AI
AI for churn, upsell, and network optimisation.
Telcos have massive data assets perfect for AI — subscriber behaviour, network usage, service interactions — but it’s trapped in legacy systems and siloed formats. We engineer AI-ready subscriber data for churn prediction, upsell propensity, network optimisation, and even data monetisation. From BSS/OSS extraction to production ML pipelines.
Key Use Cases:
- Feature stores for churn and upsell models
- Data engineering from BSS/OSS systems for AI
- Training data for network optimisation AI
- Real-time inference pipelines for next-best-action
- AI governance for subscriber data usage
Scale-ready — high-volume telco data for AI | Real-time — inference pipelines for activation
Customer Stories
How a Global Media Group Built AI-powered Creative Generation with Governed Data
