The bar changed when AI stopped just answering
Dashboards tell you what happened. Analytics explains why. AI increasingly needs to understand what is happening now, access trusted enterprise context and act within defined rules.
That requires more than pipelines. It requires a governed data foundation, with consistent definitions, real-time integration, quality, lineage, security and observability built in.
We engineer the foundation that allows data to move from source systems to trusted intelligence, and from intelligence to action.
From definitions to decisions, every layer governed
We agree what your data means, build the pipelines that keep it true, enforce the controls that make it safe to act on, then serve it to the people and the agents that use it.
Everything your enterprise needs to make data trusted and AI-ready
Build a governed, scalable, and AI-ready data foundation.
- Enterprise Data Modelling: Banking Reference Data Model (EDM) and domain-specific extensions.
- Semantic & Knowledge Layer: So agents query meaning and relationships rather than column names.
- Data Lake & Warehouse Design: Cloud-native architectures on Azure, AWS, Snowflake and Databricks.
- Data Quality & Standardisation: Automated profiling, cleansing and reconciliation (iCEDQ, IDMC), with quality thresholds that gate what agents are permitted to use.
- Metadata & Master Data Management: Unified catalogues, golden records and lineage visibility.
- Performance Optimisation: Partitioning, caching and elastic scaling for analytical workloads.
Overnight batch cannot feed a decision that has to happen now
- API-Led Integration: Secure, reusable APIs via Azure API Management / Informatica iPaaS.
- Event-Driven Pipelines: Real-time streaming using Kafka / Event Hub for instant data propagation.
- Ingestion Frameworks: Batch, CDC, and hybrid ingestion accelerators for structured & unstructured data
- Cross-Platform Connectivity: 300+ connectors across core banking, CRM, ERP, and external feeds.
- Integration Automation: Infrastructure-as-Code and CI/CD pipelines for rapid deployment.
Enforce trust, compliance, and accountability across your data estate.
- Data Lineage & Cataloguing: End-to-end traceability using Microsoft Purview, Informatica CDGC, BigID.
- Security & Compliance: Role-based access, encryption at rest/in-transit, GDPR/PDPA/UAE CB compliance.
- Data Stewardship Frameworks: Defined ownership and approval workflows for critical data assets.
- Policy Enforcement: Automated masking, classification, and retention controls.
- Monitoring & Observability: Platform health, usage analytics, and alerting via Cloud Unity and Azure Monitor.
Move from legacy to modern cloud platforms faster and safer.
- Automated discovery and mapping: Source-to-target lineage extraction using BigTapp's Migration Toolkit.
- Hybrid Migration Approach: Top-down and bottom-up strategies for curated and consumption layers.
- Cloud Data Platform Migration: On-prem to Azure, Snowflake, or Databricks with zero data loss.
- Regression Testing & Validation: Automated reconciliation and report comparisons.
- Cost & Performance Optimization: FinOps guardrails for resource utilization and ROI tracking.
Built for today's data needs. Ready for tomorrow's AI.
Trusted Data
Governed, quality-assured and connected across sources.
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Business Context
What each entity actually means in your business.
Semantic & Knowledge Layer
Relationships, rules and history agents can reason over.
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Analytics & Decision Intelligence
Insight that explains and recommends.
AI & Agents
Decisions that execute inside your rules.
Accelerate the engineering, not just the outcome
Reference Data Model
Standard semantics for Party, Product, Account, Facility, Limit, Exposure, Transaction, and Channel entities.
Data Mapping / Migration Accelerator
Automates metadata-driven entity–attribute mapping and links KPIs to data assets and Power BI models, reducing 60%+ of blueprint effort and enabling faster estimates.
Intelligent Document Processing
Intelligent Document Processing for KYC documents, statements, contracts, credit memos, and emails.
De-identification
Dynamic Masking and different types of Anonymization to protect PII Data.
AI-Driven Data Engineering Copilot
Turns requirements and documents into semantic models, ETL pipelines and deployment-ready code, accelerating platform setup by 50 to 70%
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FinOps / Cloud Unity
Automated tagging, anomaly detection and chargeback, plus self-service analytics and cost per workflow, so AI spend stays visible as it scales.
Numbers over narratives.
See how we turn fragmented data into governed, scalable foundations for analytics, AI and business growth.
