Your data is growing faster than your ability to use it.

Enterprise data is spread across legacy platforms, cloud environments, SaaS applications, partner ecosystems, and operational systems. It arrives in different formats, follows different standards, and is often difficult to trust. Gartner estimates that poor data quality costs organisations an average of at least $12.9 million a year, with inconsistency across data sources identified as a major data quality challenge.

Adding more pipelines and platforms cannot solve a data quality problem rooted in fragmented, inconsistent data, because the problem sits in the foundation itself.

Fragmented data landscapes

Critical data is distributed across legacy systems, cloud platforms, SaaS applications, and partner ecosystems—with no unified view across the business.

Inconsistent data quality

Incomplete, duplicated, and poorly standardized data creates reconciliation effort and reduces confidence in business insights.

Manual by default

Data ingestion, cleansing, transformation, and reconciliation depend heavily on engineering effort, slowing the delivery of new insights and use cases.

Governance gaps

Inconsistent lineage, ownership, security, and access controls make it difficult to understand where data came from, how it is used, and whether it can be trusted.

Complexity that compounds

Every new use case can require new integrations, pipelines, and data preparation—driving duplication, cost, and operational complexity.

AI without the right foundation

When data lacks quality, context, and governance, AI applications and agents cannot reason or act with confidence.

One connected path from
data to decisions.

Synapt DT creates a continuous path from fragmented data to trusted intelligence. Data is connected across the enterprise, transformed into usable assets, governed throughout its lifecycle, enriched with business context, and activated across analytics, AI, and operations.

What matters is having data that is connected, trusted, and ready to be put to work.

Bring together data from enterprise systems, cloud platforms, SaaS applications, partner ecosystems, and operational environments.

Cleanse, standardize, harmonize, and modernize data using AI-powered accelerators that reduce manual effort and accelerate delivery.

Apply data quality, lineage, security, access, and policy controls throughout the data lifecycle—so every insight is grounded in trusted data.

Connect data to business entities, relationships, domains, and operational context—making it easier to understand, discover, and reuse.

Make trusted data available across analytics, business applications, AI models, agents, and operational workflows.

Capabilities across the
data lifecycle.

Migration and modernisation

Move legacy data estates onto current platforms. Discovery and lineage mapping, schema and code analysis, generated mappings and transformation code, and source-to-target reconciliation.

Data Engineering

Take requirements through to deployed pipelines. Data models, code and orchestration are generated, and your engineers review and approve each step

Data Science

Work across relational sources through one interface. Exploratory analysis runs inside your own environment, with recommended transformations and leakage checks during feature engineering.

Business intelligence

Ask questions in plain language. Answers come back as queries, visualisations and dashboards.

Built for the challenges
your team faces.

You cannot see where your data came from.

Every transformation is recorded, so lineage and ownership are visible across the estate rather than reconstructed after the fact.

Your team spends more time building pipelines than using them.

Requirements, code and tests are generated and reviewed by your engineers, on the platforms you already run.

Your AI work stalls on the data rather than the model.

Data arrives structured, documented and governed, so models and agents can use it without a cleaning step first.

Trusted by leading operators and enterprises

Inside a Global FinTech Leader’s Data Modernization Journey

Legacy Hadoop workloads were migrated to Cloud through GenAI-powered automation, accelerating modernization while reducing manual effort and migration risk.

50% faster migration
65% improvement in data pipeline performance
30% reduction in infrastructure maintenance costs

Stop preparing data.
Start creating value.

Turn fragmented data into a governed foundation that your teams and AI systems can rely on.