The Semarchy Data Platform (SDP) does two main things with AI. It uses AI to simplify how you create, govern and manage master data — the customers, products, suppliers, and reference data your business runs on. And it prepares them as trusted data products that are ready for real-time AI and machine learning (ML) use.
That second part matters more than it might seem. A recent Semarchy survey of 1,000 global C-level executives found that data management is now the single most pressing AI challenge, cited by 51% of respondents — ahead of both cost and talent. Yet half of those leaders are currently running AI initiatives without master data management (MDM) foundations in place.
Most platforms claim AI capability. Fewer structure, validate, and document data in a way that AI applications can consume reliably.
Semarchy approaches AI-driven data management through seven distinct capabilities: a custom-trained AI agent that automates data engineering, embedded AI for classification and enrichment, AI-powered governance, automated data quality, human-in-the-loop agentic workflows, AI-ready data preparation, and a governed catalog for AI and ML assets.
This blog covers each one in detail.
1. Custom data engineering agent
The Semarchy Data Platform includes a custom-trained data engineering agent that lets developers describe what they need in plain language and have a working data product in minutes. Instead of manually configuring integrations and transformations, teams submit a natural language prompt, and the AI agent handles the engineering work.
This matters because data engineering bottlenecks are one of the most common reasons MDM projects stall. When every new integration requires weeks of manual configuration, the business waits. The AI agent compresses that cycle significantly.
The AI agent is trained on hundreds of successful MDM projects and connected to an MCP server with access to the latest docs and resources. It’s embedded directly in the VS Code workspace with direct access to your data product project files and context. This ensures your data products adhere to our specifications and are grounded in your business requirements. Full chain-of-thought reasoning with human-in-loop governance means every agent action is traceable and reversible before outputs are incorporated.
For teams evaluating MDM platforms, the practical question is whether AI assistance shows up at the point where engineering work actually happens — or is limited to dashboards and recommendations that still require manual execution. In SDP, it shows up at the point of creation with the industry’s first DataOps-driven design experience (DXP).
2. Embedded GenAI for data classification and enrichment
Data stewards spend a significant portion of their time on tasks that are repetitive but consequential:
- Classifying data assets
- Assigning business terms
- Enriching metadata
They do this so that other teams can understand and trust what they’re working with. Semarchy automates this work using embedded generative AI (GenAI).
The platform’s AI classification enricher automatically organizes and classifies data assets, then assigns relevant glossary terms based on its analysis of the data. This replaces a manual cataloging process that typically requires domain expertise, careful judgment, and considerable time.
The enrichment side works alongside classification. Rather than leaving metadata fields incomplete or relying on stewards to populate them manually, SDP uses AI models to infer and fill contextual information about data assets automatically.
The practical benefit is consistency. Manual classification introduces variation, since different stewards make different judgment calls. AI-driven classification applies the same logic at scale, across every asset, every time. Of course, stewards remain in the loop to review and correct outputs, but the volume of manual work drops considerably.
3. AI-powered data governance for rules, compliance, and workflows
Governance is often where data management initiatives slow down. Policies need to be defined, rules need to be applied consistently, and compliance requirements need to be enforced across teams that may be working with very different data. Semarchy uses AI to automate and accelerate that process.
Rather than relying purely on manually configured rule sets, SDP applies AI to augment governance workflows. This includes automated application of data rules, compliance checks, and stewardship decisions — reducing the overhead that typically falls on data teams when governance is handled manually.
The Semarchy survey found that 77% of leaders have now integrated AI considerations into their data governance policies, with many retrofitting compliance under pressure. A platform that embeds governance into automated workflows from the start makes that retrofitting less necessary.
Governance in SDP is not a separate layer bolted on after the fact. It runs alongside data engineering and stewardship workflows, which means compliance checks happen continuously rather than periodically. For regulated industries in particular, that distinction has real operational consequences.
4. AI-assisted data quality, profiling, and cleansing
Bad data is the most common reason AI initiatives underdeliver. Models trained on incomplete, inconsistent, or duplicate records produce outputs that reflect those flaws. Fixing the problem after the fact is expensive. Semarchy addresses it earlier in the process, at the point where data is profiled, cleansed, and prepared.
The platform automates profiling — scanning datasets to detect issues with completeness, consistency, and accuracy — and applies cleansing rules to standardize and correct data at scale. Enrichment runs alongside this, filling gaps in records using available context and reference data.
What separates AI-assisted quality from basic rule-based validation is the ability to detect anomalies that predefined rules would miss. The Semarchy Data Platform monitors data continuously and can surface issues that fall outside expected patterns, rather than only catching errors that someone thought to write a rule for in advance.
Stewards review and approve changes before they’re applied, which keeps human judgment in the process without requiring manual effort at every step. The result is cleaner data, delivered faster, with less burden on the teams responsible for it.
5. Human-in-the-loop agentic workflows for data stewardship
Agentic AI refers to systems where AI completes multi-step tasks with a degree of autonomy, rather than simply responding to individual queries. In the context of data stewardship, this means AI can propose actions, route tasks, and progress workflows without waiting for a human to initiate every step.
The Semarchy Data Platform implements this through a human-in-the-loop design. AI agents handle the operational work — flagging exceptions, suggesting resolutions, enriching records — but humans retain approval authority over changes before they’re committed. This keeps automation moving at pace while ensuring that consequential decisions don’t bypass human judgment.
This balance matters for enterprise data teams. Fully automated workflows introduce risk when data quality or compliance is at stake. Purely manual workflows don’t scale. Semarchy’s agentic design sits between those two positions, applying automation where it adds speed and requiring human review where it adds safety.
For organizations building toward agentic AI capabilities — 65% of leaders in the Semarchy survey said this is a priority for the year ahead — having an MDM platform that already operates on agentic principles gives teams a meaningful head start.
6. AI-ready data preparation for analytics and machine learning
An AI model is only as reliable as the data it learns from. Feeding ML pipelines with data that lacks lineage, quality metrics, or business context produces outputs that are difficult to trust and harder to audit. Semarchy prepares data specifically to meet the requirements of AI and ML consumption, not just business reporting.
This means every dataset the platform produces carries documented lineage — a traceable record of where data originated, how it was transformed, and where it flows downstream. Quality metrics travel with the data, so consuming teams know exactly what they’re working with before they use it.
The platform also validates AI readiness directly, surfacing gaps in completeness, coverage, or governance so teams can resolve issues before they reach a model training pipeline. This is a more proactive approach than discovering data problems after an AI project has already been built around flawed inputs.
Given that more than half of organizations lack confidence in the quality of data fueling their AI applications, having a platform that makes readiness visible and measurable is a practical advantage rather than a nice-to-have.
7. A governed data catalog for discovering and consuming AI and ML assets
Most data catalogs help teams find datasets. The Semarchy Data Platform’s catalog goes further, providing a single searchable environment where users can discover and access datasets, applications, APIs, and AI and ML models within a governed framework.
This matters because AI initiatives generate assets — trained models, curated datasets, pipelines — that need to be managed, documented, and made available for reuse. Without a catalog that explicitly supports AI and ML assets, those resources become siloed and difficult to govern. Teams rebuild what already exists, and models get deployed without proper documentation or access controls.
Semarchy’s data product catalog includes glossary terms, lineage, ownership records, data quality metrics, and usage documentation for every asset. This gives consuming teams — whether human users, business applications, or AI agents — the context they need to evaluate and use assets confidently.
The governed element is significant. Access controls and policy enforcement are built into the catalog, so discovery doesn’t come at the cost of compliance. Teams can share AI and ML assets broadly without losing oversight of who is using them and for what purpose.
The Semarchy Data Platform: AI capabilities built for enterprise MDM
AI investment is accelerating. The Semarchy survey found that significant AI investment has tripled year on year, with half of organizations now committing more than 20% of their total tech budget to scaling AI initiatives. The platforms teams build on matter more than ever.
The Semarchy Data Platform delivers many different AI capabilities that work across the full data management lifecycle, but each capability is designed to do two things: make data management faster and less manual, and ensure the data that reaches your AI applications is trustworthy, traceable, and fit for purpose.
If you’re evaluating MDM platforms for AI-ready data delivery, see how SDP performs in practice.
Explore the Semarchy demos to walk through the platform’s AI capabilities in detail.
Share this post
