Enterprises have spent years trying to tame sprawling data ecosystems, only to see trust in their data erode under the weight of complexity. Precisely, a global leader in data integrity, is betting AI can change that—responsibly. The company announced plans to expand its Data Integrity Suite with a fleet of autonomous AI Agents and a context-aware Copilot, designed to help organizations keep their data accurate, consistent, and contextual across hybrid and multi-cloud environments.
AI That Doesn’t Run Wild
Unlike the “move fast and break things” approach still common in Silicon Valley, Precisely is emphasizing autonomy with enterprise guardrails. The AI Agents will proactively automate data integration, discovery, quality checks, and even geospatial enrichment—but within parameters set by the customer. Every action will be explainable, traceable, and compliant with business and regulatory priorities.
Chris Hall, Precisely’s Chief Product Officer, puts it bluntly: “AI can only achieve its promise when it operates on data that can be trusted, and when users remain firmly in control of how it’s applied.”
That control-first message is timely. Enterprises are already nervous about black-box AI making business-critical decisions. By combining automation with transparency, Precisely hopes to position itself as the safe pair of hands in an increasingly experimental AI landscape.
Meet the Agents
The first wave of Precisely AI Agents target the core pillars of data integrity:
- Integration Agent – Monitors and orchestrates pipelines across systems from mainframe to cloud. Its debut feature: an automated replication recommender to speed modernization projects.
- Data Discovery Agent – Scans and classifies enterprise data, flagging sensitive information like PII and surfacing hidden quality issues.
- Data Quality Agent – Automates remediation of messy data, starting with normalization and standardization, and continuously learns to refine strategies.
- Location Intelligence Agent – Adds geospatial context to enterprise datasets, unlocking location-aware insights.
Together, these agents form an orchestrated ecosystem—interacting seamlessly, scaling globally, and feeding trustworthy data into downstream decision-making.
A Copilot That Speaks Human
Precisely’s new Copilot adds a conversational layer on top of this complexity. Designed for both IT teams and business users, it translates intricate data tasks into guided, natural language interactions. Think: asking the system to “show me where customer address inconsistencies impact billing” instead of querying databases by hand.
The Copilot doesn’t replace human oversight but instead delegates repetitive work to AI while keeping people firmly in the loop. That design choice nods to growing enterprise demand for explainable, human-centered AI tools.
Beyond the Buzzwords
The launch builds on Precisely’s broader AI ecosystem for data integrity, which integrates multiple AI models (machine learning, generative, agent-based) with enterprise-grade features like audit trails, configuration flexibility, and regulatory alignment. Earlier this year, Precisely added a Model Context Protocol (MCP) server, letting AI applications access trusted location and consumer data via natural language, thanks to an open standard from Anthropic.
That’s a big deal: connecting clean, contextual data directly to LLMs helps ground outputs in real-world conditions, reducing hallucinations and improving explainability—an ongoing pain point for generative AI adoption.
Why It Matters
In an era where data quality failures can tank AI initiatives, Precisely’s pitch is straightforward: enterprises don’t need more AI—they need AI that can be trusted. With rivals like Informatica, Talend (Qlik), and Snowflake also racing to build AI-assisted data platforms, the differentiation may come down to who best balances automation with governance.
If Precisely can deliver on its promise, enterprises could spend less time firefighting bad data and more time using it as a strategic growth lever. Or, as the company frames it, shifting focus from “managing data issues” to “confidently using data to innovate faster.”
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