As enterprises race from AI pilot projects to full-scale automation, one bottleneck keeps resurfacing: messy, inconsistent, incomplete data.
Now, Precisely is aiming to fix that with a new set of AI agents embedded directly into its Precisely Data Integrity Suite—bringing automation to some of the most manual, time-consuming data workflows in the enterprise stack.
The company unveiled new Data Quality, Data Enrichment, and Location Intelligence agents designed to work alongside its Gio AI Assistant. The goal: help organizations build what Precisely calls “Agentic-Ready Data”—accurate, standardized, and context-rich information that autonomous AI systems can actually trust.
It’s not just about cleaner spreadsheets. It’s about making AI deployments viable at scale.
What’s New: AI Agents for the Data Layer
Precisely’s latest release introduces AI agents that automate core data management tasks that typically require specialized expertise and extensive rule writing.
The new capabilities include:
- Rule recommendation and creation: Automatically identify gaps and generate data quality rules based on data patterns, structure, metadata, and user input.
- Normalization and standardization: Detect inconsistencies across sources and harmonize data formats without manually crafting transformation logic.
- Address verification and geocoding: Validate and geocode address data to ensure reliable, consistent location intelligence.
- Data enrichment: Append verified attributes to datasets to add business and geographic context.
All of this is delivered through conversational interaction with Gio, Precisely’s AI assistant inside the Data Integrity Suite. Instead of scripting transformations or building rules from scratch, data teams can ask for recommendations, review previews of changes, and approve updates—keeping humans in the loop while cutting down on repetitive labor.
The positioning is clear: AI not just for analytics and apps, but for the foundational data plumbing underneath them.
Why It Matters: AI Is Only as Good as Its Data
As enterprises move toward increasingly autonomous AI systems—models that don’t just analyze data but trigger actions—the margin for error shrinks.
An AI agent that acts on inconsistent address data, misclassified records, or incomplete customer attributes doesn’t just generate flawed insights; it can automate flawed decisions.
Precisely’s bet is that “Agentic-Ready Data” must be:
- Accurate
- Consistent across systems
- Enriched with verified real-world attributes
- Governed and auditable
In other words, data prepared for a world where AI systems operate with greater autonomy and less human oversight.
This comes at a time when organizations are layering generative AI, predictive models, and workflow automation on top of legacy systems that were never designed for such demands. The data stack often lags behind AI ambition.
Precisely is trying to close that gap by applying AI to the data integrity process itself.
Conversational Data Engineering—With Guardrails
A notable aspect of the release is how the agents integrate with Gio. Rather than functioning as black-box automation, the system emphasizes:
- Conversational task initiation
- Clear recommendations
- Previews of proposed changes
- Built-in approval workflows
That’s a critical design choice. In heavily regulated industries—financial services, healthcare, telecom—automated data transformations without oversight are a nonstarter.
Ulf Viney, EVP of Engineering, Support & Operations at Precisely, framed it as a shift from reactive, manual data preparation to AI-driven automation with governance intact. The messaging underscores a balancing act: speed and productivity without sacrificing transparency and control.
This “AI with approvals” model mirrors a broader enterprise trend. Vendors across analytics, DevOps, and cybersecurity are embedding AI copilots and agents—but wrapping them in auditability and policy controls to avoid runaway automation.
Location Intelligence Gets an AI Boost
The inclusion of address verification and geocoding agents is particularly strategic.
Location data underpins logistics, fraud detection, insurance risk modeling, retail site planning, and more. Yet address data is notoriously messy—variations in formatting, incomplete fields, and outdated records can degrade analytics accuracy.
By baking geocoding and verification into the same AI-driven workflow as normalization and rule generation, Precisely is pushing for an integrated approach to context-rich data. Instead of cleaning data in one tool and enriching it in another, enterprises can manage the lifecycle in a single suite.
That consolidation could appeal to organizations tired of stitching together multiple point solutions.
From AI Assistant to AI Fabric
Today’s launch builds on a series of AI-focused enhancements within the Data Integrity Suite, including:
- Gio AI Assistant
- Data Catalog Agent
- AI and Agentic Fabric
The throughline is clear: Precisely is positioning itself not just as a data quality vendor, but as an enabler of enterprise AI infrastructure.
As competitors in data management and governance increasingly bolt AI features onto existing products, Precisely is leaning into a platform narrative—where AI agents orchestrate and automate foundational data processes.
The timing aligns with broader industry shifts. As generative AI and agentic systems move from proof-of-concept to production, CIOs and CDOs are realizing that scaling AI isn’t primarily a model problem—it’s a data readiness problem.
The Competitive Context
The data quality and governance market has grown crowded, with players offering everything from cataloging and lineage tracking to automated transformation pipelines. But many still rely heavily on rule-based systems requiring deep technical know-how.
Precisely’s approach attempts to lower that barrier by:
- Automating rule suggestions
- Reducing manual coding
- Embedding enrichment and location intelligence
- Keeping governance embedded in the workflow
If it works as advertised, it could reduce reliance on scarce data engineering talent—an increasingly valuable resource in AI-heavy enterprises.
The key question will be execution. AI-generated rules and standardizations must be accurate and explainable. Enterprises will want evidence that automation improves data quality metrics, not just speeds up configuration.
The Bottom Line
Precisely’s new AI agents represent a practical evolution in enterprise AI infrastructure: using AI to prepare data for AI.
Rather than chasing the next generative model headline, the company is targeting the unglamorous but mission-critical layer underneath—data normalization, rule creation, enrichment, and location intelligence.
As organizations push toward autonomous systems that act on insights, “good enough” data won’t cut it. The enterprises that succeed with AI at scale will be those that treat data integrity as a first-class, automated discipline.
With its latest release, Precisely is betting that the future of enterprise AI starts not with smarter models—but with smarter data pipelines.
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