Everything We Shipped in June to Make Enterprise Data AI-Ready

Most enterprise AI initiatives don’t fail because the models are wrong. They fail because the data feeding those models is undocumented, unvalidated, ungoverned, or unreachable.

Every release in this launch targets one of those problems. Across governance, data quality, conversational analytics, vector search, and agentic database connectivity, here’s what’s available starting June 3.

Actian Data Intelligence Platform: Governance that scales

Data catalogs fail for a boring reason: keeping them current is manual work, and manual work doesn’t scale. Definitions go stale. Ownership drifts. The AI systems consuming your semantic layer start producing bad output because the context they’re reading is months out of date.

The Data Steward Agent handles the routine work of catalog management directly inside the Actian Data Intelligence Platform. It generates documentation, assigns ownership to orphaned assets, recommends tags and classifications, and flags incomplete definitions using natural language. Data stewards shift from executing the work to reviewing it. For teams managing catalogs at enterprise scale, that’s a different job.

Item Lifecycle Policies: Only approved definitions reach users and agents. Data stewards define customizable stages (draft, review, approved) and set visibility rules at each stage across Explorer, APIs, MCP Server, and browser extensions. Users see only approved, trusted definitions. Contested content stays in draft. For regulated enterprises – Forvis Mazars, Natixis, and Merck among them – that visibility control is the governance capability they’ve been asking for.

AI Analyst: Insight delivery without the bottleneck

Dashboards are good at answering questions you already knew to ask. They don’t tell you something changed before you notice it, and they don’t package that finding for the executive who needs it at 8:00 am.

Reports convert conversational analysis into structured, reusable outputs: narrative, charts, and tables connected to live governed data. The weekly merchandising summary, the customer health report, the portfolio review, all generated from the analysis workflow itself, not rebuilt manually afterward.

Scheduled Insights monitor governed enterprise data continuously, evaluates changes contextually, and surfaces meaningful anomalies and operational shifts through Slack, Teams, or executive summaries. The difference from threshold-based alerts: judgment. AI Analyst determines whether a change is actually worth surfacing, not just whether it crossed a preset number. BI teams stop fielding recurring monitoring requests. Executives get the brief, not the dashboard.

The result is a more operational approach to analytics, where monitoring, investigation, and executive reporting continue automatically instead of restarting from scratch every week.

Actian Data Observability: Quality you can prove

Telling a stakeholder your data is clean is very different from showing them a pass/fail record for every rule, across every asset, over any time window they name. One is an assertion. The other is evidence.

The DQ Report Page gives data quality teams a centralized, filterable view of rule-level results across all their custom monitors. No more navigating asset by asset or waiting on a manual export. Results export to S3, GCS, Azure Blob, and CSV for downstream BI consumption.

SQL DQ Checks remove the biggest migration barrier in enterprise data quality: rewriting existing rules in a proprietary syntax. Standard ANSI SQL works. The DBT rule your team built last year can move into Actian Data Observability as written, running as a pushdown query against the native source system. Cross-dataset joins are supported, which means foreign key checks, referential integrity, and multi-table business logic all run in a single query.

Replay Scan handles late-arriving data, a problem most tools ignore. When records settle after the scan window closes, standard monitors miss them. Replay Scan lets engineers retroactively apply monitors to up to 20 prior scan periods – no pipeline redesign required. Update a rule, hit Save & Replay, and see results against historical data immediately. New monitors also get a meaningful baseline on day one.

Actian VectorAI DB: Private vector search at production scale

Enterprises with sensitive data face a real tradeoff with most vector databases: send data to a multi-tenant cloud service, or accept that local options can’t scale.

Prometheus-based telemetry closes the observability gap for teams running VectorAI DB on-premises. DBAs and SREs get real-time visibility into engine health, vector query performance, cache behavior, and I/O waits through existing Prometheus and Grafana workflows. Open standards, no proprietary monitoring stack, full visibility in air-gapped environments where data can’t leave the network.

20M+ vector benchmark performance is the scale proof point. VectorAI DB delivers 39x more queries per second than Qdrant Local on a 20M vector, 768-dimension dataset, with p99 latency nearly 5x lower (14.6ms vs. 70.9ms) on identical hardware. That performance holds on a single server, without sharding or horizontal scaling.  For regulated enterprises, public sector agencies, and legal teams running large-scale RAG over sensitive document collections, that means production AI on private data.

MCP Server for Actian Databases: Every Actian database, agent-ready

Enterprises that can’t connect their existing databases to the agentic ecosystem will watch their AI investments stop at the demo stage.

Actian MCP Server 1.0 is now available for Ingres, HCL Informix®, Zen, NoSQL, and the Analytics Engine. It exposes a read-only query interface, schema-as-context via table definitions and DDL, OAuth-compliant authentication, and containerized deployment via Docker Hub. AI agents connect to your databases without risk of accidental modification, without new query languages, and without custom integration work per system. It works with any MCP-compatible client, including Claude, Cursor, Copilot, and custom agents.

The read-only architecture in version 1.0 is intentional. Teams can pilot AI-assisted database workflows against production systems before write operations and human-in-the-loop confirmation land in version 1.1. Built-in result set limits keep runaway queries from overwhelming agent context windows, a guardrail that matters when you’re connecting a live database to an autonomous agent for the first time.

All of the above is available starting June 3. Catch it live at an upcoming Actian event, or connect with your CSM if you’re an existing customer. If you’re new to Actian, the Demo Center is the fastest way to see it in context.

Informix est une marque déposée d'IBM Corporation dans au moins une juridiction et est utilisée sous licence.

À propos de l'auteur

À propos de Dee Radh

En tant que directrice principale du marketing produit, Dee Radh dirige le marketing produit chez Actian. Auparavant, elle a occupé des postes de direction dans le domaine du marketing produit chez Talend et Formstack. Dee a consacré l'intégralité de sa carrière à la commercialisation de produits technologiques. Son expertise réside dans l'élaboration de récits stratégiques et de positionnements différenciés pour optimiser l'efficacité de la mise sur le marché. Outre un diplôme de troisième cycle de l'Université de Toronto, Dee a obtenu des certifications auprès du Pragmatic Institute, de la Product Marketing Alliance et de Reforge. Dee est basée à Toronto, au Canada.