AI agents should use the LLM to reason, and the data platform to calculate
The OcientAIQ™ Unified Data Platform helps agents, applications, and analysts work closer to the petabyte-scale enterprise data that holds your business context, so more computation happens where the data already lives instead of inside costly, limited context windows.
Enterprise AI is paying an orchestration tax
Enterprise AI needs more than a model and a prompt. To answer complex questions, recommend actions, and support autonomous workflows, AI agents need access to enterprise context, but that context is often too large, sensitive, fast-moving, or distributed to copy into external AI systems.
Most enterprises respond by adding more layers: pipelines, semantic tooling, vector stores, and orchestration frameworks. Every layer adds cost, latency, governance risk, and another point of failure. The industry has started calling this the orchestration tax, and it compounds quickly.
Agents cannot pull years of transactions, telemetry, relationships, policies, and signals into context without increasing cost, latency, and reliability issues.
When agents work across pipelines, semantic layers, vector stores, and orchestration tools, each question becomes a multi-system integration problem.
Prompt-stuffing schemas, samples, and intermediate results force the model to approximate joins, aggregations, and analysis that should happen in the data engine.
When agents rely on copied extracts, stale subsets, or inconsistent governance, outputs become harder to repeat, explain, audit, and defend.
On the OcientAIQ Unified Data Platform, agents push analytical work into the platform instead of pulling massive enterprise datasets into prompts. The platform filters, joins, aggregates, analyzes, and enriches data where it already lives, giving agents the trusted context they need before they reason, decide, or act. The result is faster answers, lower cost, and outputs your teams can trust and defend.
Give AI workflows access to historical activity, operational signals, relationships, events, and transactions
Let agents work with governed enterprise data without creating new copies of sensitive information across fragmented systems
Continuously ingest and prepare high-volume data so AI workflows can work from current information
Run AI-driven analysis close to the data, so workflows can retrieve context and return answers with the speed enterprise use cases require
OcientAIQ combines a scalable, high-performance data foundation with agent-ready services that help AI workflows ingest, analyze, govern, contextualize, and manage enterprise data at production scale.
100M+ rows per second continuous ingest so data is AI-ready the moment it arrives, not hours later
Give agents direct access to multimodal analytics in one engine, including SQL, machine learning, geospatial, graph, relational, and real-time analytics
Treat agents as governed users. Apply access controls, lineage, and audit trails to AI workflows without bypassing enterprise policies
Expose machine-readable business semantics – including schemas, relationships, metrics, definitions, and rules – so agents understand what the data means, not just where it lives
Provide pre-built, reusable capabilities that help agents execute trusted analytical tasks against enterprise data
Observe, trace, and manage agent activity to maintain performance, reliability, and control as usage scales
Connect to your AI ecosystem
The OcientAIQ Unified Data Platform is designed to work within existing AI infrastructures. Connect agents, applications, models, and workflows to enterprise data through open interfaces and deployment patterns that fit your environment.
Connect agents and orchestration frameworks to the OcientAIQ Unified Data Platform through the Ocient MCP server, with governed access to enterprise data, platform context, and analytical capabilities
Integrate applications, workflows, and external systems with OcientAIQ’s REST API and connectors designed for agent-ready access
Support analysts, developers, and data workflows with familiar database connectivity through JDBC and Python
OcientAIQ use cases for high-volume enterprise data
Support AI-assisted decisions on high-volume operational data, including telemetry, events, transactions, and time-sensitive signals.
Give AI workflows governed access to network-scale data for monitoring, analysis, and action.
Analyze billions of interactions with the speed and cost discipline needed for real-time market decisions.
Fuse multi-source data with governance, security, and deployment flexibility for sensitive environments.
Ready to make your enterprise data usable for AI?
OcientAIQ helps teams move beyond fragile AI pilots by providing secure access to the enterprise data and computation they need at production scale.