Intelligent Agentic Actions Proven at Enterprise Scale
Deeply understand data context, detect anomalies, and take precise corrective actions.
Cut through organizational and technology silos, enable business and data teams to collaborate and accelerate outcomes.
A powerful, exabyte scale, AI-aware data processing engine, runs on hyperscalers, data clouds, and on-prem.
Natural language interface with contextual memory. Continuously learns, recalls, and explains its reasoning.
Build and deploy your own AI agents into the Acceldata Agentic DM platform.
Bring Your Own LLM—with enterprise-grade governance for data-driven AI decisions
Connect your stack, apply policies, and act with confidence.
Stand up ADM safely within your environment—cloud, on-prem, or hybrid—without disrupting production. Secure, data never leaves your premises.
Resource Based Access Management—policies that move with your data.
Unify, Scale, and Secure Your Data Observability—All While Ensuring Privacy and Control
Robust Protection You Can Trust
Choose the Best Fit for Your Data— Efficiency without Compromise
Runs natively in Snowflake, BigQuery, and more, using existing compute for cost efficiency.
Runs on Spark in your environment, enabling scale and governed execution.
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Optimize spend and measure value with a unified view of data health and cost.
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Monitor pipelines, identify issues, and gain a holistic data view.
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Ensure data trustworthiness, facilitate remediation, and optimize costs.
No, Acceldata observes your data inline so it is highly scalable and secure.
We observe structured, unstructured and streaming datasets.
Yes, we provide recommendations on data quality rules to use based on the data context and several others.
Acceldata platform covers five key pillars: Data Quality, Pipeline, Infrastructure, User, and Cost. AI-powered insights provide full visibility into your data ecosystem, ensuring reliability, optimized pipelines, efficient infrastructure, and cost control. This drives faster decisions, improved efficiency, and higher ROI.
Yes, we can observe data that’s on premises and in the cloud. This makes us ideally suited for cloud migration initiatives.
Yes, you can customize data quality rules and specify exactly where they apply.
Fine grained RBAC, each feature of ADOC can be accessed by users only if they have the required permissions. RBAC also extends to applicable API operations.
Most enterprises already have a defined AI strategy that includes approved model providers, security controls, and governance processes. Using a vendor’s fixed LLM can create parallel AI governance, increase compliance risk, and introduce long-term lock-in. BYOLLM allows organizations to apply AI to data quality, governance, and observability while staying aligned with their existing AI platform, model approvals, and risk controls.
BYOLLM ensures that AI reasoning happens only within approved environments such as an enterprise cloud account or internally hosted model infrastructure. Metadata, schemas, lineage, and policy documents do not need to be sent to external or unapproved AI endpoints. This makes BYOLLM suitable for regulated industries where data movement, model access, and inference boundaries must be tightly controlled.
In a BYOLLM setup, the LLM primarily reasons over governed metadata rather than raw data. This includes technical metadata, profiling results, data quality metrics, lineage relationships, policy definitions, and usage signals. Access to this metadata is constrained by enterprise role-based access controls, ensuring the model only reasons over information the user is authorized to see.
BYOLLM implementations are designed with human-in-the-loop controls. AI-generated outputs such as data quality rules, policy recommendations, or insights are proposed—not automatically enforced. Every action is transparent, reviewable, and auditable. This ensures AI accelerates decision-making without bypassing governance, approval, or accountability processes.
BYOLLM is most effective when paired with platforms that already understand enterprise data context across warehouses, pipelines, catalogs, and downstream consumers. The LLM leverages this unified metadata layer to reason across multiple systems, tables, and transformations. As data estates grow, organizations can scale AI-assisted governance and observability without rewriting rules or duplicating logic for each new system.
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