AI Is Only as Good as Its Data.
Datagaps Ensures Your Data Is AI-Ready.

AI-ready data is data that AI models and agents can understand, trust and act on without a person fixing or explaining it first. It has high quality on the inputs that matter, correct and owned context, complete content in every format, and consistent values from source to output. See the AI readiness guide.

Models and agents answer with confidence even when their inputs are wrong, so bad data becomes bad decisions. Gartner predicts 60% of AI projects unsupported by AI-ready data will be abandoned through 2026, and found 63% of organizations lack, or are unsure they have, the right data management practices. Read the challenges of data quality for AI.

Data quality confirms data is complete, valid and on time. AI-ready data also needs correct context, coverage of unstructured sources, consistency across systems, continuous monitoring of key inputs, and clear ownership. Data can pass every check and still mislead an agent. Data quality testing is the foundation, not the finish line.

Data governance decides who owns data, how it is defined and who resolves disputes. AI-ready data also requires proof that the data meets those standards, continuously. Governance sets the rules; Datagaps validates, monitors and reconciles against them, routes issues to owners and keeps audit evidence. See compliance solutions.

Build in order: know your data, make it reliable, make it owned, make it trusted, then make it AI-ready. Start with the stage where your data is weakest, usually reliability and ownership, and make it solid before moving on. Datagaps covers the validation, reconciliation and monitoring stages. Book an AI-readiness review.

Core criteria are the few key input parameters your AI cannot be right without, such as up-to-date documents, no missing data and no duplicates for a knowledge assistant. Every use case also has additional criteria for its industry and rules. You set the standards; Datagaps Data Quality Monitor validates and monitors all of them.

Agents read documents, emails, chats and notes, and gaps in them are hard to see. Datagaps applies field, pattern and completeness checks to text, JSON, XML and flattened content, so required details are confirmed before AI uses them, using the same rules engine as your tables. Explore the DataOps Suite.

No tool can promise that. Many hallucinations start with missing, ambiguous or inconsistent inputs, and Datagaps removes those causes by clarifying context, checking every format, reconciling systems and monitoring key inputs from source to output. Models can still err for other reasons, so keep evaluating outputs. See data reconciliation and data observability.

Four agents do the heavy lifting: grounded descriptions, AI-generated rules, instant root cause, and the DataOps MCP Server. They suggest and your team approves every action, with a full audit trail. The embedded LLM sends no data to external AI. Datagaps has generated 500+ rules in under two hours; see the agents datasheet.

No. Your governance model and catalog keep owning definitions, roles and decision rights. Datagaps adds validation, reconciliation, monitoring and audit evidence on top, and works with Collibra and Unity Catalog, so your standards are enforced rather than only documented.

DataOps Suite connects to 200+ sources including Snowflake, Databricks, Microsoft Fabric and Azure Synapse, and runs in your cloud, VPC or on-premises. The embedded LLM sends no data to external AI. It is SOC 2 Type II and ISO 27001 certified.

Pricing scales by user, not by rule count or data volume; see pricing. Start a free 14-day trial on your own data, book a demo on your AI use case, or estimate savings with the ROI calculator.