Agentic Data Quality Accelerator
Vendor-agnostic control layer that cuts implementation time by 40–60%
Data quality becomes expensive when bad data still looks plausible.
Intellias Data Quality Accelerator is a production-ready agentic data quality control layer for validating, monitoring, and governing data across your existing estate.
It brings automated checks, profiling, quality scorecards, incident workflows, and governed AI-assisted rule creation into one deployable architecture.
Our workflow from first sprint to steady state
Week 0 — Assess
Business and technical objectives, scope boundaries, critical data elements, and the four to five fields worth profiling first.
Week 1 — Adapt
Reference architecture tuned to your pilot workload. DQ capability structure defined. Landscape and lifecycle mapped.
Week 2 — Integrate
IntelliDQ deployed into your ecosystem. Test cases implemented for prioritized use cases. Rules authored, critiqued, validated, and approved.
Week 3+ — Operationalize
Delivered artifacts: DAMA DMBOK-aligned Data Quality Policy, Technical Design and Reference Architecture, DQ Operating Model with data steward role definition, Assessment Findings, and an Implementation Roadmap.
Steady state
The governed loop runs on live data. Agents propose rule refinements. Engineers approve. Dashboards move. Executives read the numbers they asked for.
Handover or Managed DataOps
Transfer artifacts, patterns, and operating capability to your team, or continue under a managed run.
Why Intellias for data quality
The differentiator sits in how the quality layer is engineered and operated, rather than in the presence of another dashboard.
The Accelerator works across heterogeneous data platforms and supports cloud and on-premises deployment. Existing platforms remain part of the architecture.
AI assists with rule suggestion and refinement. Engineers approve. Deterministic validation handles production enforcement.
Rules can reflect distinct requirements for finance, compliance, operations, analytics, and data science while remaining attached to common enterprise data assets.
The technical layer includes rules, profiling, observability, and alerts. The operating layer defines ownership, rule lifecycle, remediation, exceptions, and stewardship.
Rule patterns, scorecards, governance structures, and architecture established for the first critical entity can serve as a template for subsequent assets and business domains.
IntelliDQ is delivered under a free-of-charge, non-exclusive license built on open-source components. Published pricing examples in the category run at roughly $60,000 to $90,000 per year and often scale by asset count.
FAQs about the Data Quality Accelerator
The Data Quality Accelerator is a vendor-agnostic data quality control layer for enterprise data environments. It combines deterministic data validation, profiling, anomaly monitoring, quality dashboards, incident workflows, metadata context, and governed AI-assisted rule creation.
Observability focuses heavily on detecting and investigating changes in data and pipelines. The Accelerator also formalizes explicit business and technical quality rules, executes deterministic validations, connects rules to governance and ownership, and supports quality gates in the data-consumption path. Its architecture still includes observability through profiling, dashboards, anomaly detection, and incident workflows.
AI agents take business intent, data statistics, glossary definitions, and other supplied context and propose candidate rules. The workflow critiques and validates those proposals before engineers approve them. Approved rules are enforced through the deterministic validation layer.
Production enforcement in the reference architecture runs through Great Expectations after rule approval. The LLM supports rule authoring and refinement rather than making the final validation decision. Great Expectations documents its Checkpoints as a production validation mechanism for running defined expectations against data.
Yes. The current architecture covers Databricks, Snowflake, Redshift, BigQuery, Microsoft Fabric, Azure Synapse Analytics, transactional SQL databases, and cloud object storage including S3, GCS, and ADLS Gen2.
Yes. The reference design supports cloud and on-premises deployment through containerized components and can use self-hosted LLMs where required.
Yes. The operating model treats quality from the perspective of data use. Compliance, finance, data science, and other consumers can define different required controls for the same asset while keeping those rules visible and governed.
That depends on the workload. The architecture supports synchronous validation when passing the quality gate is required before publication, and asynchronous validation when data can be published alongside its quality state for a downstream consumption decision.
A focused assessment can start with one critical entity and four to five fields. The engagement defines the rules, thresholds, architecture, ownership model, MVP controls, and roadmap before extending the pattern to additional entities.