Trusted Data.Everywhere. At Scale.
One platform to centralize quality management, align business and data teams, expand coverage with AI, and enforce standards via APIs.
Centralized Quality Management
Centralized data quality management across the entire data ecosystem.
As data stacks grow, data quality often becomes siloed across teams and tools. Duplicate rules, inconsistent remediation workflows, and unclear ownership makes it difficult to manage data quality consistently at scale.
Qualytics provides a centralized platform to define, monitor, and manage data quality across the enterprise data ecosystem. Business and data teams collaborate to manage rules, processes, and anomalies consistently across all datastores and pipelines. Centralized visibility and accountability help teams detect issues earlier and resolve them faster, with full explainability and auditability that increase control and trust in the data.
Key capabilities include:
Unified management:
Enable business and data teams to collaborate with a shared, consistent view of quality across the organization.
Shared and standardized
rules and processes:
Prevent duplicate work and ensure rules remain auditable and compliant across teams and environments.
Centralized visibility and accountability:
Provide rollups for coverage, trends, and hotspots, and notify responsible teams so they can triage and resolve issues quickly.
Business and Data Team Collaboration
Shared ownership for data quality from definition to resolution.
Business and data teams experience data quality differently. Business teams focus on domain logic, KPIs, and whether numbers look right based on experience. Meanwhile, data teams deal with failing pipelines, noisy alerts, and unclear requirements. Without a way to collaborate, data quality work becomes reactive, fragmented, and slow.
Qualytics helps business and data teams share responsibility for data quality by giving them one platform to define, review, and maintain automated and authored rules, monitor quality and review anomalies, and take action to resolve issues. With continuous monitoring and shared visibility, teams spot problems earlier, resolve them faster, and maintain clear ownership and understanding of business impact. The result is tighter alignment between business expectations and technical execution, helping teams trust and use their data with greater confidence.
Key capabilities include:
Automated technical and business rules:
Auto-suggest advanced technical rules and foundational business rules so teams start with broad, reliable coverage immediately.
No, low, full code rule flexibility:
Enable business teams to define and own rules using 50+ templated rule types with guided configuration, while engineers can still implement full-code rules for specialized edge cases.
Centralized rule and anomaly management:
Track rule versions, anomaly status, ownership, severity, and resolution metadata in one platform to ensure consistent enforcement and prevent duplicated work across teams and tools.
AI-Augmented Data Quality
Eliminate manual bottlenecks with AI-suggested rules and AI assistants.
Manual profiling, rule creation, and monitoring fall behind quickly as new sources arrive, schemas drift, volumes grow, and normal behavior changes. Static thresholds go stale, coverage gaps widen, and keeping rules current turns into constant hand-tuning that no team can sustain without intelligent automation.
Qualytics delivers AI-augmented data quality in two complementary modes: in-platform AI models that continuously profile data and suggest rules, and an optional bring-your-own AI assistants or agent for natural-language assistance.
Qualytics continuously profiles every field and metric to build rich metadata baselines per column, per measure, and across segments. This is single-tenant: models are trained only on your data, and model outputs are never shared between customers. This means every baseline and suggestion is derived from your own production patterns, not pooled customer behavior. You get privacy by design while still benefiting from models that adapt to your environment.
These profiles capture real production behavior, such as schema characteristics, distribution shapes, null and sparsity patterns, correlations across fields, and end-to-end observability signals like volumetrics and freshness. From that metadata, Qualytics’ proprietary AI models auto-suggest a wide spectrum of rules with calibrated thresholds based on observed data behavior.
Examples include:
Technical rules: Validate data types and formats; enforce null thresholds, uniqueness, referential integrity, and allowed-set checks.
Observability rules: Protect pipelines with freshness expectations, arrival cadence, volume bounds, and missing-partition detection.
Multi-field and model-based rules: Provide higher-order guarantees using learned tolerances, including regression expectations (e.g., revenue ≈ orders × price), distribution checks, and correlation/consistency rules across columns or tables.
Behavior and shape rules: Learn “normal” over time and adapt baselines to detect spikes/drops, seasonality violations, trend breaks, and distribution drift.
Business users know when KPIs look wrong. But translating that context into enforceable rules and fast remediation often requires tickets, handoffs, and back-and-forth between business and engineering teams to align on requirements. This is especially true when the logic must be expressed in SQL, where translating business context into correct joins, filters, and edge cases often takes multiple rounds of clarification. At the same time security and governance teams want full control over which LLMs, copilots and agents are used, where inference is run, and what data is shared.
Natural-language assistance for data quality management.
Qualytics enables you to connect the LLM, copilot, or agent of your choice to the Qualytics Data Quality Platform through the MCP protocol to provide natural-language assistance for authoring rules, investigating anomalies, and coordinating remediation. Through governed APIs, agents can trigger profiling and run validation checks as part of automated workflows. All access is controlled by existing authentication and permissions and logged for audit, enabling teams to move faster without compromising security or control.
What natural language assistance enables
Conversational rule authoring: Enable business users to translate domain logic into operational rules through natural language, complementing the low-code and no-code options.
Faster anomaly triage: Provide clear explanations with impact and root-cause context so teams know what changed and what to do next.
Assistive workflows beyond triage: Help teams explore schemas, validate SQL safely, and create transformations using scan history and ownership context.
How you control it
Bring Your Own Key: Choose models, where inference runs, and what data is shared, including private or air-gapped deployments.
Auditable by design: Ensure every agentic action inherits existing authentication and governance boundaries and leaves an auditable trail.
MCP + Agentic API: Connect through our MCP server, or use the Agentic API to plug copilots and agents into the Qualytics platform.
Entity Resolution
Automate entity name anomaly detection and management.
Duplicate and near-duplicate entities create silent reporting errors. Small variations in names or identifiers can split the same entity, such as a customer or vendor, into multiple records, breaking joins and inflating counts. These issues can take weeks to find manually because near-duplicates are not exact matches and cannot be caught with simple equality checks. In practice, teams combine n-gram and Jaccard similarity with phonetic matching and domain rules, then manually calibrate match-confidence thresholds and manage edge cases for each dataset. The problem of detection is complex, never fully complete, and gets harder as volumes grow and new variants keep appearing.
Qualytics Entity Resolution detects and groups likely duplicate entities by scanning key fields (such as names, customers, vendors, products, locations, or accounts) and clustering records that appear to represent the same real-world entity. It then checks those clusters against a reference ID you provide (for example, customer_id, vendor_key, or business_id) and flags inconsistencies like near-duplicates tied to different IDs or the same ID spanning multiple clusters. The result is an explainable set of clusters and violations that teams can quickly review and remediate, keeping identity mappings, joins, and downstream reporting accurate without manual cleanup.
Configurable matching techniques, including:
Substring pairing with normalization:
Detect containment and token overlap after standardizing casing, punctuation, spacing, and common stopwords or legal suffixes (Inc., LLC, Co.), so “J.P. Morgan Chase & Co.” matches “JP Morgan Chase.”
Phonetic similarity:
Apply sound-alike and homophone matching to group entities that are spelled differently but pronounced similarly.
Spelling similarity thresholds:
Uses edit-distance and character-level similarity to catch near matche, with tunable thresholds per dataset, field, or segment so teams can adjust sensitivity to fit different layers and use cases.
Data Reconciliation
Automate cross-system reconciliation with an out-of-the-box rule.
Reconciling data between systems is deceptively hard, especially at enterprise scale. Engineers often have to write complex SQL just to pull the right slices of data, align keys, match rows, and interpret differences. In modern data ecosystems, that work quickly multiplies because reconciliation is needed not only within a single warehouse, but across warehouses, lakes, databases, and files. Discrepancies can come from transfer issues, transformation bugs, schema drift, or simple human error. And when reconciliation involves files, or comparing a file to a database, SQL alone is not enough. Teams then rely on Python and custom code, adding time, complexity, and more opportunities for discrepancies to slip through undetected.
Qualytics makes cross-system comparisons automated and repeatable, with a configurable DataDiff rule that reconciles any source to any target and produces a left/right view of raw differences. It validates that pipelines, syncs, and transformations preserve both values and structure, including keys, row presence, and overall shape, with support across files, databases, warehouses, and lakes while handling format and dialect differences behind the scenes. When discrepancies occur, it explains exactly what diverged, where, and by how much, down to row- and field-level differences, so teams can catch regressions early and debug faster without one-off SQL or Python.
Key capabilities include:
Any-source to any-target reconciliation:
Compare tables, views, files, or application extracts across environments and technologies without custom code.
Configurable matching logic:
Define row identifiers (including compound keys) to detect missing, extra, or changed rows and pinpoint diffs precisely.
Reference baselines:
Choose any datastore, table, or file as the reference source and reconcile one-time, scheduled, or in-pipeline.
Flexible comparators and tolerance:
Set field-level comparators and margins of error for numeric, duration, or string fields so acceptable variation doesn’t create noise.
Explainable diffs and audit trail:
Summarize discrepancies automatically and surface the offending records, with governed history of runs and outcomes.
Time-Series Anomaly Detection
Spot metric anomalies using explicit ranges and adaptive baselines.
Production metrics rarely stay constant. Order volume, event counts, and conversion rates shift as volumes grow, user behavior changes, and seasonality alters demand, changing what normal looks like overnight. Fixed thresholds break quickly because normal is a moving target. For example, daily order volume might reasonably fluctuate 15 to 25 percent week over week, while a conversion rate may only be expected to drift a point or two. Teams either set tight static bounds and get flooded with false positives, or loosen bounds and miss real issues. Many of the most damaging problems never appear as row-level violations. Instead, they surface as spikes, drops, drift, or missing activity across hours or days, quietly skewing KPIs and downstream models until the business feels the impact.
Qualytics Time-Series Anomaly Detection continuously validates metrics defined on raw or derived data, including calculated columns and cross-dataset rollups, turning any numeric field into a monitored time-series signal. Teams define the metric and evaluation interval (per run, hour, day, and more), and Qualytics compares each new point to real historical behavior using static guardrails, dynamic baselines that adapt to trends and seasonality, or both at once. This makes spikes, drops, trend breaks, seasonality violations, and data gaps visible as soon as they emerge, helping teams catch systemic failures like partial loads, duplication, drop-offs, and upstream outages early, with less alert noise and far less manual tuning.
Key capabilities include:
Static and dynamic rules:
Combine hard guardrails with adaptive baselines that learn normal behavior over time.
Metric monitoring across layers:
Apply rules to raw fields, derived metrics, and rollups.
Adaptive detection:
Catch spikes, drops, trend breaks, seasonality violations, and gaps without hand-tuned thresholds.
API First Architecture
Enforce data quality everywhere through APIs.
Defining data quality rules is only half the job. The other half is enforcing them wherever data is created and used, including applications, pipelines, warehouses, and lakes. When data quality tools are UI-centric or expose limited APIs, teams rely on manual workflows that are hard to automate, slow to maintain, and difficult to keep consistent as the data stack changes.
Qualytics is API-first, so every platform capability is accessible through REST APIs, with common workflows also available via a CLI. This makes quality standards callable wherever your data runs, letting teams embed profiling, validation, and monitoring into orchestrators, pipelines, and CI/CD as automated gates, scheduled checks, or event-driven controls rather than manual steps. The UI supports collaborative rule definition, anomaly review, and investigation, while the API and CLI operationalize those shared standards across engineering workflows at scale so data quality keeps pace with delivery velocity and changing business needs.
Key capabilities include:
Orchestrator, pipeline, and CI/CD integration:
Trigger profiles, scans, and metric rules as native pipeline steps or quality gates, then fail or warn based on scan outcomes.
Programmable, portable rule management:
Version, promote, roll back, and export/import rules through code so standards stay consistent across dev, stage, prod, teams, and data products.
Asset registration and management:
Register datastores and containers (tables, views, files, computed assets) as governed targets tied to the data asset lifecycle.
Operational anomaly access:
Query anomalies and break records with full context via API, then route them into incident and remediation workflows in systems teams already use (e.g., Slack, Jira).
Ready to see Qualytics in action?
See how Qualytics tackles data quality challenges across your stack.
