Fragmented Checks & High Maintenance
Quality rules are duplicated across every pipeline with no reusable contracts. Every new dataset means starting from scratch, and every rule change means touching a dozen places at once.
Enterprise Data AI — Semantic Layer
Justice for your data. Order for your enterprise.
A multi-layered Data Quality Operating System that bridges raw data and trusted business intelligence - with governance, rules, and compliance built in from day one.
Most organizations don’t have a data problem. They have a data trust problem. And the tools they’re using were never designed to solve it.
Quality rules are duplicated across every pipeline with no reusable contracts. Every new dataset means starting from scratch, and every rule change means touching a dozen places at once.
Hardcoded rules without historical baselines can’t detect when your data quietly shifts over time. By the time you notice, the damage is already downstream in your reports, your models, your decisions.
Validation failures land as “Regex mismatch” or “Null constraint violation.” Nobody in the business can act on that. The gap between the error and the business impact stays invisible.
Rules, profiling metrics, and contextual metadata disappear between runs. Every execution starts cold. The institutional knowledge your team builds around data quality exists only in people’s heads.
Bad data does not stay put. It flows downstream into rework, reconciliation, and decisions made on the wrong numbers. Estimate what your team spends cleaning up after untrusted data, and how much of that ThemisData gives back.
Use the figures you already know about your team.
The 40% reflects ThemisData's published reduction in downstream rework. The rest is driven by your own estimate of the hours the team loses to bad data, converted against a 40 hour week. Figures are directional, meant to frame the conversation rather than replace a detailed assessment.
ThemisData operates across three layers simultaneously, each one doing what the others can’t.
Fast, transparent execution of fundamental rules that run on every dataset, every time, no exceptions. The non-negotiable control floor before intelligent analysis begins.
Behaves like a senior data analyst, finding what you didn’t know to look for. Specialized agents mine hidden behavioral patterns, infer semantic meanings, and explain anomalies in plain business language.
Every approved rule becomes a versioned, reusable enterprise asset. System memory stores context, manages rule lifecycle, and turns approved findings into policies that compound over time.
Six quality dimensions, scored live. Completeness, Uniqueness, Validity, Accuracy, Consistency, Exploratory, every dataset profiled across every axis. The amber and red scores aren’t failures; they’re exactly what Themis AI is built to surface and fix.
These aren’t features. They’re architectural decisions that determine whether a data quality system scales, or collapses under its own weight.
The observed fact, such as a 12% null rate, never gets mixed with the rule or the execution result. Auditability requires that separation.
No monolithic all-in-one LLM. Multiple focused agents, including Intake, Profiling, Semantic Labeling, and Rule Mining, each handle a single responsibility.
Agents propose and explain. Actual validation executes deterministically through Python and pandas: reliable, fast, repeatable, every time.
AI suggestions don’t become policy without steward review. Approved rules become versioned, reusable contracts. Nothing is automatic truth.
Not every check runs every time. Dynamic logic runs minimum controls always, while intelligent discovery wakes only when needed.
Every failed validation includes full context: failing counts, percentages, data samples, root-cause hints, and suggested remediation paths.
Governed merging in action. Upload → Detect Differences → Apply & Version. Every dataset change is tracked, compared, and versioned automatically.
Deterministic validation, agentic intelligence, and human governance, integrated into a single sequential flow that compounds value with every run.
Reads input files, detects encoding and delimiters, normalizes headers, and captures full metadata context.
Computes structural and statistical distributions, infers datatypes, and flags obvious anomalies immediately.
Always-on deterministic checks via Python and pandas: schema validity, nulls, duplicates, and typing. No exceptions.
Agents analyze hidden behavioral patterns, infer semantic meanings, and mine candidate business rules.
Framework compiles semantic labels, rule candidates, threshold recommendations, and severities into a unified pack for review.
Data stewards review AI-generated proposals and accept, reject, or adjust thresholds before promoting them to active policies.
Approved checks, semantic mappings, metric histories, and dataset fingerprints are stored for reuse across every future run.
Engine intelligently executes recurring runs with wake/sleep optimization logic: compound value, minimal compute waste.
Comprehensive scorecards, business-readable narratives, drift alerts, and actionable failed row extracts are generated automatically.
Datasets organized by initiative, team, or domain. Each project tracks quality independently and accumulates a full history of runs — giving leadership a live view of data health across the entire enterprise.
ThemisData is built for the leaders who own the data problem, and the consequences when it’s wrong.
Your board wants data governance. Your auditors want evidence. Your regulators want audit trails. ThemisData gives you all three, automatically, on every dataset, with every run.
Your ML models are only as good as the data they train on. Your analytics are only as reliable as the pipelines feeding them. ThemisData is the quality gate your AI strategy depends on.
Compliance failures happen when rules exist in spreadsheets and enforcement depends on individuals. ThemisData turns your data policies into versioned, automatically-enforced contracts.
Themis was the Greek titaness of justice, order, law, and balance. We named this platform after her because your data deserves the same standard: governed by rules, enforced consistently, with full transparency on every decision. No exceptions. No workarounds. No silent failures.
Most organizations don’t know what’s wrong with their data until it’s already wrong in production. One dataset. One session. Full transparency on what your current tools are missing.
Practical answers about ThemisData, data quality, data governance, policy enforcement, versioned data contracts, deterministic validation, drift detection, auditability, and AI-ready data controls.
ThemisData is IWConnect’s AI-powered data quality and governance platform. It turns data policies into versioned, automatically enforced contracts with deterministic validation, human approval, drift detection, and full audit evidence.
Traditional data quality tools often rely on fragmented checks, static thresholds, and technical error messages. ThemisData combines AI-assisted discovery with deterministic validation, versioned contracts, business-readable explanations, human approval, and evidence trails that support governance and compliance.
ThemisData validates data through deterministic checks that can be executed consistently and repeatedly. AI can help discover issues, suggest rules, explain anomalies, and package proposals, but approved validation logic is enforced through controlled, repeatable data-quality contracts.
ThemisData uses AI to assist with profiling, anomaly discovery, rule proposals, explanations, and governance intelligence. Enforcement is controlled through approved policies, versioned contracts, deterministic validation, and human approval so teams can keep control over what becomes active.
Versioned data contracts define the rules that data must satisfy before it is trusted for reporting, AI workloads, analytics, or downstream processing. Versioning keeps a history of what changed, who approved it, when it changed, and which validation rules were active at each point in time.
ThemisData detects drift by comparing new data runs against approved baselines, expected distributions, structural patterns, value ranges, business rules, and historical behavior. This helps identify silent changes before they break reports, downstream processes, or AI outputs.
ThemisData supports compliance and auditability by preserving evidence for validation runs, rule proposals, human approvals, contract versions, failures, explanations, and trends. This helps teams show what was checked, what failed, what was approved, and why a dataset was considered trusted or blocked.
When a validation fails, ThemisData documents the failure with context, affected fields, detected patterns, business-readable explanations, severity, and supporting evidence. Teams can review the issue, approve or reject proposed rule changes, and decide whether the data should be corrected, blocked, or monitored.
ThemisData is built for data leaders, technology leaders, compliance teams, governance teams, analytics teams, and organizations preparing enterprise data for AI workloads. It is especially useful for CDOs, CTOs, CCOs, data engineering teams, and teams responsible for data trust, audit evidence, and regulatory control.
The best starting point is one dataset, data pipeline, or business-critical data domain where quality, trust, compliance, or AI readiness matters. IWConnect can profile the data, identify baseline issues, propose minimum controls, define versioned contracts, and show how ThemisData creates governed validation evidence.
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