AI Readiness

Maximize your Extractive and Generative AI initiatives – and reduce risk – with clean,
curated, and trustworthy AI-ready content.

A precise combination of intelligent software & expert services.

PowerHouse

AutoClassification Engine scans & analyzes content

BlackCat

Metadata Visualization displays content & metadata

Connectors

to source repositories process data in place

Professional Services

Subject matter experts in IG strategies & best practices

Clean data is the foundation of AI

Large enterprises often face significant obstacles when applying AI algorithms to their data – specifically Extractive and Generative AI – due to the presence of ROT (Redundant, Obsolete, Trivial) and unknown data. Issues such as poor data quality, duplication, irrelevance, questionable provenance, and unstructured formats make it difficult for AI engines to deliver accurate and meaningful insights. Additionally, large volumes of ROT data increase processing costs, while unknown data introduces blind spots that hinder AI effectiveness. These challenges are compounded by compliance risks and the inefficiencies of training models on low-quality data.

Identify content in-scope for AI

Valora helps organizations overcome these challenges by automatically organizing, cleaning, remediating, and tagging your data to ensure its AI-readiness and compliance. It eliminates redundancies, enriches metadata, and flags obsolete, questionable, or trivial information, ensuring only relevant, high-quality, and permissible data remains.

By streamlining data for AI use cases, Valora’s platform improves searchability, reduces compliance risks, and optimizes storage and computational costs. This ensures Generative AI models are trained on accurate and permissible data, enabling better insights, increased efficiency, and enhanced decision- making, without compromise.

Connectors

Valora PowerHouse connects to structured and unstructured data systems to crawl and analyze content in place. Valora develops these custom connectors using APIs or other direct database extraction methods to access the content at source. Some examples of systems we connect to are:

On-prem unstructured systems: Windows fileshares.
On-prem structured systems: ECMs, databases, etc.
Cloud unstructured systems: SharePoint online, Dropbox, Box, etc.
Cloud structured systems: Data lakes/warehouse, Oracle, MSSQL, etc.
Could applications: Salesforce, Workday, SAP, NetSuite, etc.
Email systems: Microsoft 365, Google mail, etc.

Professional Services

Valora’s team of subjsect metter experts

First Look Dashboard

Valora’s AutoClassification Suite includes:

List View

Valora’s AutoClassification Suite includes:

Document Preview

Valora’s AutoClassification Suite includes:

On-Demand Reporting

Valora’s AutoClassification Suite includes:

Chronological Reporting

Valora’s AutoClassification Suite includes:

Benefits of using Valora for AI Readiness

Improved Data Quality

Valora improves source data quality by identifying and removing duplicate or irrelevant content (ROT), tagging permissible and impermissible use data, and ensuring that only relevant, high-value data is used for AI training and decision-making while minimizing the impact of “junk” data.

AI Model Training Optimization

Well-classified data ensures cleaner, more reliable input for AI models, improving training outcomes, while organizing data with metadata and contextual information enhances AI’s ability to understand and process information effectively.

Enhanced Data Governance

By properly classifying sensitive and regulated data, Valora’s solution ensures compliance and security, streamlining the application of governance rules while enabling AI readiness by restricting access to authorized data only.

Data Organization & Structure

AutoClassification enhances data accessibility by categorizing unstructured, structured, and semi-structured data for easier retrieval and analysis, while establishing standardized taxonomies to ensure consistent labeling and use across the data estate.

Accelerated AI Deployment

Pre-classified data simplifies LLM preparation by reducing preprocessing time, enabling faster implementation and allowing curated datasets to feed directly into AI systems, accelerating time-to-value.

Data Enrichment

AutoClassification enhances AI capabilities by adding meaningful metadata to content, improving taxonomies, search, retrieval, and interpretation, while providing additional insights such as keywords, entities, and sentiment for more informed analysis.

AI Readiness FAQ

What kind of AI technology does Valora apply in its own platform?

Valora’s PowerHouse platform encompasses state-of-the-art content analysis and AutoClassification, incorporating elements of probabilistic systems, Bayesian learning, natural language processing (NLP) and machine learning (AI).

It does not incorporate any Generative AI (creating new data), but uses extractive algorithms to “read” and “pull out” data elements for automated classification of content from existing data sets.

What kinds of AI does Valora support?

Valora supports all formats of extractive and generative AI models, using industry-standard classification and output formats.

Do you have native integrations with Open AI/Chat GPT, Anthropic/Claude, etc.?

No. The typical technique involves using Valora to pre- or live-curate enterprise data, and then feed the approved corpus to an AI-accessible data lake or directly to the AI engine.

Can Valora exclude specific data from being accessed by AI engines?

Yes. Valora can prevent AI engines and enterprise search tools from accessing or using sensitive data by analyzing content to identify sensitive information and applying classification labels (ex. “Confidential” or “Restricted”). These labels enforce access control policies, ensuring only authorized users or systems can view or process the data. Additionally, Valora can redact or anonymize sensitive information, allowing non-sensitive parts to be analyzed securely. This approach ensures compliance with data protection policies while maintaining operational efficiency.