This demonstration provides and overview of Komprise Intelligent AI Ingest. Cut AI Costs and Risks. Boost RAG, LLM Accuracy and ROI.
Komprise Intelligent AI Ingest
Getting the right data for AI is important to ensure accuracy, cut high AI processing costs and most importantly, to ensure sensitive data isn’t incorrectly exposed to LLMs. This demonstration showcases how you can streamline the process of curating data for AI, leveraging a powerful combination of features within Komprise Deep Analytics and Smart Data Workflows.
Komprise Intelligent AI Ingest speeds the curation of the right unstructured data across disparate storage silos for AI, boosts AI ROI by eliminating the noise, data risk and high cost of using unstructured data in RAG and LLM pipelines.
_______________________
Read the Komprise Data Preparation Guide
How do enterprises ingest unstructured data into AI pipelines?
Enterprises today typically ingest unstructured data into AI pipelines through multi-stage processes that include data discovery, filtering, enrichment, and delivery into systems such as vector databases, data lakes, and Retrieval-Augmented Generation (RAG) frameworks. This commonly involves:
- discovering data across file shares, cloud, and object storage
- extracting content from formats like PDFs, images, and logs
- enriching metadata for context (owner, date, type, sensitivity)
- filtering irrelevant or duplicate data
- indexing or embedding content for AI retrieval
However, traditional approaches often ingest too much data without sufficient filtering, leading to higher costs and lower AI accuracy.
Komprise improves this process with Intelligent AI Ingest, which uses the Global Metadatabase to identify and deliver only relevant, high-value unstructured data. Combined with Smart Data Workflows and KAPPA data services, Komprise automates ingestion and prepares curated datasets for AI, without requiring bulk data movement.
What is the best way to prepare unstructured data for RAG and LLMs?
The most effective way to prepare unstructured data for RAG and large language models is to use a metadata-driven approach that prioritizes relevance, quality, and governance before ingestion. Key best practices include:
- identifying authoritative and current data sources
- enriching metadata to improve filtering and retrieval
- removing duplicate, stale, and low-value content
- structuring content for efficient indexing and chunking
- detecting and excluding sensitive data
Without these steps, RAG systems may retrieve irrelevant or outdated information, reducing accuracy and trust.
Komprise enables this process by:
- using the Global Metadatabase, Deep Analytics, and data tagging for AI to analyze and enrich all unstructured data
- applying Intelligent AI Ingest to select only relevant data
- leveraging Smart Data Workflows to automate preparation and processing
- using KAPPA to deliver custom data services, such as industry-specific metadata enrichment, without having to provision or manage the infrastructure to process the operation across large datasets
This ensures that AI systems are trained and queried against trusted, high-quality data.
How can I reduce AI costs by filtering unstructured data before ingestion?
AI costs are heavily influenced by the volume of data ingested, stored, embedded, and queried. Ingesting unnecessary data increases storage, compute, and inference costs without improving results.
To reduce costs, organizations should:
- eliminate duplicate and redundant files
- exclude stale or inactive data
- prioritize high-value, frequently accessed content
- avoid bulk ingestion of entire file and object storage environments
A metadata-driven AI ingestion strategy enables these optimizations.
Komprise helps reduce AI costs by:
- identifying up to 70%+ of irrelevant or cold data before ingestion
- using Intelligent AI Ingest to filter data at the source
- minimizing data movement and duplication
- automating selection and delivery with Smart Data Workflows
By ingesting only what matters, organizations can significantly lower infrastructure and AI processing costs while improving performance.
How do you prevent sensitive data from being exposed to AI systems during ingestion?
Preventing sensitive data exposure requires detecting and governing data before it enters AI pipelines. Unstructured data often contains hidden PII, PHI, financial records, and confidential information that can be unintentionally surfaced by AI systems.
Effective strategies include:
- scanning files for sensitive content using pattern matching and metadata analysis
- tagging and classifying sensitive data
- excluding or masking sensitive content before ingestion
- enforcing access and governance policies
Komprise integrates Sensitive Data Management directly into the AI ingestion process by:
- detecting sensitive data across file and object storage
- applying policies to exclude or remediate risky data
- integrating governance into Smart Data Workflows
- ensuring only approved datasets are delivered via Komprise Intelligent AI Ingest
This reduces risk and ensures compliance while enabling safe AI adoption.
Why do traditional ETL and data ingestion tools fail for unstructured data and AI?
Traditional ETL and ingestion tools are designed for structured data and rely on predefined schemas and bulk data movement. These approaches are not well suited for unstructured data or AI use cases.
Key limitations include:
- inability to handle diverse file formats and content types
- lack of metadata-driven filtering and enrichment
- reliance on ingesting entire datasets rather than selecting relevant data
- limited visibility across distributed storage environments
- no built-in handling of sensitive data
As a result, traditional tools often lead to inefficient pipelines, higher costs, and lower AI accuracy.
_______________________