Learn how Gentic builds a privacy sensitive pipeline for data ingestion.

---

Introduction

With the increase in LLM and AI use, transforming data from multiple disparate unstructured sources into structured LLM-ready formats has become even more important. This architecture presents a foundation to build an LLM-ready data pipeline to prepare, ingest, load and transform data into formats most suitable for access by AI Agents and for integrations with LLM-enabled tools, while also providing functional layers to preserve privacy and mask sensitive data.

The key objectives of the architecture are -

• Define data ingestion and loading mechanisms for real time data ingestion with low-latency streaming capabilities.
• Provide a multi-modal data transformation layer to transform, enrich, and index data for agentic AI, LLM, and application querying use cases.
• Present a privacy-preserving solution to mask and de-ID sensitive data.

Data Preparation & Ingestion

Addressing the challenge of integrating information from a heterogeneous landscape of sources is fundamental to powering effective Agentic AI systems. This foundational layer of the architecture establishes robust mechanisms to ingest data from diverse origins—ranging from unstructured documents and images to structured databases, APIs, and cloud storage platforms. It provides the essential groundwork for transforming raw information into an AI-ready state.
The architecture proposes data ingestion from various sources including:

• Unstructured document types in the form of files and collections.
• Structured data sources like databases, and other relational and schematic stores.
• Application data sources including API integrations, cloud storage and data warehouse integrations.
• Realtime data sources and analytic sources ranging from event-based messaging, web hooks to Kafka-style signal streaming services.

Change Data Capture Engine

To truly empower Agentic AI, simply connecting to different data sources isn't enough. This foundational layer must nimbly manage the flow of information as well as its origin. Beyond just pulling in documents, databases, APIs, and real-time feeds, the architecture proposes capabilities to handle both large-scale, periodic data loads through batched ingestion and the constant influx of information via high-volume ingestion. This ensures we can efficiently process both historical troves of data and the rapid pulse of ongoing updates, providing a comprehensive and timely data foundation for intelligent agents.
The CDC engine provides a standard ETL/ELT style multi-phase data transformation

• AI-powered data extraction to convert incoming streams of data into structured, processable entities.
• Node-based loaders to load data into chunks for knowledge processing and indexing.
• Transformers to convert chunks into relational vector entities and knowledge-graph formats.

AI-powered Data Extraction

The extraction phase represents a critical transformation point where raw source material is converted into processable content through intelligent ai-powered parsing mechanisms. This also provides the essential framework for handling diverse data structures—from standardized formats like JSON, XML, and CSV to proprietary layouts requiring custom extractors. Each source type demands tailored configuration settings that govern extraction frequency, authentication credentials, and specialized parsing rules.

Beyond conventional structured data, AI-based extraction capabilities elevate the system's ability to process historically challenging document types. Advanced computer vision and natural language processing techniques extract meaningful text, tables, and relationships from PDFs with complex layouts, scanned documents, and images containing textual information. These AI-powered extractors must transform previously inaccessible information locked in visual formats into structured, semantically meaningful content ready for LLM consumption—converting unstructured visual information into the precisely formatted textual data that foundation models can effectively process, reason about, and incorporate into their knowledge frameworks.

Loading, Chunking and Indexing Data for Knowledge Processing

Data loaders represent the crucial bridge between raw extracted content and the structured knowledge representation required by intelligent agents. These specialized components implement sophisticated ingestion mechanics calibrated to the unique characteristics of each data type, ensuring optimal processing efficiency. Configurable chunking strategies form the cornerstone of this process, with the architecture supporting dynamic segmentation based on document structure, semantic boundaries, token limits, and content relationships. For technical documents, chunking may preserve hierarchical integrity by respecting heading levels and section boundaries; conversely, narrative content might be segmented to maintain thematic coherence across related passages. The chunking configuration adapts not only to file types—recognizing the structural differences between spreadsheets, PDFs, and plain text—but also responds to metadata tags that signal specific handling requirements. This tag-driven approach enables precise control over how specialized content like legal documents, code snippets, or tabular data gets processed, ensuring that the resulting chunks maintain appropriate context and relationships while optimizing for downstream vector embedding and knowledge graph construction.

Node Processors function as sophisticated enrichment engines that operate on chunked data to amplify its contextual value and improve relevance of results for agentic interactions. These processors apply layered augmentation techniques that imbue raw content with critical metadata dimensions—ranging from entity identification and semantic classification to sentiment analysis and domain-specific terminology extraction. The intelligence of these processors lies in their configurability, allowing for specialized processing pipelines tailored to the unique characteristics of different content types. Technical documentation chunks might trigger processors that identify code blocks, API references, and technical parameters; while legal texts could engage processors that recognize jurisdictional markers, statutory references, and defined legal terms. This tag-driven and filetype-responsive processing ensures that all content receives appropriate enrichment without unnecessary computational overhead, creating chunks that carry not only their core content but also a rich constellation of attributes that significantly enhance downstream retrieval precision. By performing this enrichment at the chunk level rather than on entire documents, the architecture preserves granular context that might otherwise be lost in broader processing approaches, ensuring that agents can access precisely the right information granularity for specific reasoning tasks.

Knowledge graph builders act as intelligent weavers, forging meaningful connections across the diverse tapestry of ingested data. By allowing the declaration of explicit rules, these components can discern and extract inherent relationships that might otherwise remain latent within disparate sources. This rule-based approach empowers the architecture to go beyond simple data storage, actively constructing a rich semantic network where entities and concepts are linked by clearly defined relationships. Whether identifying customer-product interactions from transactional databases or uncovering author-paper connections within document collections, these builders transform isolated data points into an interconnected web of knowledge, providing Agentic AI with a powerful framework for contextual understanding and insightful reasoning across the entire information landscape.

Knowledge Graph Builders represent the architecture's relational intelligence layer, systematically weaving isolated chunks into an interconnected semantic fabric that captures the multidimensional relationships inherent in the data ecosystem. These specialized components transcend simple keyword associations by also implementing rule-based relationship extraction that identifies meaningful conceptual, causal, hierarchical, and temporal connections between entities across disparate data sources. Additionally, the architecture proposes modeling sophisticated relationship patterns through both explicit rule definitions and implicit pattern recognition—specifying, for instance, that personnel records should form organizational relationships with project documents, or that technical specifications should link to relevant implementation examples. The graph construction process employs entity reconciliation algorithms to identify when seemingly disconnected mentions across various sources reference the same underlying concept, while relevance scoring mechanisms ensure that only meaningful relationships populate the knowledge structure. This intentional relationship building transforms the previously flat dimension of chunked content into a richly navigable knowledge network that agents can traverse with contextual awareness, following relationship pathways that mirror the natural connections within the domain—enabling agents to not merely retrieve isolated facts but to understand them within their broader conceptual ecosystem and reason about the complex interrelationships that define real-world knowledge structures.

Privacy-preserving pipelines - To safeguard sensitive information while empowering Agentic AI, the architecture incorporates privacy-preserving pipelines as a fundamental layer. These pipelines are designed to meticulously mask confidential data and facilitate de-identification directly at the source, adhering to specified patterns. Crucially, they also provide a mechanism for secure re-identification at the destination when absolutely necessary and under strict authorization, ensuring a balance between data utility for AI processing and the paramount importance of user privacy. This controlled approach to data anonymization and pseudonymization allows for responsible innovation with sensitive datasets.

Node Retrievers, Chunk Reranking, and Time-Weighted Querying

Reranking algorithms configurable based on content type
To ensure Agentic AI retrieves the most pertinent information, the architecture incorporates intelligent Reranking algorithms. These algorithms go beyond initial similarity matching, dynamically adjusting their scoring mechanisms based on the specific content type of the retrieved data. By understanding the nuances inherent in different forms of information – be it technical documentation, customer reviews, or financial reports – the reranker can prioritize the most relevant chunks based on content-specific features, ultimately leading to more accurate and contextually appropriate responses from the AI agents

Time-weighted retrievers to accommodate recency
Recognizing that the value of information often decays with time, the architecture incorporates Time-weighted retrievers. These intelligent components factor in the recency of the data chunks during the retrieval process. By assigning higher scores to more recently ingested or updated information, the system ensures that Agentic AI can prioritize the most current and relevant context when responding to queries, allowing it to operate with up-to-date knowledge and avoid reliance on stale or outdated data.

Self-querying retrievers for relational vector queries
To enable Agentic AI to perform more sophisticated and nuanced information retrieval, the architecture includes self-querying retrievers. These advanced mechanisms empower the AI agents to go beyond simple keyword or vector similarity searches. Instead, they can intelligently formulate relational vector queries that incorporate structured attributes and relationships inherent in the underlying data. This allows for retrieval based not only on semantic similarity but also on specific criteria and connections between data points, significantly enhancing the precision and contextual relevance of the information surfaced for the agents.

Data transformers to build relational links and preserve privacy

Building knowledge graphs from the data chunks
To unlock deeper insights and contextual understanding for Agentic AI, the architecture features sophisticated Data Transformers. These components are engineered to construct a rich knowledge graph directly from the processed data chunks. By identifying rules-based relationships within the content, they forge meaningful connections between disparate pieces of information. Furthermore, these transformers must intelligently enrich the data based on its specific content type, adding relevant metadata, semantic annotations, and contextual information that further enhances the knowledge graph's utility and the AI agents' ability to reason and draw inferences across the interconnected data landscape.

To further refine the data for Agentic AI, the architecture employs Data Transformers to enrich vector chunks through the application of specified transformation rules. These rules enable the injection of additional contextual information, semantic tags, or calculated features directly into the vector representations. By tailoring these transformations to the specific characteristics of the data and the anticipated needs of the AI agents, the system ensures that the vector embeddings capture not just the core meaning but also crucial supplementary details that enhance retrieval accuracy and the overall reasoning capabilities of the intelligent agents.

Masking PII data
To uphold the critical principle of data privacy, the Data Transformers incorporate mechanisms to preserve privacy by masking sensitive data according to predefined rules. These rules dictate how personally identifiable information (PII) or other confidential data is identified and obfuscated within the data chunks before they are further processed or indexed. This ensures that the underlying data can be utilized for training and inference by Agentic AI while minimizing the risk of exposing sensitive details, striking a crucial balance between data utility and user confidentiality.

Knowledge as a Service

The architecture's destination layer presents a multi-modal knowledge delivery system designed to serve the diverse retrieval needs of modern AI applications. At its core, the system populates three complementary knowledge structures—each optimized for specific query patterns and reasoning modalities. Vector stores form the semantic foundation, housing high-dimensional embeddings that capture the nuanced meaning of each processed chunk, enabling similarity-based retrieval that transcends keyword limitations. Knowledge graphs provide the relational dimension, representing entities and their interconnections in a navigable network that agents can traverse to follow conceptual pathways and discover indirect relationships. Traditional data warehouses complete the triad by maintaining structured records with precise schema definitions, supporting complex analytical queries and aggregations that complement the more fluid semantic and relational retrieval methods while also acting as the source of truth in the data ecosystem.

This knowledge infrastructure exposes its capabilities through a unified service layer that abstracts the underlying complexity while offering specialized access patterns. AI agents can leverage context-aware retrieval that dynamically blends vector similarity, graph traversal, and structured queries based on the specific reasoning task at hand. Integration-focused applications access the knowledge via purpose-built APIs that expose domain-specific endpoints tailored to common workflow patterns. Direct API access enables both semantic and topical queries that return precisely formatted responses optimized for downstream consumption. This comprehensive knowledge delivery system ensures that whether an agent needs to retrieve factual information, explore conceptual relationships, or analyze patterns across multiple data dimensions, the architecture provides the right knowledge modality through the right access mechanism—transforming raw data not merely into structured information but into actionable intelligence that powers truly knowledgeable AI interactions.

Ready to build with Agentic AI?

📩 Get in touch now! Contact us to learn more.

📩 Email us at karthik.bacherao@gentic.in to get started.

📩 Visit gentic.in to learn more.