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RAG Infrastructure for Enterprise

Enterprise Knowledge.
Instantly Accessible. Transform. Built for Production. Searchable.

Obsygnal builds Retrieval-Augmented Generation systems that connect enterprise knowledge to AI — enabling trusted answers, intelligent search, and operational efficiency at scale.

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THE CHALLENGE

The Enterprise
Knowledge Problem

PDFs
SharePoint
Confluence
Emails
Databases
Tickets
Wikis
Internal Docs

Organizations generate massive amounts of knowledge every day — but most of it remains inaccessible when people need it most. Critical expertise becomes trapped in systems and documents, invisible to the people and systems that could use it.


Modern AI is extraordinarily powerful. But without access to trusted organizational knowledge, it cannot deliver meaningful, grounded results. The bottleneck is not the model — it's the connection.

Employees spend 20–30% of their workday searching for information across disconnected systems

Customers wait for answers that exist in internal documentation but can't be surfaced quickly

Critical operational knowledge disappears when team members leave the organization

AI deployments fail because they hallucinate — lacking access to authoritative enterprise context

ARCHITECTURE

How Obsygnal
Solves It

Every system we build follows a rigorous retrieval architecture — from raw enterprise data through intelligent processing, embedding, indexing, and reasoning to trusted, citable answers.

01
Enterprise Data Sources
SharePoint, Confluence, S3, databases, APIs, email, wikis
02
Ingestion Pipeline
Batch + streaming connectors, change detection, schema normalization
03
Document Processing
Chunking strategies, metadata extraction, hierarchy preservation
04
Embedding Models
Domain-fine-tuned models, multi-modal embeddings, sparse encoding
05
Vector Database + Knowledge Graph
Hybrid stores: Qdrant, Weaviate, Pinecone + Neo4j / graph layers
06
Hybrid Retrieval
Dense + sparse retrieval, cross-encoder reranking, contextual fusion
07
Large Language Model
Grounded generation with citations, hallucination detection, RAG eval
✓
Trusted Enterprise Answers
Cited, auditable, grounded responses with full observability
SOLUTIONS

What We Build

From employee knowledge assistants to operational intelligence systems — we build RAG infrastructure tailored to your specific knowledge landscape.

01
Enterprise Knowledge Assistants
Allow employees to have intelligent conversations with internal documentation, policies, and organizational knowledge. Grounded answers with full source attribution.
02
Customer Support RAG
Ground AI responses using support documentation and knowledge bases. Reduce ticket resolution time while improving answer accuracy and consistency.
03
Engineering Knowledge Platforms
Make architecture decisions, RFCs, ADRs, technical documentation, and operational runbooks instantly searchable and accessible across your engineering organization.
04
Security & Operations Intelligence
Surface incidents, postmortems, runbooks, and operational intelligence on demand. Accelerate incident response and reduce mean time to resolution.
05
Compliance & Policy Assistants
Enable rapid access to policies, procedures, and regulatory documentation. Reduce compliance risk by ensuring teams always find the right, current policy.
06
Custom Enterprise RAG Platforms
Tailored retrieval systems designed around your specific business workflows, data landscape, compliance requirements, and integration architecture.
WHY OBSYGNAL

Why Organizations
Choose Obsygnal

Engineering Excellence
Production-grade systems built for reliability, observability, scalability, and long-term maintainability. We don't build demos — we build systems that run in production for years.
Knowledge Intelligence
Deep expertise in information retrieval, vector search, knowledge graphs, hybrid retrieval, and enterprise AI. We understand knowledge — not just technology.
Business Impact
Measurable outcomes: reduced search time, faster decisions, accelerated onboarding, and unlocked organizational knowledge. Every system we build is tied to a business result.
0
Hours Saved
0
Knowledge Sources Connected
0
Documents Indexed
0
Answers Generated
RAG EXPERTISE

Our RAG Capabilities

Deep technical expertise across the full spectrum of modern retrieval-augmented generation — from classical pipelines to cutting-edge agentic architectures.

Classical RAG
Foundational retrieval-augmented generation pipelines with chunking, embedding, vector search, and LLM generation. The reliable baseline we optimize from.
Hybrid Search
Combining dense vector search with BM25 sparse retrieval using Reciprocal Rank Fusion. Consistently outperforms either approach alone across diverse query types.
Semantic Search
Meaning-aware search that understands intent beyond keywords. Fine-tuned embedding models capture domain-specific semantics for enterprise knowledge domains.
Knowledge Graph RAG
Graph-structured retrieval for complex enterprise knowledge. Traverse entity relationships, surface implicit connections, and navigate organizational knowledge hierarchies.
Agentic RAG
AI agents that autonomously plan retrieval strategies, decide when to search, synthesize across multiple retrievals, and iteratively refine answers for complex queries.
Multi-Hop Retrieval
Chain multiple retrieval steps to answer questions requiring reasoning across disparate document sources. Essential for complex enterprise knowledge workflows.
Contextual Retrieval
Enriching chunks with contextual metadata before embedding — dramatically improving retrieval precision for long documents and knowledge hierarchies.
Re-Ranking
Cross-encoder reranking of retrieved candidates against the original query. Substantial precision improvements that transform retrieval quality in production systems.
Retrieval Evaluation
RAGAS, LLM-as-judge, and custom evaluation frameworks. We measure retrieval precision, recall, faithfulness, and answer relevancy systematically.
AI Observability
Full pipeline observability with tracing, logging, and monitoring for every retrieval operation. Know exactly why every answer was generated and how to improve it.
Citation Tracking
Every answer is fully traceable to its source documents. Enterprise-grade citation systems that build trust and enable humans to verify AI-generated responses.
Hallucination Reduction
Systematic approaches to grounding, faithfulness scoring, and factual consistency checks. We engineer hallucination out of enterprise RAG systems.
INSIGHTS

From the Knowledge Lab

Deep technical writing on retrieval architecture, enterprise AI, and the future of organizational knowledge.

Architecture 12 min read
The Evolution of RAG: From Naive Pipelines to Production Intelligence
How retrieval-augmented generation has matured from a simple retrieve-and-generate pattern into a sophisticated family of architectures that power enterprise knowledge systems.
Read article
Knowledge Graphs 9 min read
Knowledge Graphs and Enterprise Intelligence
Why knowledge graphs are the missing layer in most enterprise RAG systems — and how graph-based retrieval fundamentally changes the quality of answers you can achieve.
Read article
Agentic AI 15 min read
Agentic RAG Architecture Patterns for the Enterprise
A comprehensive look at how autonomous retrieval agents differ from static pipelines — and the architectural patterns that make them reliable enough for enterprise deployment.
Read article
Search 7 min read
Why Enterprise Search Is Broken — And How RAG Fixes It
The fundamental failure modes of keyword search in large organizations, and why semantic retrieval with grounded generation represents a structural improvement, not just an incremental one.
Read article
Production 11 min read
Building Reliable AI Systems: Observability in the Retrieval Layer
The principles and practices that separate reliable production RAG systems from brittle prototypes — with a focus on tracing, evaluation, and continuous improvement.
Read article
THE TEAM

The Builders Behind
Obsygnal

We believe the future of enterprise AI depends on trusted knowledge. Obsygnal was founded by engineers, architects, and problem-solvers who are passionate about helping organizations unlock the value hidden inside their information — and turning it into real, operational intelligence.

BK
Bikram Sarkar
Co-Founder
Machine Learning engineer specializing in information retrieval and enterprise search infrastructure.
AK
Akhil Dhanpal
Co-Founder
ML systems architect with deep expertise in vector databases, knowledge graphs, and production LLM infrastructure.
NP
Naveen Pawar
Co-Founder
Enterprise software architect focused on designing scalable, observable, and maintainable knowledge systems.
SG
Souradeep Ghosh
Co-Founder
NLP and information retrieval, focused on pushing the boundaries of RAG evaluation and accuracy.
NS
Nil Sinha
Co-Founder
Data engineer with a passion for building tools that turn data into actionable insights.
Our Philosophy

Knowledge should not be trapped.Every piece of organizational knowledge should be accessible to the people who need it, when they need it.

Information should be discoverable.The right answer exists somewhere in your organization. It should take seconds, not days, to find it.

AI should be grounded in truth.Generative AI must be anchored in verified, organizational knowledge — not trained intuitions about what might be true.

Technology should amplify human expertise.The goal is not to replace human judgment — it's to ensure human knowledge is always findable and actionable.

HOW WE WORK

From Discovery to
Deployed Intelligence

1
Discovery
Understand
Map your knowledge landscape, business challenges, and success criteria. Define what "good" looks like.
2
Architecture
Design
Design the optimal retrieval architecture for your specific knowledge sources, query patterns, and scale.
3
Build
Implement
Build production-grade retrieval systems with observability, evaluation, and enterprise-grade reliability baked in.
4
Deploy
Integrate
Integrate seamlessly with your existing enterprise workflows, tools, and authentication infrastructure.
5
Optimize
Improve
Continuously measure retrieval quality, monitor production behavior, and improve system accuracy over time.
YOUR ANSWERS ARE ALREADY THERE

Your Organization
Already Has
The Answers

Let's make them accessible.

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Tell us about your knowledge challenge

We work with enterprises, mid-market companies, and funded startups that are serious about building production-grade RAG infrastructure. Every engagement starts with a focused discovery conversation.

hello@obsygnal.com
Hyderabad, Telangana, India · Serving enterprises globally