Your teams are stuck with search tools that treat a query for "Q4 pipeline risk" the same as a web search. They return documents containing those words, not the nuanced analysis your sales leaders need. This creates critical knowledge gaps and slows decision-making.
Service
Enterprise Semantic Search RAG Development
Build search systems that understand your business context. We develop domain-aware RAG with knowledge graphs to deliver precise, actionable answers from internal documentation, reducing support overhead and accelerating decision-making.
WHY TRADITIONAL SEARCH FAILS
The Problem with Generic Enterprise Search
Keyword-based systems cannot understand business context, leading to irrelevant results and wasted time.
Generic search lacks semantic understanding of your proprietary data, processes, and jargon.
The core failures include:
- No domain awareness: Cannot distinguish between "Java" (coffee), "Java" (island), and "Java" (code).
- Fragmented context: Treats each document as an island, missing connections across your CRM, wikis, and support tickets.
- High recall, low precision: Floods users with hundreds of marginally relevant results, forcing manual filtering.
- Static indexing: Cannot incorporate real-time data from streaming sources like
Kafkaor live dashboards.
This isn't just an IT problem—it's a productivity tax. Teams waste hours weekly searching, while critical insights remain buried in PDF archives and legacy SQL databases. For a deeper technical dive on building the underlying infrastructure, explore our guide on Retrieval-Augmented Generation (RAG) Infrastructure. To solve the specific challenge of unifying fragmented data, see our service for RAG for Legacy Data Silos Integration.
ENTERPRISE-GRADE RESULTS
Measurable Outcomes for Your Business
We deliver semantic search RAG systems that move beyond prototypes to production-grade infrastructure, providing clear, quantifiable improvements to your operations and bottom line.
01
80% Reduction in Search Time
Deploy semantic search that understands business context and jargon, cutting average query resolution time from minutes to seconds. Our systems deliver precise, actionable answers from internal wikis, documentation, and knowledge bases.
< 200ms
P95 Query Latency
> 95%
Answer Relevance
02
40% Lower Hallucination Rates
Implement advanced retrieval strategies with hybrid search and query routing grounded in your proprietary data. We engineer RAG pipelines that prioritize accuracy and source attribution, building user trust.
> 99%
Source Grounding
< 2%
Factual Error Rate
03
Production Deployment in 4-6 Weeks
Accelerate from concept to live system with our proven development framework. We handle vector database integration, pipeline orchestration, and API deployment, ensuring a scalable launch on your infrastructure.
4-6 weeks
Time to Production
99.9%
Uptime SLA
Transparent Project Roadmap
Typical Development Timeline & Deliverables
A clear breakdown of the phases, key outputs, and timeline for developing a production-ready Enterprise Semantic Search RAG system, from initial architecture to final deployment and optimization.
| Phase & Key Deliverables | Weeks 1-4 | Weeks 5-8 | Weeks 9-12+ |
|---|---|---|---|
Discovery & Architecture Design | |||
Data Pipeline & Semantic Chunking Engine | |||
Vector Index & Hybrid Search Implementation | |||
RAG Pipeline & LLM Integration | |||
Performance Tuning & Security Hardening | |||
Production Deployment & API Launch | |||
Core Deliverables | Technical Design Document, POC | Indexed Knowledge Base, Search API | Production API, Integration Guides, SLA |
Team Involvement | Solution Architect, PM | ML Engineer, Data Engineer | DevOps, Security Engineer, Your Team |
Client Milestone | Architecture Sign-off | Search Accuracy Validation | Go-Live & Handoff |
ENTERPRISE-GRADE RAG
Core Technical Capabilities We Deliver
We architect semantic search systems that deliver precise, actionable answers from your proprietary data. Our focus is on accuracy, security, and seamless integration with your existing tech stack.
01
Domain-Aware Semantic Search
We build search systems that understand your business jargon and context. Using knowledge graphs and entity recognition, we ensure queries return precise answers from internal wikis, documentation, and technical manuals.
40%+
Reduced Hallucination
Sub-100ms
Query Latency
02
Advanced Chunking & Embedding Strategy
We implement sophisticated semantic chunking and embedding pipelines using models like BGE or OpenAI's text-embedding-3. This ensures the most relevant context is retrieved, dramatically improving answer quality and reducing irrelevant results.
95%+
Retrieval Accuracy
Multi-Modal
Data Support
03
Production-Grade RAG Pipeline Engineering
We deliver robust, scalable RAG pipelines built with frameworks like LlamaIndex and LangChain. Our systems feature automated ingestion, real-time indexing, and rigorous monitoring for continuous accuracy and performance.
99.9%
Uptime SLA
2-4 Weeks
Deployment Time
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
Read more
Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
Read more
Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read moreEnterprise Semantic Search RAG
Frequently Asked Questions
Get specific answers about our development process, timelines, and outcomes for building domain-aware semantic search systems.
We follow a structured 4-phase methodology: 1) Discovery & Architecture (1-2 weeks) to map your domain-specific jargon, data sources, and performance requirements. 2) Prototype & Validation (2-3 weeks) where we build a proof-of-concept with a subset of your data to validate retrieval accuracy. 3) Production Development (3-6 weeks) for full-scale pipeline engineering, including knowledge graph integration and entity recognition. 4) Deployment & Handoff (1 week) with comprehensive documentation and training. This process is informed by our experience delivering 50+ enterprise RAG projects.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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