About
Industry
- Enterprise Software / Knowledge Management
Application Type
- Web-Based Enterprise AI Platform
Core Functionality
- Enterprise AI search, AI knowledge assistant, and AI-powered knowledge management
This enterprise AI search platform was designed to address the growing challenge of fragmented organizational knowledge. In large enterprises, information is distributed across dozens of tools, making it difficult for employees to find accurate documents, conversations, tickets, or code when they need it.
The solution acts as an AI knowledge assistant and enterprise AI assistant, connecting with over 40 internal systems to create a unified AI-powered knowledge base. Through natural-language queries and contextual retrieval, employees gain instant access to trusted internal data while organizations maintain full control through secure, self-hosted deployment.
Results
Enterprise AI search and AI-powered knowledge management delivering measurable productivity gains
Information Discovery Speed
75% reduction in time spent searching for internal knowledge
Workflow Efficiency
2× improvement through enterprise AI assistant automation
Work Duplication Reduction
40% fewer repeated tasks due to better visibility
Decision Accuracy
Improved outcomes using a centralized AI-powered knowledge base
Security & Compliance
Enterprise-grade protection via fully self-hosted AI search platform
Their deep understanding of AI knowledge management, security, and enterprise workflows resulted in faster decision-making and measurable productivity gains.
Challenges
Why It Mattered
Inefficient enterprise search leads to lost productivity and repeated work. Implementing an AI-powered knowledge management layer enabled faster access to trusted information while preserving security, compliance, and organizational context.
Our Approach-
We designed an intelligence-first enterprise AI search and knowledge management system.
Our Tools:
AI / RAG Engine
- LlamaIndex
- LangChain
- OpenAI and local LLMs
Integrations
- Google Workspace
- Slack
- GitHub
- Jira
- Confluence
- Notion
- SharePoint
- Salesforce
- HubSpot
Backend
- Python
- FastAPI
- Node.js
Frontend
- React.js
- Next.js
Databases
- PostgreSQL
- ElasticSearch
- Vector Databases (Pinecone
- Qdrant
- Milvus)
DevOps
- Docker
- Kubernetes
- CI/CD
- Self-hosted Infrastructure
Before & After
The unified enterprise AI search platform dramatically improved speed, accuracy, security, and operational efficiency.
| Feature / Metric | Before – Traditional Systems | After – Enterprise AI Search Platform |
|---|---|---|
| Knowledge Search | Manual and scattered | Instant enterprise AI search |
| Information Accuracy | Context-poor results | Context-aware AI-powered retrieval |
| Duplicate Work | Very common | 40% reduction |
| Productivity | High time wastage | 75% time saved |
| Data Security | External AI risk | Secure self-hosted AI search solution |
| Automation | Minimal | Enterprise AI assistant workflows |
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