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Platform Components

The technology stack powering Intelligent Knowledge Assistant

This solution is built on proven, production-ready platform components. This page provides links to detailed technical documentation for each component.

Core Components​

1. Document Search & Summarization​

What it does: Hybrid retrieval combining BM25 keyword search with semantic vector search, plus cross-encoder reranking.

Key capabilities:

  • Multi-format document ingestion (PDF, Word, HTML, Markdown)
  • Chunking strategies optimized for Q&A
  • Hybrid search with Reciprocal Rank Fusion (RRF)
  • Cross-encoder reranking for precision
  • Source attribution and citations

Technical Documentation: View Platform Details →

2. Chatbot & AI Agent Creation​

What it does: Conversational interface with multi-turn dialogue, context tracking, and intelligent routing.

Key capabilities:

  • Natural language understanding
  • Multi-turn conversation memory
  • Intent recognition
  • Context-aware responses
  • WebSocket streaming for real-time interaction

Technical Documentation: View Platform Details →

3. AI Security & Guardrails​

What it does: Enterprise-grade security ensuring safe, compliant AI interactions.

Key capabilities:

  • PII detection and masking (50+ entity types)
  • Content policy enforcement
  • Prompt injection prevention
  • Output validation
  • Audit trails for compliance

Technical Documentation: View Platform Details →

4. LLM & RAG Architecture​

What it does: Multi-provider LLM support with cost optimization and quality monitoring.

Key capabilities:

  • Multi-LLM support (OpenAI, Anthropic, Google)
  • Semantic caching (60-80% cost reduction)
  • Prompt compression
  • Quality evaluation (RAGAS metrics)
  • Fallback and retry logic

Technical Documentation: View Platform Details →

Supporting Components​

Query Understanding​

Expands queries with domain-specific terminology, synonyms, and related concepts.

Technical Documentation: View Details →

Vector Stores​

Supports multiple vector database options (OpenSearch, MongoDB Atlas, Azure AI Search, Vertex AI).

Technical Documentation: View Details →

Caching & Optimization​

Multi-layer caching (response, semantic, embedding) reduces costs and latency.

Technical Documentation: View Details →

Response & Observability​

Real-time monitoring, tracing, and analytics for production systems.

Technical Documentation: View Details →

Architecture Diagram​

┌─────────────────────────────────────────┐
│         User Interface                   │
│    (Chat, API, Web, Slack, Teams)       │
└───────────────┬─────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────┐
│    Conversational Agent Layer            │
│  (Intent, Context, Routing, Memory)     │
└───────────────┬─────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────┐
│         RAG Pipeline                     │
│  Query Expansion → Hybrid Search →      │
│  Reranking → Context Assembly            │
└───────────────┬─────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────┐
│      Document Storage & Indexing        │
│  (Vector DB, Keyword Index, Metadata)   │
└─────────────────────────────────────────┘
                │
                ▼
┌─────────────────────────────────────────┐
│    Security & Observability             │
│  (Guardrails, PII, Monitoring, Audit)   │
└─────────────────────────────────────────┘

Integration Points​

What We Integrate With​

Document Sources:

  • SharePoint, Confluence, Google Drive
  • File servers (SMB, NFS)
  • Databases (SQL, NoSQL)
  • Web scraping
  • Custom APIs

User Interfaces:

  • Web application (included)
  • Slack, Microsoft Teams
  • REST API
  • Embed widget
  • Mobile apps

Authentication:

  • SSO (SAML, OAuth, OIDC)
  • Active Directory / LDAP
  • API keys
  • Role-based access control (RBAC)

Monitoring:

  • Prometheus + Grafana
  • LangSmith tracing
  • Custom dashboards
  • Alert integration

Performance Characteristics​

MetricSpecification
Response Time<2 seconds (95th percentile)
Accuracy75-85% (measured by RAGAS)
Throughput100-500 queries/second
Availability99.9% uptime SLA
Document Indexing1,000 docs/hour
Concurrent Users100-10,000+ (scalable)

Deployment Options​

Cloud Deployment​

  • AWS, Azure, or Google Cloud
  • Managed infrastructure
  • Auto-scaling
  • 99.9% availability
  • Faster setup (4-6 weeks)

On-Premise Deployment​

  • Your data center
  • Full data control
  • Compliance-friendly
  • Kubernetes or Docker
  • Longer setup (6-8 weeks)

Hybrid Deployment​

  • Sensitive data on-premise
  • Public data in cloud
  • Best of both worlds
  • Setup: 6-10 weeks

Technical Specifications​

Supported Formats:

  • Documents: PDF, Word, Excel, PowerPoint
  • Web: HTML, Markdown, XML
  • Code: Python, Java, JavaScript, SQL
  • Data: CSV, JSON, YAML
  • Images: OCR for scanned documents

Language Support:

  • English (native)
  • 100+ languages (multilingual models)
  • Custom language packs available

Scaling:

  • Horizontal scaling (add more servers)
  • Vertical scaling (more powerful servers)
  • Load balancing included
  • Auto-scaling based on demand

Want to Learn More?​

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