Agentic and RAG Platform for Enterprise Vertical Applications
Enterprise control plane for agentic workflow orchestration, retrieval-augmented generation, and governed content processing.
What is Dailogue?
Dailogue is an enterprise Agentic and RAG platform that orchestrates workflows across four runtime providers (LangGraph, OpenAI Agents, Google ADK, Claude Agent SDK) with human-in-the-loop approval, and provides configurable retrieval with 17 strategies. It uses a 5-level tenant hierarchy (platform, application, customer, organization, project) to enforce policy inheritance. LLM operations route through LiteLLM to 5+ providers (OpenAI, Anthropic, Google, Azure, Mistral) covering 100+ models. The platform supports 40+ source types for ingestion, policy-driven processing, and governed content delivery for vertical applications.
40+
Source Types
5
Tenant Levels
5+
LLM Providers
17
Retrieval Strategies
4
Agentic Providers
6
Pipeline Stages
Platform Architecture
Policy-driven architecture with configurable agentic, RAG, LLM, and processing modules tuned for cost, quality, groundedness, and performance.
Orchestrate agentic workflows
Run governed multi-step automation across LangGraph, OpenAI Agents, Google ADK, and Claude Agent SDK with human-in-the-loop approval.
Run advanced RAG strategies
Select from 17 retrieval strategies with hybrid search, reranking, and adaptive strategy selection for grounded responses.
Route LLM operations at scale
Route to 5+ providers and 100+ models via LiteLLM with tenant-scoped policies for cost, quality, and latency.
Ingest from enterprise sources
Connect 40+ source types including files, web, APIs, RSS, and cloud storage with governed extraction.
Process and enrich content
Apply policy-aware normalization, chunking, and enrichment for reliable downstream retrieval.
Govern with enterprise controls
Enforce 5-level tenant isolation, RBAC, observability, and auditable operations by default.
- Agentic Execution
Orchestrate multi-step workflows with four runtime providers and human oversight.
- LangGraph, OpenAI Agents, Google ADK, Claude Agent SDK
- Capability-aware selection with streaming, checkpoints, and structured output
- Human-in-the-loop approval with MCP protocol and A2A interoperability (v1.0 RC)
- Retrieval and RAG
Ground outputs using 17 retrieval strategies with hybrid search and reranking.
- Hybrid RRF, SPLADE, ColBERT, HyDE, RAG fusion, adaptive selection
- Cross-encoder reranking and contextual compression
- Quality assessment with strategy performance tracking
- LLM Orchestration
Route models and prompts through configurable tenant-scoped policies.
- 5+ providers via LiteLLM: OpenAI, Anthropic, Google, Azure, Mistral
- Streaming with time-to-first-token metrics and cost estimation
- Prompt management, safety checks, and model registry
- Ingestion and Processing
Collect, normalize, and validate enterprise data before retrieval.
- 40+ source types with sitemap discovery and multipart upload
- Policy-driven chunking, enrichment, and quality assessment
- Async job execution with heavy-content worker routing
Step 1
Ingest
Collect raw data across sources and tenants.
Step 2
Process
Normalize, enrich, and prepare content for indexing.
Step 3
Chunk + Embed
Generate embeddings using configurable model policies.
Step 4
Index
Store vectors and metadata for low-latency retrieval.
Step 5
Retrieve + Rerank
Assemble grounded context using hybrid search patterns.
Step 6
Agentic Execute
Run governed workflows across LangGraph, OpenAI Agents, Google ADK, or Claude Agent SDK.
Configuration-Driven Extensibility
Choose and swap libraries, model families, and execution strategies through configuration policies.
| Module | Configurable Controls | Business Result |
|---|---|---|
| Agentic | Provider selection (4 runtimes), tool permissions, MCP, approval checkpoints | Governed automation with human oversight and provider-matched capabilities |
| RAG | 17 retrieval strategies, reranking, quality assessment, adaptive selection | Higher retrieval precision and grounded responses per workload |
| LLM | Provider routing via LiteLLM, model selection, prompt policies, fallback | Cost-quality-latency balancing across 5+ providers and 100+ models |
| Processing | Chunking, enrichment, metadata extraction, and policy gates | Reliable input quality for downstream retrieval and generation |
Configurable Ecosystem
Configure model providers, RAG strategy profiles, reranking, and orchestration policies without rewriting application code.
Examples: OpenAI, Claude, Gemini, Mistral, Voyage reranking, LangGraph, OpenAI Agents, Google ADK, Claude Agent SDK.
Decision Axis
Cost
Choose efficient libraries and model routes for predictable unit economics by workload.
Decision Axis
Quality
Prioritize relevance, grounded output quality, and auditability for regulated scenarios.
Decision Axis
Performance
Maintain throughput and latency targets with scalable, resilient orchestration defaults.
Future-Ready by Configuration
- Swap providers, libraries, and models through policy configuration, not code rewrites
- Evolve RAG strategy per application and tenant while preserving shared governance
- Run evaluation and observability loops to continuously improve quality and groundedness
Vertical Application Plan
Shared platform architecture powers each vertical while teams configure workflows and policies per domain.
Automated GenAI Newsletter
Pre-LaunchEnd-to-end editorial pipeline: source ingestion, retrieval-driven curation, AI-assisted drafting, and human review before publication.
- Weekly intelligence digests
- Editorial calendar automation
- Source-to-publication workflows
Product Intelligence
PlannedMarket intelligence and competitive analysis through configurable ingestion, retrieval, and reporting.
- Competitive landscape monitoring
- Product feature analysis
Sustainability
PlannedESG intelligence and reporting through policy-aware retrieval and narrative generation.
- Regulatory landscape tracking
- Sustainability reporting workflows
Regulatory
PlannedRegulatory compliance intelligence through document analysis, clause-level retrieval, and change monitoring.
- Regulatory change summaries
- Compliance document analysis
Product Showcase
Screenshots are based on the current Platform Admin design language and include in-development previews.
Ingestion Operations
Built TodayCurrent ingestion jobs and source controls from Platform Admin.
Retrieval and Curated Content
Hybrid PreviewCurated content flow with retrieval-quality overlays and operational monitoring.
Agent Workbench
Hybrid PreviewConcept UI on live backend capabilities. Workflow orchestration, approvals, and MCP protocol are operational via API. Admin UI in development.
Roadmap
Delivery sequence for platform maturity, vertical acceleration, and broader automation capabilities.
March 2026
Delivered
- Four agentic runtime providers with MCP and A2A interoperability (v1.0 RC)
- 17 retrieval strategies with adaptive selection and quality assessment
- LLM routing via LiteLLM to 5+ providers covering 100+ models
- 40+ source types with sitemap discovery and multipart upload
April 2026 - June 2026
Now
- Newsletter vertical Pre-Launch with end-to-end editorial pipeline
- Durable approval storage for production agentic workflows (planned)
- Vertical accelerator framework for Product Intelligence, Sustainability, Regulatory
July 2026 - September 2026
Next
- A2A agent federation for cross-organization workflow interoperability
- Progressive tool-use assistants for targeted business workflows
- Computer-use enablement where model confidence and controls are sufficient
Built for Enterprise Adoption
Platform controls are designed for private enterprise adoption today, with API docs and interactive API explorer available in controlled internal environments.