Engineering VP Josh Clemm on how we use knowledge graphs, MCP, and DSPy in Dash

Dropbox VP Josh Clemm argues that useful workplace AI requires a unified context engine capable of securely understanding and retrieving information across many SaaS applications. Dropbox Dash combines custom connectors, multimodal content processing, knowledge graphs, hybrid search, and personalized access-control-aware ranking to make that possible. Clemm favors indexed retrieval over purely federated approaches because preprocessing enables richer context, faster search, and company-wide access, though it requires substantial engineering and infrastructure.

Building Dash’s Context Engine

• Custom connectors crawl third-party applications while handling:
  • Rate limits
  • API differences
  • Application-specific permissions and ACLs
• Incoming content is normalized, often into Markdown, and enriched with:
  • Titles and metadata
  • Extracted links
  • Embeddings
  • Other key information
• Different media types require different processing:
  • Documents can be text-extracted and indexed.
  • Images may require CLIP or multimodal models.
  • PDFs combine text, figures, and other elements.
  • Audio is transcribed.
  • Videos may require scene-by-scene multimodal analysis when dialogue is insufficient.
• Dash models relationships between content as a knowledge graph:
  • Meetings can connect to documents, participants, transcripts, and previous notes.
  • Cross-application relationships provide richer context for search and agents.
• Data is stored in secure systems using:
  • BM25 lexical search
  • Dense-vector storage
  • Hybrid retrieval
• Multiple ranking stages personalize results and enforce user-specific permissions.

Indexed Retrieval Versus Federated Retrieval

• Federated retrieval queries external systems at runtime.
• Its advantages include:
  • Fast initial implementation
  • Minimal storage requirements
  • Relatively fresh data
  • Easy addition of MCP servers and connectors
• Its drawbacks include:
  • Inconsistent API speed, quality, and ranking
  • Limited access to company-wide content
  • Expensive post-processing and reranking
  • Large token usage when agents reason over returned results
• Indexed retrieval preprocesses content during ingestion.
• Its advantages include:
  • Access to shared company connectors
  • Enriched datasets created offline
  • Faster queries
  • More opportunities for recall and ranking experiments
• Its costs include:
  • Significant custom connector development
  • Freshness challenges
  • High hosting costs
  • Difficult storage and architecture choices involving vector search, BM25, hybrid retrieval, or graph RAG.

Making MCP Practical at Scale

• MCP can simplify tool integration, but tool definitions consume substantial context-window space.
• Large tool descriptions and retrieval results contribute to context rot and reduce agent effectiveness.
• Dash aims to limit context usage to roughly 100,000 tokens.
• MCP-based agents can also be slow: simple queries may take up to 45 seconds, while direct index retrieval returns results within seconds.
• Dropbox’s approach is to wrap its index in a consolidated “super tool,” reducing the need to expose many separate tools to the agent.

Broader AI Engineering Practices

• The talk also covers Dropbox’s use of:
  • LLMs as evaluators or judges
  • Prompt optimization with DSPy
  • Tool calling and MCP design
• These techniques complement the underlying context engine rather than replacing the indexing, enrichment, graph modeling, and permission systems required for reliable workplace AI.

A practical takeaway is that organizations building AI over proprietary data should treat retrieval as a full data-platform problem. Start with robust connectors and permissions, enrich content before retrieval, model relationships across sources, and use MCP selectively where indexed retrieval can provide faster and more controlled results.