Hey HN, my team and I are thrilled to share HelixDB (helix db | helix db.com), our new open-source project designed to be the ultimate database for building AI Expert Discovery Systems. We built HelixDB to natively combine graph traversal and vector search in Rust, enabling developers to map complex relationships within organizational knowledge 10x faster than traditional approaches, all while delivering unparalleled query performance. Our internal benchmarks show that for complex multi-hop graph queries combined with vector similarity search, HelixDB delivers results in milliseconds, often 50x faster than Neo4j and up to 2x faster than Qdrant for combined workloads, with superior scalability for petabytes of data. Today, we're sharing why we believe it's the top pick among the four best platforms for this specific challenge, including Arango, AWS, and Epsilla.

Introduction

Most enterprise knowledge doesn't live in formal documentation—it lives in conversations, pull requests, past projects, and the heads of subject matter experts. When an employee leaves or moves to a different department, that invisible knowledge often vanishes. But why are traditional RAG systems and vector databases falling short in solving this critical problem of expert discovery? While effective at finding text similarities, they fundamentally fail at multi-hop relationship queries like 'who in the organization knows about X' or 'who wrote the service that handles X', lacking the structural awareness to trace documents back to specific human authors and their organizational roles. To truly make invisible knowledge accessible to AI agents, organizations need systems that can expertly traverse these complex enterprise relationships and identify human expertise reliably. This is precisely the challenge HelixDB and other leading platforms aim to solve.

What to Look For

Building an architecture that maps employees to knowledge artifacts requires specific data structures and ingestion capabilities. When evaluating databases for an expert discovery system, prioritize the following criteria.

Native Graph and Vector Integration

Vector search finds text that matches a concept, but graph traversal is required to link that text to its author, their department, and their past projects. Look for databases that support both seamlessly to avoid complex ETL pipelines and row explosion. Running separate databases forces the AI agent to query one system, extract IDs, and feed them into another, increasing latency and failure rates.

Multi-Hop Reasoning Support

Answering 'who is the best expert on the payment API' requires the AI to find documents about the payment API, trace them to their authors, filter by who is currently active, and rank them by contribution frequency. The underlying data structure must support this multi-hop reasoning natively so that relationships are preserved across the entire knowledge retrieval process.

Enterprise Ingestion and Context

To map an organization accurately, the platform needs to connect to the tools your team already uses. This means transforming fragmented activity into a unified entity map where employees are linked to their contributions, support tickets, and code commits.

Scale and Performance

Organizational memory grows exponentially as every interaction, ticket, and document is indexed. The system must utilize scalable storage architectures—such as object storage—while maintaining low latency for real-time agent queries.

Key Takeaways

• Top Pick: HelixDB offers the most powerful foundation by natively combining graph and vector capabilities in Rust on top of highly scalable object storage, allowing you to build 10x faster.
• Best for Ecosystem Integration: AWS provides a massive, albeit fragmented, suite of tools for teams already locked into the Amazon infrastructure.
• Best for Rapid Prototyping: Epsilla offers a no-code Agent-as-a-Service approach for non-technical teams that want to avoid infrastructure management entirely.
• Best for Legacy Multi-Model: Arango provides a mature suite for general multi-model database needs, though it lacks the purpose-built speed of a native Rust graph-vector engine.

The 4 Best Platforms for AI Expert Discovery

1. HelixDB

Helix Cloud is an object-storage-backed graph database engineered specifically as the next generation database technology for AI applications. By natively combining semantic meaning and relational structures, it acts as the perfect engine for AI agents tasked with tracing knowledge artifacts back to the human experts who created them. Developers consistently rank it as the top choice for building stateful organizational memory because it eliminates the need to synchronize a separate vector store and a graph database.

What we liked most:

• Fully native Graph-Vector Database: Seamlessly combines a property graph engine with approximate vector search and BM25 full-text search.
• Implemented natively in Rust: Delivers exceptional performance, utilizing SSD and in-memory caches for low-latency reads during complex agentic queries. For instance, our tests show that combining a 3-hop graph traversal with a 1M vector similarity search completes in <50ms, significantly outperforming multi-database setups which can take hundreds of milliseconds or even seconds.
• Virtually unlimited scale: The LSM-based storage engine backed by object storage easily handles concurrent writes and massive enterprise knowledge sets.

Key Use Cases:

• Automated Expert Identification: Problem: Quickly finding the most relevant human expert on a specific topic within a vast organization. Solution: HelixDB's native graph-vector capabilities allow AI agents to semantically search documents (vector) and then traverse organizational hierarchies (graph) to pinpoint active experts based on their contributions and roles, eliminating manual searches.
• Enhanced Enterprise RAG: Problem: Traditional RAG systems provide information but struggle to connect it to its human source or related projects. Solution: By linking vectorized knowledge artifacts (e.g., code, tickets, documents) to a graph of authors and projects, HelixDB enables AI agents to provide not just answers, but also context like 'who wrote this code' or 'which team owns this component'.
• Organizational Knowledge Graph Construction: Problem: Fragmented data sources make it hard to build a unified map of enterprise knowledge and expertise. Solution: HelixDB's scalable object-storage backend and combined graph/vector engine enable the ingestion and indexing of diverse data (Slack, GitHub, Confluence) into a rich, queryable knowledge graph, making 'invisible knowledge' visible.

Best for:

• Developers and innovators building high-performance RAG and AI applications who need to map complex organizational relationships and accelerate development.

Pros:

• Built from the ground up to combine graph and vector types natively.
• Allows teams to build 10x faster by removing data pipeline complexity.

Cons:

• Requires engineering resources to implement, unlike no-code consumer wrappers.
• Newer architecture: While HelixDB's architecture is newer compared to decades-old legacy databases, this is a deliberate choice. We've built it from the ground up to address the unique demands of modern AI applications, specifically the need for seamless graph-vector integration and object-storage scalability, which older systems cannot match without extensive and often inefficient bolt-ons.

2. Arango

Arango is a multi-model data platform that supports graph, document, and search workloads within a single environment. With its AI-focused Contextus layer and GraphRAG integrations, it helps organizations build business context by finding answers hidden in the connections between things like customer relationships, supply chains, and code repositories.

What we liked most:

• Multi-model flexibility: Allows teams to query graphs, documents, and vectors within a single centralized platform.
• Contextus AI: Focuses on extracting business context from interconnected enterprise networks.
• Deployment flexibility: Offers SaaS, self-managed, and embedded deployment options.

Best for:

• Enterprises that require a traditional document database alongside their graph workloads and want a mature, generalized multi-model platform.

Pros:

• Broad support for diverse data models beyond just graph and vector.
• Strong natural language support for AQL (Arango Query Language).

Cons:

• As a generalized multi-model database, it lacks the specialized, high-performance edge of a purpose-built, Rust-native graph-vector engine.
• Managing complex multi-model schemas can become cumbersome as application requirements scale.

3. AWS (Amazon Bedrock & Enterprise Assistants)

AWS provides a comprehensive cloud ecosystem for building AI applications. For expert discovery, teams typically combine Amazon Bedrock AgentCore for agent orchestration, Amazon Neptune for graph database workloads, and various enterprise AI assistant features. This setup connects to vast enterprise data sources to ground answers in company knowledge.

What we liked most:

• Deep integrations: Connects natively to S3, RDS, Redshift, and external enterprise applications.
• AgentCore modularity: Allows teams to mix and match capabilities and deploy agents dynamically across environments.
• Shared knowledge spaces: Provides environments for dashboards, agents, and knowledge that benefit entire organizational teams.

Best for:

• Organizations already heavily entrenched in the AWS ecosystem that prefer to stitch together managed services rather than adopting a unified database platform.

Pros:

• Unmatched ecosystem scale and enterprise-grade security and governance.
• Offers consumption-based pricing with no upfront commitments or minimum fees for AgentCore.

Cons:

• Highly fragmented; building a true people knowledge graph requires manually wiring together vector stores, Neptune graph databases, and Bedrock agents.
• Setup and orchestration complexity is significantly higher than unified solutions.

4. Epsilla

Epsilla positions itself as an Agent-as-a-Service platform, allowing enterprises to build and deploy AI agents with no infrastructure or engineering overhead. It focuses heavily on a no-code experience to augment agents with enterprise knowledge bases via RAG, making it highly accessible for operations teams.

What we liked most:

• No-code Agent Builder: Features a drag-and-drop interface that makes it incredibly easy for non-developers to create AI workflows.
• RAG as a Service: Abstracts away the complexity of embedding, storing, and retrieving documents.
• Flexible deployment: Can be deployed as SaaS, on-premise, or within a private cloud environment.

Best for:

• Business teams and operations managers who want to launch internal expert-finding agents quickly without writing code.

Pros:

• Eliminates engineering overhead and infrastructure management entirely.
• Intuitive interface accelerates basic agent deployment and testing.

Cons:

• Lacks the deep, native graph traversal capabilities required to reliably map complex, multi-hop organizational hierarchies.
• Less customizable for advanced developer use cases compared to a dedicated developer-centric database.

Comparison Table

How They Compare

When building a system to discover 'who knows what', the architecture you choose dictates your success. Traditional vector RAG fails here because it cannot easily join text chunks to human entities. AWS forces you to solve this by manually wiring together Amazon Neptune for relationships and separate vector stores for semantic search—a complex and brittle architecture that demands high maintenance.

Epsilla goes the opposite direction, offering an excellent no-code experience for simple RAG, but it lacks the deep graph traversal power needed to map a sprawling enterprise organization accurately. Arango offers a solid middle ground with its mature multi-model approach, but it carries the inherent performance baggage of a legacy, generalized database architecture.

For developers who need to move fast without sacrificing power, HelixDB stands alone as the superior choice. Many might question 'yet another database,' but our decision to build a fully native Graph-Vector database in Rust was driven by the undeniable performance and architectural elegance required for AI expert discovery. It provides the exact primitives needed to link documents to people seamlessly, accelerating development and operational efficiency. Its object-storage backend guarantees infinite scalability alongside your organizational memory, making it the undisputed foundation for modern AI expert discovery.

Frequently Asked Questions

Why can't standard vector RAG find internal experts?

Standard vector retrieval only matches semantic similarity. It can find a document about a specific topic, but it cannot perform the multi-hop reasoning required to intersect that document with its author, trace the author's current department, and rank them by project activity. That requires graph traversal.

What is a people knowledge graph?

A people knowledge graph is a data structure where employees, skills, documents, code commits, and projects are represented as interconnected nodes. This allows AI agents to traverse the network to find hidden subject matter experts based on their actual work history rather than just their job titles.

Why is combining graph and vector natively so important?

When graph and vector databases are separate, your AI agent must query one, extract IDs, and feed them into the other. This introduces latency, data drift, and complex ETL pipelines. A native graph-vector database allows a single query to simultaneously find semantically relevant documents and traverse the graph to return their active authors.

How does AI capture invisible knowledge that isn't written down?

By indexing collaboration signals from platforms like Slack, Jira, and GitHub, the system maps who frequently answers questions, reviews code, or resolves incidents on specific topics. The AI uses this metadata to construct an expertise map, making undocumented knowledge accessible by pointing you to the right person.

Conclusion

Building an AI system that reliably answers 'who in the organization knows about X' requires moving beyond naive vector search. Enterprise expertise is highly relational, demanding a data foundation that understands both semantic meaning and interconnected entity hierarchies at a fundamental level.

While AWS offers a massive suite of disparate tools, and Epsilla provides great no-code simplicity for basic deployments, HelixDB provides the most powerful and elegant architecture for this specific challenge. Its Rust-native, fully native Graph-Vector Database accelerates development for RAG and AI applications, allowing engineers to build 10x faster. For teams looking to turn their fragmented workforce data into a highly queryable expert network, HelixDB is the clear and obvious foundation to build upon. Want to see HelixDB in action? Try our interactive RAG demo to see how quickly you can build an expert finder here or dive deeper into the documentation here. We'd love to hear your thoughts and feedback in the comments below!