Agent Foundations

Query your data naturally with GraphRAG. Auto-generated APIs, intelligent graph navigation agents, and world-class entity resolution deliver connected insights at scale.

GraphRAG Engine

Combines knowledge graphs with retrieval-augmented generation. The graph provides context and relationships, while RAG ensures responses are grounded in your actual data, not hallucinated.

Auto-Generated APIs

Production-ready REST and GraphQL APIs are automatically created from your data model, complete with authentication, versioning, and documentation. Zero manual development.

Graph Navigation Agents

Intelligent agents traverse the knowledge graph to answer complex queries, continuously refining their understanding and aggregating context from entity resolution.

What is GraphRAG?

Traditional RAG (Retrieval-Augmented Generation) searches text documents. GraphRAG uses your knowledge graph to understand relationships, enabling dramatically more accurate and contextual responses.

Traditional RAG

Searches text chunks, misses connections between entities

GraphRAG

Traverses relationships to understand context and connections

Key Advantages

Multi-hop reasoning: Follows chains of relationships to answer complex questions

Aggregated context: Entity resolution provides complete context about each node

Structured knowledge: Graph structure ensures accurate, not hallucinated, responses

"Which suppliers for our beverage products in Europe have had quality issues in the last 6 months?"

GraphRAG Process:

Identify entities: "beverage products", "Europe", "suppliers", "quality issues"
Navigate graph: Products → (category: beverage) → (region: Europe) → Suppliers
Filter by time: Suppliers with quality issue events in last 6 months
Aggregate context: Pull in supplier details from golden profiles
Generate response: Natural language answer with supporting data

Example Query

Turn data into conversations

The best interface is no interface. GraphRAG makes data exploration feel like asking a colleague, not writing SQL queries or waiting for engineers to build APIs.

Graph Navigation Agents

Intelligent agents don't just retrieve data - they understand your knowledge graph structure, navigate complex relationships, and continuously refine their understanding of your data landscape.

Complex graph querying

Agents can traverse multi-hop relationships to answer questions that would require dozens of manual queries

Find all products affected by a supplier quality issue

Identify customers impacted by a supply chain disruption

Map complete data lineage from source to report

Memory & refinement

Agents remember previous queries and continuously update their understanding of the graph structure and common access patterns

Learn which relationships are most frequently queried

Optimize traversal paths based on usage patterns

Suggest new graph connections based on query needs

Context aggregation

World-class entity resolution ensures agents have complete, accurate context about every node they encounter

Golden profiles provide authoritative entity data

Relationship confidence scores guide traversal decisions

Conflicting information is flagged with resolution status

Agent Loop

Query received

Natural language question arrives

Graph traversal

Agent navigates relationships to find relevant data

Context aggregation

Entity resolution provides complete context for each node

Response generation

Connected insights synthesized into natural language

Memory update

Agent refines understanding of graph structure and patterns

Each query enhances the agent's understanding, improving recall and precision over time.

Connected Insights

Because entity resolution provides aggregated context, agents see the complete picture at every node:

All attributes from golden profiles
Confidence scores for each relationship
Full lineage back to source systems
Temporal context (when data was last updated)

Real-World Applications

Financial Services: Risk Analysis

A global investment bank needs to assess exposure to a specific counterparty across all business lines.

The Challenge

Data spread across 15+ systems (trading, credit, ops)
Counterparty appears under different legal entities
Complex ownership hierarchies to navigate
Need answers in minutes, not days

Alchemia's Solution

Natural language query

"What is our total exposure to Acme Corp and all subsidiaries?"

Entity resolution

Golden profiles identify all legal entities linked to Acme Corp

Graph traversal

Agent navigates hierarchy relationships to find all subsidiaries

Cross-system aggregation

Pulls positions from trading, credit lines from risk, settlements from ops

Connected insight

Produces a complete exposure report in 30 seconds, with full lineage and confidence scores

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