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Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

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HydraDB for

Financial Services AI

Engineering teams building AI agents for banking, insurance, and capital markets face a fundamental challenge: financial decisions require traceable memory that spans transactions, evolving regulations, and complex entity relationships. Standard vector databases fail because they treat every query as isolated, cannot track how compliance rules change over time, and lack the audit trails regulators demand. HydraDB provides the persistent memory infrastructure that financial AI agents need to maintain context across sessions, track temporal state changes, and deliver deterministic outputs.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why Financial Services AI keeps failing

Building reliable AI agents for regulated environments requires more than semantic search. When your compliance copilot needs to track evolving KYC states or your risk agent must traverse complex transaction networks, standard RAG architectures break down.

No memory across client sessions

Vector databases are stateless. When a relationship manager queries a client's risk profile, the system cannot recall previous assessments, portfolio changes, or compliance decisions from earlier sessions. Engineers end up building custom session management on top of their RAG stack.

No memory across client sessions

No memory across client sessions

Missing audit trails for decisions

Regulators require proof of how decisions were made. Standard vector search returns chunks based on mathematical similarity without preserving the logical chain of evidence. Your compliance team cannot trace which specific regulation or client document drove a recommendation.

Missing audit trails for decisions

Flattened temporal states

Financial data evolves constantly. A regulation valid in Q1 may be obsolete by Q3. Standard databases destructively overwrite old states, making it impossible to answer temporal questions like "Was this trade compliant under the rules that existed when it was executed?"

Flattened temporal states

Lost entity relationships

Fraud detection requires understanding networks of accounts, transactions, and counterparties. Flat embeddings cannot model these relationships natively, forcing teams to maintain separate graph databases alongside their vector store.

Lost entity relationships

// See It In Action //

An agent that actually remembers

HydraDB gives your financial AI agents structured memory that persists across sessions, preserves temporal states, and maintains relationship context.

Live support session — HydraDB context active

Retrieval : 94ms

Is Marcus Chen cleared for margin trading, and why was his account restricted in Q2?

Compliance officer

Account compliance review

Q2 restriction history

HydraDB recalled:

Current state: Marcus Chen cleared for margin trading as of Aug 16, 2024

Historical state: Account restricted May 10, 2024 due to missing AML documentation

Resolution: AML docs received Aug 15, 2024; verified by Compliance Officer Sarah Lin

Entity relationship: Marcus Chen → Account Manager → Jennifer Wu (Global Wealth Division)

Marcus Chen is currently cleared for margin trading. His account was restricted on May 10, 2024 due to missing AML documentation required under current KYC regulations. The restriction was lifted on August 16, 2024 after the required documentation was received on August 15 and verified by Compliance Officer Sarah Lin. His account manager Jennifer Wu in Global Wealth has been notified of the status change.

// How Hydra Helps //

The Memory Layer for Banking AI

HydraDB replaces fragmented DIY stacks with unified memory infrastructure designed for the strict requirements of financial services.

Persistent memory

HydraDB maintains complete client histories, transaction logs, and compliance decisions across sessions. When your risk agent evaluates a portfolio, it recalls previous assessments, market conditions at decision time, and the full relationship history.

Persistent memory

Persistent memory

Relationship-aware retrieval

Financial analysis requires understanding entity connections. HydraDB's graph-native architecture links accounts to owners, transactions to counterparties, and trades to regulatory frameworks. Your fraud detection agents can traverse these relationships to identify complex patterns.

Relationship-aware retrieval

Temporal context

HydraDB preserves append-only histories of changing states. When KYC requirements update or risk thresholds change, the system maintains both current and historical versions. Your compliance copilots can accurately determine what was valid at any point in time.

Temporal context

Structured data ingestion

HydraDB ingests from your existing financial infrastructure. Extract records from data warehouses, stream transactions from message queues, or pull documents from secure storage. The system enriches and structures this data into queryable memory.

Structured data ingestion

// Get Started //

Three steps to production-ready memory

HydraDB integrates with your existing infrastructure and works with any LLM. Replace brittle custom memory logic with structured context retrieval.

Connect your financial data sources

Extract the records your agents need from your secure infrastructure.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Connect to your data warehouse or document store
kyc_record = data_warehouse.get_kyc_profile(client_id="chen_8842")
transaction_log = transaction_db.get_recent_trades(account="chen_8842")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Connect to your data warehouse or document store
kyc_record = data_warehouse.get_kyc_profile(client_id="chen_8842")
transaction_log = transaction_db.get_recent_trades(account="chen_8842")

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Connect to your data warehouse or document store
kyc_record = data_warehouse.get_kyc_profile(client_id="chen_8842")
transaction_log = transaction_db.get_recent_trades(account="chen_8842")

Connect your financial data sources

Extract the records your agents need from your secure infrastructure.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Connect to your data warehouse or document store
kyc_record = data_warehouse.get_kyc_profile(client_id="chen_8842")
transaction_log = transaction_db.get_recent_trades(account="chen_8842")

Connect your financial data sources

Extract the records your agents need from your secure infrastructure.

import os
from hydra_db import HydraDB

client = HydraDB(token=os.environ.get("HYDRA_KEY"))

# Connect to your data warehouse or document store
kyc_record = data_warehouse.get_kyc_profile(client_id="chen_8842")
transaction_log = transaction_db.get_recent_trades(account="chen_8842")

Ingest and structure compliance context

Map your financial records into HydraDB's memory format. The system automatically resolves entities and tracks temporal changes.

import json

# Ingest KYC profiles and transaction history
client.context.ingest(
    type="knowledge",
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    app_knowledge=json.dumps([
        {
            "id": "kyc_chen_8842_v3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Marcus Chen KYC Update",
            "type": "kyc_profile",
            "content": {"text": "AML documentation verified. Margin trading approved."},
            "metadata": {"status": "approved", "verified_by": "Sarah Lin", "date": "2024-08-16"}
        },
        {
            "id": "trade_log_8842_q3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Q3 Trading Activity",
            "type": "transaction_log",
            "content": {"text": f"Total volume: {transaction_log.volume}, Risk score: {transaction_log.risk_score}"},
            "metadata": {"account": "chen_8842", "period": "2024_Q3"}
        }
    ])
)

import json

# Ingest KYC profiles and transaction history
client.context.ingest(
    type="knowledge",
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    app_knowledge=json.dumps([
        {
            "id": "kyc_chen_8842_v3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Marcus Chen KYC Update",
            "type": "kyc_profile",
            "content": {"text": "AML documentation verified. Margin trading approved."},
            "metadata": {"status": "approved", "verified_by": "Sarah Lin", "date": "2024-08-16"}
        },
        {
            "id": "trade_log_8842_q3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Q3 Trading Activity",
            "type": "transaction_log",
            "content": {"text": f"Total volume: {transaction_log.volume}, Risk score: {transaction_log.risk_score}"},
            "metadata": {"account": "chen_8842", "period": "2024_Q3"}
        }
    ])
)

import json

# Ingest KYC profiles and transaction history
client.context.ingest(
    type="knowledge",
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    app_knowledge=json.dumps([
        {
            "id": "kyc_chen_8842_v3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Marcus Chen KYC Update",
            "type": "kyc_profile",
            "content": {"text": "AML documentation verified. Margin trading approved."},
            "metadata": {"status": "approved", "verified_by": "Sarah Lin", "date": "2024-08-16"}
        },
        {
            "id": "trade_log_8842_q3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Q3 Trading Activity",
            "type": "transaction_log",
            "content": {"text": f"Total volume: {transaction_log.volume}, Risk score: {transaction_log.risk_score}"},
            "metadata": {"account": "chen_8842", "period": "2024_Q3"}
        }
    ])
)

Ingest and structure compliance context

Map your financial records into HydraDB's memory format. The system automatically resolves entities and tracks temporal changes.

import json

# Ingest KYC profiles and transaction history
client.context.ingest(
    type="knowledge",
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    app_knowledge=json.dumps([
        {
            "id": "kyc_chen_8842_v3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Marcus Chen KYC Update",
            "type": "kyc_profile",
            "content": {"text": "AML documentation verified. Margin trading approved."},
            "metadata": {"status": "approved", "verified_by": "Sarah Lin", "date": "2024-08-16"}
        },
        {
            "id": "trade_log_8842_q3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Q3 Trading Activity",
            "type": "transaction_log",
            "content": {"text": f"Total volume: {transaction_log.volume}, Risk score: {transaction_log.risk_score}"},
            "metadata": {"account": "chen_8842", "period": "2024_Q3"}
        }
    ])
)

Ingest and structure compliance context

Map your financial records into HydraDB's memory format. The system automatically resolves entities and tracks temporal changes.

import json

# Ingest KYC profiles and transaction history
client.context.ingest(
    type="knowledge",
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    app_knowledge=json.dumps([
        {
            "id": "kyc_chen_8842_v3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Marcus Chen KYC Update",
            "type": "kyc_profile",
            "content": {"text": "AML documentation verified. Margin trading approved."},
            "metadata": {"status": "approved", "verified_by": "Sarah Lin", "date": "2024-08-16"}
        },
        {
            "id": "trade_log_8842_q3",
            "tenant_id": "global_bank",
            "sub_tenant_id": "compliance_dept",
            "title": "Q3 Trading Activity",
            "type": "transaction_log",
            "content": {"text": f"Total volume: {transaction_log.volume}, Risk score: {transaction_log.risk_score}"},
            "metadata": {"account": "chen_8842", "period": "2024_Q3"}
        }
    ])
)

Retrieve full context at inference time

Query HydraDB before generation to retrieve precise historical state and compliance context.

# Retrieve compliance history and current status
result = client.query(
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    query="Marcus Chen margin trading status and restriction history",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_compliance_prompt(result.data),
    user_query="Provide compliance summary for this account"
)

# Retrieve compliance history and current status
result = client.query(
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    query="Marcus Chen margin trading status and restriction history",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_compliance_prompt(result.data),
    user_query="Provide compliance summary for this account"
)

# Retrieve compliance history and current status
result = client.query(
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    query="Marcus Chen margin trading status and restriction history",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_compliance_prompt(result.data),
    user_query="Provide compliance summary for this account"
)

Retrieve full context at inference time

Query HydraDB before generation to retrieve precise historical state and compliance context.

# Retrieve compliance history and current status
result = client.query(
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    query="Marcus Chen margin trading status and restriction history",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_compliance_prompt(result.data),
    user_query="Provide compliance summary for this account"
)

Retrieve full context at inference time

Query HydraDB before generation to retrieve precise historical state and compliance context.

# Retrieve compliance history and current status
result = client.query(
    tenant_id="global_bank",
    sub_tenant_id="compliance_dept",
    query="Marcus Chen margin trading status and restriction history",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass structured context to your LLM
response = llm.generate(
    system_prompt=build_compliance_prompt(result.data),
    user_query="Provide compliance summary for this account"
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for Financial Services

HydraDB integrates with your existing infrastructure and works with any LLM. Replace brittle custom memory logic with structured context retrieval.

Features

HydraDB

Standard Vector Search

Cross-session memory

Maintains complete client and portfolio history

Stateless, treats each query as isolated

Relationship-aware retrieval

Graph-native traversal of entity networks

Flat similarity matching only

Temporal context

Versioned states with point-in-time accuracy

Destructive updates lose historical context

Audit trail

Full decision lineage with source attribution

No traceable reasoning path

Best fit

Compliance copilots, risk analysis, fraud detection

Simple document search

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Audit trail

Best fit

HydraDB

Maintains complete client and portfolio history

Graph-native traversal of entity networks

Versioned states with point-in-time accuracy

Full decision lineage with source attribution

Compliance copilots, risk analysis, fraud detection

Standard Vector Search

Stateless, treats each query as isolated

Flat similarity matching only

Destructive updates lose historical context

No traceable reasoning path

Simple document search

// What Teams Are Saying //

Trusted by teams building Financial Services AI

Standard RAG couldn't provide the audit trails our regulators require. HydraDB gives us traceable decision lineage — every recommendation links back to specific regulations and client documents. We deployed our compliance copilots to production in half the time.

Michael Torres

VP of AI Engineering, Global Investment Bank

Fraud patterns span multiple accounts and time periods. Vector databases missed these connections. HydraDB's graph-native memory lets our risk agents traverse complex entity relationships and detect sophisticated schemes that would have gone unnoticed.

Priya Patel

CTO, Enterprise Risk Platform

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

HydraDB is an object-store-native distributed graph database built in Rust, designed to serve as the context layer for AI systems. Unlike traditional graph databases, where storage is tied more closely to database servers or dedicated cluster volumes, HydraDB makes object storage itself the source of truth. This lets query nodes and indexers scale, restart, or be replaced independently without moving or replicating the graph. It also supports snapshot-consistent OpenCypher, GraphBLAS-accelerated traversal, Neo4j-compatible Bolt, and HTTP APIs.

Why use a graph database like HydraDB for AI agents?

A graph database like HydraDB gives AI agents something vector search alone cannot: relationships and state. Instead of retrieving isolated chunks that merely look similar to a query, an agent can follow explicit connections between people, projects, events, policies, documents, and past actions to understand how the current situation fits together.

When should I use HydraDB instead of a vector database?

Use a vector database when your main problem is semantic search over mostly static content: finding documents, chunks, tickets, or products that are similar to a query. Use HydraDB when your AI agent needs to understand how information connects and changes over time. If the agent needs to answer questions like who owns this project, what is blocking it, which policy applies to this customer, or what changed since the last session, similarity search alone is not enough. HydraDB lets the agent traverse entities and relationships and work with structured state instead of only retrieving nearby embeddings.

Does HydraDB work with GraphRAG?

Yes. HydraDB can be used as the graph database behind a GraphRAG system. GraphRAG is a retrieval approach rather than a specific database: it builds or uses a knowledge graph, then retrieves context by traversing relationships instead of relying only on vector similarity. Microsoft’s GraphRAG architecture is explicitly designed around a storage-agnostic knowledge model and allows custom storage and workflow implementations.

How does HydraDB improve retrieval for AI agents?

HydraDB improves retrieval by combining vector search with graph traversal, exact-match search, and temporal context. Instead of returning isolated similar chunks, it reconstructs the relationships, dependencies, and latest valid state around a query, which gives AI agents a smaller, more relevant set of evidence to reason over.

Can I use HydraDB with my existing AI stack?

Yes. HydraDB plugs into your existing AI stack through Neo4j-compatible Bolt and HTTP APIs. Keep your current models, agents, and retrieval pipeline, and add HydraDB as the graph layer for connected, stateful context.

How does HydraDB use context graphs for AI agents?

HydraDB turns fragmented agent memory into a connected context graph. This lets agents retrieve not only the relevant fact, but also how that fact relates to other entities, where it came from, and what the current state is, which gives multiple agents and workflows a consistent view of the same domain.

What AI use cases is HydraDB best suited for?

HydraDB is best for AI agents that need persistent, connected context such as support agents, coding agents, research agents, GraphRAG systems, and enterprise copilots. It is especially useful when the agent needs to remember history, follow relationships, and understand how state changes over time.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

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X / Twitter

Discord

Frequently Asked questions

What is AI agent memory for financial services?

AI agent memory gives banking, risk, compliance, and financial agents persistent context across client interactions, transactions, portfolio changes, and compliance decisions. Instead of treating every request independently, the agent can remember previous assessments, account states, regulatory requirements, and decisions across sessions.

Why isn't standard vector search enough for financial services AI?

Vector search retrieves information primarily based on semantic similarity. But financial decisions often depend on relationships, history, and point-in-time state: which regulation applied when a trade was executed, what a client’s KYC status was at the time, or how multiple accounts and counterparties are connected. HydraDB connects these facts in a graph so agents can retrieve the relevant historical state and relationships rather than only similar text chunks.

How does HydraDB help financial agents remember client history?

HydraDB maintains persistent memory across client records, portfolio changes, transactions, compliance reviews, and previous decisions. When an agent evaluates a client or portfolio again, it can retrieve previous assessments, market conditions, risk decisions, and relationship history instead of reconstructing that context from disconnected systems.

How does HydraDB handle financial information that changes over time?

HydraDB preserves changing financial and compliance states in a versioned temporal graph rather than simply overwriting previous information. For example, a client’s KYC status, risk threshold, or regulatory requirement may change over time. HydraDB allows an agent to understand both the current state and what was valid at a specific point in the past.

Can HydraDB help with fraud detection and transaction investigations?

Yes. Fraud and financial investigations often require understanding relationships across accounts, transactions, counterparties, clients, and time periods. HydraDB models these relationships directly in a graph, allowing agents to traverse connected entities and uncover patterns that may be difficult to identify when transactions are analyzed independently.

Can HydraDB provide traceable context for compliance decisions?

Yes. HydraDB can preserve decision lineage and source attribution, connecting retrieved facts back to the regulations, client records, transactions, and other evidence that supports them. This gives compliance and risk agents more traceable context for understanding why a previous decision was made instead of relying on isolated retrieved text.

What financial data can I bring into HydraDB?

You can bring client profiles, KYC and AML records, transaction histories, portfolio data, compliance decisions, regulatory documents, risk assessments, and other structured or unstructured financial data into HydraDB. HydraDB can then resolve entities, connect related accounts and transactions, preserve historical states, and organize this information into persistent context for financial AI agents.

Why is HydraDB a strong choice for building financial services AI?

Financial services is inherently temporal and relational. The right decision often depends on what was true at a specific point in time, how a client’s risk or compliance state changed, which regulations applied, and how accounts, transactions, counterparties, and decisions are connected. HydraDB is built for this kind of long-lived financial context. Its temporal graph preserves historical state and decision lineage, while fast graph traversal retrieves connected context across complex entity networks. Its object-storage-native architecture also makes large, growing financial histories more affordable to retain while keeping frequently accessed context fast.

How can we bring financial data into HydraDB?

HydraDB can ingest data from your existing financial infrastructure using connectors, ingestion APIs, and existing data pipelines. You can extract records from data warehouses, stream transaction data from message queues, or ingest documents from secure storage. HydraDB can then structure the information, resolve related entities, and track how financial and compliance states change over time. Learn more about HydraDB connectors.

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

Frequently Asked questions

What is HydraDB? How is it different from other graph databases?

Why use a graph database like HydraDB for AI agents?

When should I use HydraDB instead of a vector database?

Does HydraDB work with GraphRAG?

How does HydraDB improve retrieval for AI agents?

Can I use HydraDB with my existing AI stack?

How does HydraDB use context graphs for AI agents?

What AI use cases is HydraDB best suited for?

Don’t see your use case?

HydraDB is general-purpose graph-native memory infrastructure. If your AI needs persistent, structured context, it can probably help — talk to us.

Read the docs

Talk to us

X / Twitter

Discord