Architecture

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Resources

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Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

Developer

HydraDB for

Customer Support AI

Building support agents that actually remember customer history is harder than it should be. Standard vector search treats every ticket as isolated, forcing engineers to build custom metadata pipelines just to track what happened yesterday. HydraDB provides the memory infrastructure your agents need to resolve ambiguous transcripts, recall human-solved edge cases, and maintain context across multi-year customer relationships.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why customer support AI keeps failing

Support agents built on stateless RAG treat every query as brand new. The result: bots that force users to repeat themselves, fail to understand informal chat transcripts, and escalate issues that human agents already solved.

Unresolved entity ambiguity

When a customer says "I'm still having that issue from yesterday," standard vector stores save the text verbatim. Without entity resolution during ingestion, "that issue" becomes semantically invisible, making the recalled chunk useless.

Unresolved entity ambiguity

Unresolved entity ambiguity

Re-escalating solved problems

Complex edge cases leave decision traces in Zendesk and Jira. But stateless agents can't connect new tickets to past resolutions, blindly escalating problems that already have documented solutions.

Re-escalating solved problems

Complex edge cases leave decision traces in Zendesk and Jira. But stateless agents can't connect new tickets to past resolutions, blindly escalating problems that already have documented solutions.

Cross-session memory loss

Customers expect continuity. But managing user-specific history requires complex metadata filtering on vector stores. When those filters fail, users repeat the same information every session.

Cross-session memory loss

Customers expect continuity. But managing user-specific history requires complex metadata filtering on vector stores. When those filters fail, users repeat the same information every session.

Deep context latency spikes

Support queries often need ticket history plus technical documentation from Confluence or GitHub. Stitching this together at runtime causes severe latency, breaking real-time chat.

Deep context latency spikes

Support queries often need ticket history plus technical documentation from Confluence or GitHub. Stitching this together at runtime causes severe latency, breaking real-time chat.

// See It In Action //

An agent that actually remembers

HydraDB structures your historical tickets, chat transcripts, and issue tracking into a temporal graph. Before responding, your agent retrieves precise multi-session context so it knows what was tried, what failed, and what shipped.

Live support session — HydraDB context active

Retrieval : 94ms

The sync latency issue from ticket #8821 is back. Did the engineering team ever deploy that fix we discussed?

Marcus Chen

Customer account

Recurring support case

HydraDB recalled:

Marcus Chen reported sync latency on Nov 14 (Ticket #8821)

Marcus Chen → Escalated to → Tier 2 Support (Nov 14)

Jira Issue ENG-402 linked to Ticket #8821 (Merged: Nov 16)

Sync latency resolved Nov 16; recurrence reported Nov 22

Hi Marcus, I see you originally reported sync latency on November 14 in ticket #8821. Engineering merged the fix (ENG-402) on November 16. Since you’re experiencing this again today, I’m reopening the ticket with full context of the previous patch and escalating directly to Tier 2.

// How Hydra Helps //

Context Infrastructure for Support Agents

HydraDB replaces brittle RAG pipelines with graph-native memory designed for support workflows. It provides the persistent brain your CX automation needs.

Persistent memory

Every chat, email, and ticket becomes structured memory. Your agents maintain continuous context of user preferences, past frustrations, and account state across sessions.

Persistent memory

Every chat, email, and ticket becomes structured memory. Your agents maintain continuous context of user preferences, past frustrations, and account state across sessions.

Persistent memory

Every chat, email, and ticket becomes structured memory. Your agents maintain continuous context of user preferences, past frustrations, and account state across sessions.

Relationship-aware retrieval

HydraDB connects users, tickets, and documentation in a traversable graph. An Intercom chat links to backend Jira issues, giving agents the exact engineering precedent to resolve complex queries.

Relationship-aware retrieval

HydraDB connects users, tickets, and documentation in a traversable graph. An Intercom chat links to backend Jira issues, giving agents the exact engineering precedent to resolve complex queries.

Temporal context

Track how ticket states evolve. The append-only temporal graph distinguishes yesterday's open bug from today's merged fix with 97.4% accuracy on knowledge updates.

Temporal context

Track how ticket states evolve. The append-only temporal graph distinguishes yesterday's open bug from today's merged fix with 97.4% accuracy on knowledge updates.

Ingestion built for support workflows

Ingest support tickets, chat transcripts, emails, Slack conversations, Jira issues, and internal wiki pages. HydraDB connects customer history, troubleshooting steps, and resolution outcomes into a unified memory layer, enabling agents to deliver consistent support across every interaction.

Ingestion built for support workflows

// Get Started //

Three steps to production-ready memory

HydraDB works with any LLM. Stop building metadata indexes and start retrieving structured history.

Connect your ticketing systems

Pull historical tickets and transcripts from your existing tools.

import os
from hydra_db import HydraDB

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

# Retrieve a transcript and an opportunity record using
# each platform's own API or export tooling.
ticket = support_platform_client.get_ticket(8821)
issue = issue_tracker_client.get_issue(ticket.custom_fields.issue_id)

import os
from hydra_db import HydraDB

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

# Retrieve a transcript and an opportunity record using
# each platform's own API or export tooling.
ticket = support_platform_client.get_ticket(8821)
issue = issue_tracker_client.get_issue(ticket.custom_fields.issue_id)

import os
from hydra_db import HydraDB

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

# Retrieve a transcript and an opportunity record using
# each platform's own API or export tooling.
ticket = support_platform_client.get_ticket(8821)
issue = issue_tracker_client.get_issue(ticket.custom_fields.issue_id)

Connect your ticketing systems

Pull historical tickets and transcripts from your existing tools.

import os
from hydra_db import HydraDB

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

# Retrieve a transcript and an opportunity record using
# each platform's own API or export tooling.
ticket = support_platform_client.get_ticket(8821)
issue = issue_tracker_client.get_issue(ticket.custom_fields.issue_id)

Connect your ticketing systems

Pull historical tickets and transcripts from your existing tools.

import os
from hydra_db import HydraDB

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

# Retrieve a transcript and an opportunity record using
# each platform's own API or export tooling.
ticket = support_platform_client.get_ticket(8821)
issue = issue_tracker_client.get_issue(ticket.custom_fields.issue_id)

Ingest and structure support context

Map records to HydraDB’s format. Entity resolution and relationship linking happen automatically.

import json

# Ingest the ticket and issue record using the
# records retrieved in the step above. HydraDB resolves
# entities and links them at indexing time.
client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "ticket_8821",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Marcus Chen ticket: sync latency",
            "type": "support_ticket",
            "content": { "text": ticket.description },
            "metadata": { "status": "resolved" }
        },
        {
            "id": "eng_402",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sync latency bug fix",
            "type": "engineering_issue",
            "content": { "text": f"Status: {issue.status}, Resolution: {issue.resolution}" },
            "metadata": { "pipeline": "engineering" }
        }
    ])
)

import json

# Ingest the ticket and issue record using the
# records retrieved in the step above. HydraDB resolves
# entities and links them at indexing time.
client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "ticket_8821",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Marcus Chen ticket: sync latency",
            "type": "support_ticket",
            "content": { "text": ticket.description },
            "metadata": { "status": "resolved" }
        },
        {
            "id": "eng_402",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sync latency bug fix",
            "type": "engineering_issue",
            "content": { "text": f"Status: {issue.status}, Resolution: {issue.resolution}" },
            "metadata": { "pipeline": "engineering" }
        }
    ])
)

import json

# Ingest the ticket and issue record using the
# records retrieved in the step above. HydraDB resolves
# entities and links them at indexing time.
client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "ticket_8821",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Marcus Chen ticket: sync latency",
            "type": "support_ticket",
            "content": { "text": ticket.description },
            "metadata": { "status": "resolved" }
        },
        {
            "id": "eng_402",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sync latency bug fix",
            "type": "engineering_issue",
            "content": { "text": f"Status: {issue.status}, Resolution: {issue.resolution}" },
            "metadata": { "pipeline": "engineering" }
        }
    ])
)

Ingest and structure support context

Map records to HydraDB’s format. Entity resolution and relationship linking happen automatically.

import json

# Ingest the ticket and issue record using the
# records retrieved in the step above. HydraDB resolves
# entities and links them at indexing time.
client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "ticket_8821",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Marcus Chen ticket: sync latency",
            "type": "support_ticket",
            "content": { "text": ticket.description },
            "metadata": { "status": "resolved" }
        },
        {
            "id": "eng_402",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sync latency bug fix",
            "type": "engineering_issue",
            "content": { "text": f"Status: {issue.status}, Resolution: {issue.resolution}" },
            "metadata": { "pipeline": "engineering" }
        }
    ])
)

Ingest and structure support context

Map records to HydraDB’s format. Entity resolution and relationship linking happen automatically.

import json

# Ingest the ticket and issue record using the
# records retrieved in the step above. HydraDB resolves
# entities and links them at indexing time.
client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "ticket_8821",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Marcus Chen ticket: sync latency",
            "type": "support_ticket",
            "content": { "text": ticket.description },
            "metadata": { "status": "resolved" }
        },
        {
            "id": "eng_402",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sync latency bug fix",
            "type": "engineering_issue",
            "content": { "text": f"Status: {issue.status}, Resolution: {issue.resolution}" },
            "metadata": { "pipeline": "engineering" }
        }
    ])
)

Retrieve context before responding

Query user history before your LLM generates responses.

# Retrieve structured account history before LLM generation
result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the sync latency issue from last week?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user=message
)

# Retrieve structured account history before LLM generation
result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the sync latency issue from last week?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user=message
)

# Retrieve structured account history before LLM generation
result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the sync latency issue from last week?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user=message
)

Retrieve context before responding

Query user history before your LLM generates responses.

# Retrieve structured account history before LLM generation
result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the sync latency issue from last week?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user=message
)

Retrieve context before responding

Query user history before your LLM generates responses.

# Retrieve structured account history before LLM generation
result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the sync latency issue from last week?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

# Pass exact context to your LLM
response = llm.chat(
    system=build_prompt(result.data),
    user=message
)

// Why HydraDB //

HydraDB vs. Standard Vector Search for Customer Support

HydraDB gives support automation persistent user history, relationship-aware retrieval, temporal context, and structured ingestion where standard vector search treats every query as an isolated lookup.

Features

HydraDB

Standard Vector Search

Cross-session memory

Persistent user history across years

Isolated queries with no continuity

Relationship-aware retrieval

Graph connects users, tickets, issues

Pure semantic similarity chunks

Temporal context

Tracks evolving states and resolutions

Flattens time, surfaces outdated info

Entity resolution

Resolves pronouns during ingestion

Stores ambiguous text verbatim

Escalation context

Retrieves human decision traces

Cannot map past resolutions to new queries

Integration method

Structured records from native APIs

Custom chunking and ETL glue code

Best fit

Stateful agents and technical triage

Static FAQs and knowledge bases

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Entity resolution

Escalation context

Integration method

Best fit

HydraDB

Persistent user history across years

Graph connects users, tickets, issues

Tracks evolving states and resolutions

Resolves pronouns during ingestion

Retrieves human decision traces

Structured records from native APIs

Stateful agents and technical triage

Standard Vector Search

Isolated queries with no continuity

Pure semantic similarity chunks

Flattens time, surfaces outdated info

Stores ambiguous text verbatim

Cannot map past resolutions to new queries

Custom chunking and ETL glue code

Static FAQs and knowledge bases

// What Teams Are Saying //

Trusted by teams building support automation

We spent weeks building metadata filters just to remember yesterday’s conversation. HydraDB replaced all that brittle code. Our agents now recall exact ticket precedent and stop re-escalating solved problems.

Sarah Lin

Head of AI Engineering, CX Platform

Vector databases fail on support transcripts because users say things like “that bug” or “the issue we discussed”. HydraDB’s ingestion resolves those entities automatically. First memory layer that actually understands support conversations.

James Rodriguez

CTO, Enterprise Support Copilot

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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Frequently Asked questions

What is AI agent memory for customer support?

AI agent memory gives a support agent persistent context across conversations. Instead of treating every ticket or chat as a new interaction, the agent can recall previous issues, troubleshooting steps, resolutions, account history, and related events before responding.

Why isn't vector search enough for customer support agents?

Vector search retrieves text based primarily on semantic similarity. That works well for static knowledge bases, but support questions often depend on relationships and history: which customer reported an issue, which ticket it came from, which Jira issue was created, whether a fix shipped, and what happened afterward. HydraDB connects these facts in a graph so agents can retrieve the relevant history rather than just similar chunks.

How does HydraDB help support agents remember previous conversations?

HydraDB turns chats, emails, tickets, issues, and other support records into persistent structured memory. When a customer returns, the agent can retrieve relevant context from previous sessions instead of asking the customer to repeat the same information.

What customer support data can I store in HydraDB?

HydraDB can ingest support tickets, chat transcripts, emails, Slack conversations, Jira issues, internal documentation, and other structured or unstructured support context. These records can then be connected around customers, issues, troubleshooting steps, engineering work, and resolutions.

How does HydraDB handle information that changes over time?

HydraDB maintains temporal context so agents can distinguish old state from current state. For example, an issue may have been open last week, fixed two days later, and reported again today. Instead of flattening those events into disconnected chunks, HydraDB preserves how the state changed over time.

How can we bring support data into HydraDB?

HydraDB connectors continuously sync data from your existing tools into HydraDB, so your agents can work with up-to-date context without building custom ingestion pipelines for every source. You can connect support and internal systems such as Jira, Slack, Gmail, Notion, and other supported apps, then sync conversations, issues, documentation, and related records into HydraDB. HydraDB can use this data to build persistent, connected context around customers, issues, conversations, troubleshooting steps, and resolutions. Learn more about HydraDB connectors.

Why is HydraDB a strong choice for building customer support AI?

Customer support is stateful. The right answer often depends on who the customer is, what happened before, what changed, and how different tickets, conversations, issues, and resolutions are connected. HydraDB is built for this kind of long-lived agent context. Its temporal graph keeps track of how facts and relationships change over time, while fast graph traversal helps agents retrieve the connected context they need without scanning large amounts of unrelated history. It is also designed to be more affordable and faster than other solutions at scale.

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