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Architecture

Benchmarks

Pricing

Use Cases

Resources

Documentation

Sales & GTM

HydraDB for

AI Agent Memory for Sales

Enterprise sales cycles take months or years, but most AI agents forget what happened yesterday. HydraDB gives your sales copilots and CRM agents a persistent, structured memory across transcripts, emails, and CRM records. Stop stuffing entire transcripts into context windows and start giving your agents the ability to track evolving requirements, map complex stakeholder relationships, and automatically extract commitments.

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90%

Recall accuracy on LongMemEval

<200ms

Context retrieval latency

1B+

Documents ingested

40%

Reduction in repeat contacts

// The Problem //

Why sales AI agents keep losing context

Most AI sales tools rely on standard vector databases or massive context windows. The result: agents that cannot distinguish past objections from current requirements, or hallucinate because they are overwhelmed by noise.

Flattened deal timelines

Standard vector databases flatten time. Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.

Flattened deal timelines

Standard vector databases flatten time. Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.

Flattened deal timelines

Standard vector databases flatten time. Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.

Context window limits

Stuffing entire call transcripts and email threads into an LLM is expensive, unreliable, and causes critical commitments to be lost in the middle.

Context window limits

Stuffing entire call transcripts and email threads into an LLM is expensive, unreliable, and causes critical commitments to be lost in the middle.

Fragmented stakeholder context

Sales context is siloed across chat, email, and CRM records, so agents fail to map complex stakeholder relationships and account health.

Fragmented stakeholder context

Sales context is siloed across chat, email, and CRM records, so agents fail to map complex stakeholder relationships and account health.

Lost commitments

Important follow-ups get buried in raw text instead of being stored as retrievable facts across channels.

Lost commitments

Important follow-ups get buried in raw text instead of being stored as retrievable facts across channels.

// See It In Action //

An agent that actually remembers

Once your sales data is structured and sent to HydraDB, it assembles the full account context before your LLM responds, pulling the right stakeholder history, temporal state, and cross-channel commitments without token-heavy prompt stuffing.

Live support session — HydraDB context active

Retrieval : 94ms

What did we promise the CTO about data residency last quarter, and did the compliance team approve the updated MSA?

Account Executive

Enterprise sales cycle

Q4 account review

HydraDB recalled:

Fact: Commitment made to CTO Sarah Jenkins on Oct 12 call: EU data residency required for Q1 launch.

Temporal State: Q3 objection, no EU servers, resolved on Nov 5 after infrastructure update.

Relationship: Sarah Jenkins, CTO, approved by Internal Compliance Team on Nov 5.

Document: CRM opportunity record updated Nov 5 with status Approved.

Last quarter, on October 12, we committed to the CTO, Sarah Jenkins, that we would support EU data residency for their Q1 launch. The compliance team approved the updated MSA on November 5, and the CRM record is marked as Approved. You are cleared to send the final contract.

// How Hydra Helps //

Context Infrastructure for Sales Copilots

HydraDB replaces brittle vector search and expensive prompt stuffing with a single graph-native context layer designed for multi-year enterprise relationships.

Persistent account memory

HydraDB stores the full arc of a multi-year account relationship as it is ingested, giving your AI a continuous memory of stakeholder preferences and deal history.

Persistent account memory

HydraDB stores the full arc of a multi-year account relationship as it is ingested, giving your AI a continuous memory of stakeholder preferences and deal history.

Persistent account memory

HydraDB stores the full arc of a multi-year account relationship as it is ingested, giving your AI a continuous memory of stakeholder preferences and deal history.

Relationship-aware retrieval

HydraDB models stakeholder relationships natively through its context graph, connecting emails, calls, people, projects, and decisions into a single retrievable graph.

Relationship-aware retrieval

HydraDB models stakeholder relationships natively through its context graph, connecting emails, calls, people, projects, and decisions into a single retrievable graph.

Temporal context

HydraDB tracks how requirements evolve over long sales cycles using a versioned, append-only graph, so agents know what was true last quarter versus today.

Temporal context

HydraDB tracks how requirements evolve over long sales cycles using a versioned, append-only graph, so agents know what was true last quarter versus today.

Ingestion built for sales data

Ingest structured records from call transcripts, CRM opportunity records, support tickets, emails, Slack, Gmail, Linear, and internal wiki pages.

Ingestion built for sales data

Ingest structured records from call transcripts, CRM opportunity records, support tickets, emails, Slack, Gmail, Linear, and internal wiki pages.

// Get Started //

Four steps to production-ready memory

HydraDB works with any LLM or AI framework. Replace prompt stuffing with precise, structured context retrieval by mapping sales records into HydraDB’s ingestion format and sending them to the ingestion endpoint.

Pull records from your CRM and call-recording tool

Use your CRM, call-recording platform, or messaging tool API or export to retrieve the records you want HydraDB to remember.

import os
from hydra_db import HydraDB

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

transcript = call_platform_client.get_transcript(call_id)
opportunity = crm_client.get_opportunity(opportunity_id)

import os
from hydra_db import HydraDB

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

transcript = call_platform_client.get_transcript(call_id)
opportunity = crm_client.get_opportunity(opportunity_id)

import os
from hydra_db import HydraDB

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

transcript = call_platform_client.get_transcript(call_id)
opportunity = crm_client.get_opportunity(opportunity_id)

Pull records from your CRM and call-recording tool

Use your CRM, call-recording platform, or messaging tool API or export to retrieve the records you want HydraDB to remember.

import os
from hydra_db import HydraDB

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

transcript = call_platform_client.get_transcript(call_id)
opportunity = crm_client.get_opportunity(opportunity_id)

Pull records from your CRM and call-recording tool

Use your CRM, call-recording platform, or messaging tool API or export to retrieve the records you want HydraDB to remember.

import os
from hydra_db import HydraDB

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

transcript = call_platform_client.get_transcript(call_id)
opportunity = crm_client.get_opportunity(opportunity_id)

Map records into HydraDB’s ingestion format

Structure each record using typed fields so HydraDB can resolve entities and extract commitments correctly at ingestion time.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "call_2026_10_12_001",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sarah Jenkins call: data residency",
            "type": "call_transcript",
            "content": { "text": transcript.text },
            "metadata": { "dealStage": "Negotiation" }
        },
        {
            "id": "salesforce_opp_789",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Acme enterprise renewal",
            "type": "salesforce",
            "content": { "text": f"Stage: {opportunity.stage}, Amount: {opportunity.amount}" },
            "metadata": { "pipeline": "enterprise-renewals" }
        }
    ])
)

import json

client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "call_2026_10_12_001",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sarah Jenkins call: data residency",
            "type": "call_transcript",
            "content": { "text": transcript.text },
            "metadata": { "dealStage": "Negotiation" }
        },
        {
            "id": "salesforce_opp_789",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Acme enterprise renewal",
            "type": "salesforce",
            "content": { "text": f"Stage: {opportunity.stage}, Amount: {opportunity.amount}" },
            "metadata": { "pipeline": "enterprise-renewals" }
        }
    ])
)

import json

client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "call_2026_10_12_001",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sarah Jenkins call: data residency",
            "type": "call_transcript",
            "content": { "text": transcript.text },
            "metadata": { "dealStage": "Negotiation" }
        },
        {
            "id": "salesforce_opp_789",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Acme enterprise renewal",
            "type": "salesforce",
            "content": { "text": f"Stage: {opportunity.stage}, Amount: {opportunity.amount}" },
            "metadata": { "pipeline": "enterprise-renewals" }
        }
    ])
)

Map records into HydraDB’s ingestion format

Structure each record using typed fields so HydraDB can resolve entities and extract commitments correctly at ingestion time.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "call_2026_10_12_001",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sarah Jenkins call: data residency",
            "type": "call_transcript",
            "content": { "text": transcript.text },
            "metadata": { "dealStage": "Negotiation" }
        },
        {
            "id": "salesforce_opp_789",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Acme enterprise renewal",
            "type": "salesforce",
            "content": { "text": f"Stage: {opportunity.stage}, Amount: {opportunity.amount}" },
            "metadata": { "pipeline": "enterprise-renewals" }
        }
    ])
)

Map records into HydraDB’s ingestion format

Structure each record using typed fields so HydraDB can resolve entities and extract commitments correctly at ingestion time.

import json

client.context.ingest(
    type="knowledge",
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    app_knowledge=json.dumps([
        {
            "id": "call_2026_10_12_001",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Sarah Jenkins call: data residency",
            "type": "call_transcript",
            "content": { "text": transcript.text },
            "metadata": { "dealStage": "Negotiation" }
        },
        {
            "id": "salesforce_opp_789",
            "tenant_id": "acme_corp",
            "sub_tenant_id": "acct_acme_corp",
            "title": "Acme enterprise renewal",
            "type": "salesforce",
            "content": { "text": f"Stage: {opportunity.stage}, Amount: {opportunity.amount}" },
            "metadata": { "pipeline": "enterprise-renewals" }
        }
    ])
)

Retrieve full context at inference time

Before your agent answers, query HydraDB for precise account history and stakeholder context without hitting context window limits.

result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the CTOs data residency requirement?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

response = llm.chat(
    system=build_prompt(result.data),
    user="Draft a follow-up email to the CTO."
)

result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the CTOs data residency requirement?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

response = llm.chat(
    system=build_prompt(result.data),
    user="Draft a follow-up email to the CTO."
)

result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the CTOs data residency requirement?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

response = llm.chat(
    system=build_prompt(result.data),
    user="Draft a follow-up email to the CTO."
)

Retrieve full context at inference time

Before your agent answers, query HydraDB for precise account history and stakeholder context without hitting context window limits.

result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the CTOs data residency requirement?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

response = llm.chat(
    system=build_prompt(result.data),
    user="Draft a follow-up email to the CTO."
)

Retrieve full context at inference time

Before your agent answers, query HydraDB for precise account history and stakeholder context without hitting context window limits.

result = client.query(
    tenant_id="acme_corp",
    sub_tenant_id="acct_acme_corp",
    query="What is the status of the CTOs data residency requirement?",
    type="knowledge",
    mode="thinking",
    query_apps=True
)

response = llm.chat(
    system=build_prompt(result.data),
    user="Draft a follow-up email to the CTO."
)

Pass exact context to your LLM

Use the retrieved structured memory, temporal state, and relationship data inside your preferred LLM or AI framework.

curl -X POST https://api.hydradb.ai/context/ingest \
  -H "Authorization: Bearer $HYDRA_KEY" \
  -H "Content-Type: application/json" \
  -d

curl -X POST https://api.hydradb.ai/context/ingest \
  -H "Authorization: Bearer $HYDRA_KEY" \
  -H "Content-Type: application/json" \
  -d

curl -X POST https://api.hydradb.ai/context/ingest \
  -H "Authorization: Bearer $HYDRA_KEY" \
  -H "Content-Type: application/json" \
  -d

Pass exact context to your LLM

Use the retrieved structured memory, temporal state, and relationship data inside your preferred LLM or AI framework.

curl -X POST https://api.hydradb.ai/context/ingest \
  -H "Authorization: Bearer $HYDRA_KEY" \
  -H "Content-Type: application/json" \
  -d

Pass exact context to your LLM

Use the retrieved structured memory, temporal state, and relationship data inside your preferred LLM or AI framework.

curl -X POST https://api.hydradb.ai/context/ingest \
  -H "Authorization: Bearer $HYDRA_KEY" \
  -H "Content-Type: application/json" \
  -d

// Why HydraDB //

HydraDB vs. Standard Vector Search for Sales Context

HydraDB gives sales copilots persistent memory, relationship-aware retrieval, temporal context, and structured ingestion where standard vector search treats every query as an isolated semantic lookup.

Features

HydraDB

Standard Vector Search

Cross-session memory

Stores persistent account memory across multi-year sales cycles once data is ingested

Treats every query as an isolated lookup with no historical continuity

Relationship-aware retrieval

Connects stakeholders, emails, transcripts, and CRM records in a graph

Retrieves isolated text chunks based on semantic similarity

Temporal context

Tracks evolving deal states via a versioned graph, distinguishing old objections from new requirements

Flattens time and often surfaces outdated facts as current truth

Deal state tracking

Understands commitments, reversals, and evolving account health

Requires complex manual metadata filtering to track state changes

Ingestion method

Uses structured records mapped to a typed format with support for CRM, Slack, and Gmail

Requires custom chunking, ETL pipelines, and entity resolution

Best fit

Stateful sales copilots, CRM agents, and RevOps workflows that need memory across time

Semantic search over static documents, call snippets, and knowledge bases

Features

Cross-session memory

Relationship-aware retrieval

Temporal context

Deal state tracking

Ingestion method

Best fit

HydraDB

Stores persistent account memory across multi-year sales cycles once data is ingested

Connects stakeholders, emails, transcripts, and CRM records in a graph

Tracks evolving deal states via a versioned graph, distinguishing old objections from new requirements

Understands commitments, reversals, and evolving account health

Uses structured records mapped to a typed format with support for CRM, Slack, and Gmail

Stateful sales copilots, CRM agents, and RevOps workflows that need memory across time

Standard Vector Search

Treats every query as an isolated lookup with no historical continuity

Retrieves isolated text chunks based on semantic similarity

Flattens time and often surfaces outdated facts as current truth

Requires complex manual metadata filtering to track state changes

Requires custom chunking, ETL pipelines, and entity resolution

Semantic search over static documents, call snippets, and knowledge bases

// What Teams Are Saying //

Trusted by teams building sales AI agents

Replacing full-transcript prompt stuffing with HydraDB was a game-changer. We reduced our LLM token costs drastically, and our agent accuracy in recalling historical account context went through the roof. It actually understands what happened on a call six months ago.

David Chen

CTO, AI-Native SalesTech Platform

Standard vector databases kept confusing past customer objections with current deal requirements. HydraDB’s temporal graph fixed this overnight. Our RevOps agents can finally track how a customer’s requirements evolve over a long sales cycle.

Elena Rostova

Head of Engineering, Enterprise CRM 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

X / Twitter

Discord

X / Twitter

Discord

Frequently Asked questions

What is AI agent memory for sales?

AI agent memory gives sales agents persistent context across long-running account relationships. Instead of treating every call, email, or CRM interaction independently, the agent can remember previous conversations, stakeholder preferences, objections, requirements, commitments, and how the deal has changed over time.

Why isn't standard vector search enough for sales agents?

Vector search retrieves information based primarily on semantic similarity. But sales questions often depend on relationships and history: who the decision-maker is, what the CTO requested last quarter, whether an objection was resolved, which stakeholder approved a change, and what the team promised to follow up on. HydraDB connects these facts in a graph so agents can retrieve the relevant account history and relationships rather than only similar pieces of text.

How does HydraDB help sales agents remember account history?

HydraDB maintains persistent memory across calls, emails, CRM records, messages, and other account interactions. This gives sales agents a continuous view of the relationship instead of forcing them to reconstruct the account history from scattered transcripts and records. When an account returns weeks or months later, the agent can retrieve previous requirements, objections, decisions, stakeholder preferences, and commitments before responding.

How does HydraDB handle sales information that changes over time?

HydraDB maintains temporal context so agents can distinguish what was true earlier in a deal from what is true today. For example, a security requirement may have blocked a deal last quarter but been resolved this month. Instead of mixing the old objection with the current state, HydraDB preserves how the account evolved over time so the agent can retrieve the right context for the current stage of the deal.

How does HydraDB help agents understand complex stakeholder relationships?

Enterprise deals often involve many people across engineering, security, procurement, legal, finance, and executive teams. HydraDB connects stakeholders with their roles, conversations, requirements, decisions, projects, and commitments in a knowledge graph. This allows a sales agent to understand not only what was discussed, but who requested it, who approved it, and how different stakeholders are connected to the deal.

Can HydraDB help sales agents remember commitments and follow-ups?

Yes. Important commitments often get buried inside call transcripts, emails, and chat messages. HydraDB can structure these facts and connect them to the relevant account, stakeholder, conversation, and point in time. This helps agents retrieve what was promised, when it was promised, who it was promised to, and whether the commitment has since been completed or changed.

What sales data can I bring into HydraDB?

You can bring CRM opportunity records, call transcripts, emails, Slack conversations, support tickets, internal documentation, account notes, and other structured or unstructured sales context into HydraDB. HydraDB can then connect accounts, stakeholders, requirements, objections, conversations, commitments, and decisions into persistent context that sales agents can retrieve later.

Why is HydraDB a strong choice for building AI sales agents?

Sales is inherently stateful. The right action often depends on what happened months ago, which requirements have changed, who the important stakeholders are, what objections have been resolved, and what your team has already promised. HydraDB is built for this kind of long-lived account context. Its temporal graph tracks how deals and requirements change over time, while fast graph traversal retrieves connected stakeholder and account context without stuffing entire call transcripts and email threads into the prompt. This gives sales agents persistent, structured memory across even long enterprise sales cycles.

How can we bring sales data into HydraDB?

HydraDB connectors continuously sync data from supported tools into HydraDB, while ingestion APIs let you bring in records from CRMs, call-recording platforms, and custom sales systems. You can connect sources such as Slack, Gmail, Notion, and other supported apps, or pull records from your CRM and call-recording tools through their APIs or exports. HydraDB can then structure that information and connect accounts, stakeholders, conversations, requirements, and commitments into persistent context for your sales agents. 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