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 Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.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 Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.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 Current approvals get mixed with outdated objections, confusing both the AI and the sales rep.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.