@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md

SalesAI

Two products for B2B revenue teams, built on one sales knowledge graph. One reads everything the team says to customers and turns it into revenue you can manage. The other does the talking.

1. Managed Revenue Growth

Revenue Intelligence built on a knowledge graph, not on keyword spotting.

SalesAI reads 100% of what your team says to customers - calls, meetings, email

  • and turns it into three things a sales leader can act on: a filled CRM, a forecast with the reasoning attached, and a list of deals that are slipping right now.

The mechanism is a knowledge graph of 70+ interconnected entity nodes covering the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the open internet, extracts 19 entities from every conversation (budget, decision maker, pain, objection, next step, and the rest) and resolves them against the graph. The model computes the answer through the graph instead of guessing at it, which is why the output survives contact with a CRO who asks "based on what?".

Speech analytics answers how the call went. Managed Revenue Growth answers where the money is, and what to do about it this week.

CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.

2. Elektronik, the full-cycle sales agent

Most AI SDRs work the top of the funnel. They send email, book a meeting and hand over a name. Elektronik carries the lead from the first touch to the close and into expansion, and it remembers everything that happened on the way.

CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.

What it is not: a replacement for the sales team. It takes the flow up to the moment a deal genuinely needs a person, which is exactly the moment most teams never reach, because their people are busy with the flow.

Reference numbers from the product page: managers spend 40 to 60% of their time on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost per booked meeting drops by three to five times.

Why they are one platform and not two tools

The graph is the reason. Everything the first product hears becomes structure: who decides, what the objection was, what was promised and by when. The agent works on that same structure, so it starts a call already knowing what the last one produced, and everything it does lands back in the same place.

Bolt a separate AI SDR onto a separate analytics tool and you get two systems with two versions of the customer, arguing through a CRM neither of them trusts. Here there is one version, and both products are views over it.

How it runs

Cloud, private cloud or on-premise. The MCP server and the LLM run inside the customer perimeter, so transcripts, customer names and deal values never leave it. Integrates with CRM, telephony and BI over API.

Time to first value: 2 working days from contract.

Links

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
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  1. 
.github
.githubPublic
    
SalesAI organization profile. Source of profile/README.md


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, 'i');
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Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
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.github
.githubPublic
    
SalesAI organization profile. Source of profile/README.md


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, 'i');
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(function(){
try {
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Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
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SalesAI organization profile. Source of profile/README.md


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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
Loading
  1. 
.github
.githubPublic
    
SalesAI organization profile. Source of profile/README.md


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Showing 1 of 1 repositories
People

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Top languages
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Loading…

, 'i');
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@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
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Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
Loading
  1. 
.github
.githubPublic
    
SalesAI organization profile. Source of profile/README.md


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Showing 1 of 1 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

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, 'i');
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Skip to content
@SalesAI-ru

SalesAI

Managed Revenue Growth for B2B: a 70-node sales knowledge graph turns every customer conversation into CRM records, forecast and risk signals.
README.md
SalesAI
Two products for B2B revenue teams, built on one sales knowledge graph.
One reads everything the team says to customers and turns it into revenue you
can manage. The other does the talking.
1. Managed Revenue Growth
Revenue Intelligence built on a knowledge graph, not on keyword spotting.
SalesAI reads 100% of what your team says to customers - calls, meetings, email
  • and turns it into three things a sales leader can act on: a filled CRM, a
forecast with the reasoning attached, and a list of deals that are slipping
right now.
The mechanism is a knowledge graph of 70+ interconnected entity nodes covering
the stages of a B2B deal. A proprietary LLM, trained on sales rather than on the
open internet, extracts 19 entities from every conversation (budget, decision
maker, pain, objection, next step, and the rest) and resolves them against the
graph. The model computes the answer through the graph instead of guessing at
it, which is why the output survives contact with a CRO who asks "based on
what?".
Speech analytics answers how the call went. Managed Revenue Growth answers where
the money is, and what to do about it this week.
CRM that fills itselfFacts from the conversation land in the deal card. No rep typing, no QA team listening to 3% of calls.
Account IntelligenceEvery account as one card: full history, extracted facts, open commitments, Health Score, churn signals.
Forecast with receiptsPipeline review by stage and by rep, with the conversation evidence behind each number.
Coaching per repWhere each person loses deals, in their own calls, with the competency map.
Ask your dataAn MCP server inside your perimeter. Ask in chat, Telegram or BI and get the answer in seconds instead of a ticket to analytics.
2. Elektronik, the full-cycle sales agent
Most AI SDRs work the top of the funnel. They send email, book a meeting and
hand over a name. Elektronik carries the lead from the first touch to the close
and into expansion, and it remembers everything that happened on the way.
CallsIt dials, holds the conversation and records the outcome. Voice, not just email, which is how it reaches the people who never answer a form.
QualifiesSPICED, with a time budget that matches the lead: roughly fifteen minutes on a cold one, three questions on an unknown inbound.
Uncovers the needAnd writes what it heard into the deal card as facts, not as a call summary nobody reads.
Handles objectionsIn the conversation, instead of escalating every one of them to a person.
Routes onward by tierA small self-serve account it finishes itself. A large one it prepares and hands to a human at the exact point where a human changes the outcome. The routing rule is a policy the customer sets, not a guess the model makes.
Never drops a follow-upEvery promised next step exists as a task with a time, and it runs at night, at the weekend and in the fourth quarter alike.
What it is not: a replacement for the sales team. It takes the flow up to the
moment a deal genuinely needs a person, which is exactly the moment most teams
never reach, because their people are busy with the flow.
Reference numbers from the product page: managers spend 40 to 60% of their time
on routine, the cycle runs 30 to 40% faster with the agent in it, and the cost
per booked meeting drops by three to five times.
Why they are one platform and not two tools
The graph is the reason. Everything the first product hears becomes structure:
who decides, what the objection was, what was promised and by when. The agent
works on that same structure, so it starts a call already knowing what the last
one produced, and everything it does lands back in the same place.
Bolt a separate AI SDR onto a separate analytics tool and you get two systems
with two versions of the customer, arguing through a CRM neither of them
trusts. Here there is one version, and both products are views over it.
How it runs
Cloud, private cloud or on-premise. The MCP server and the LLM run inside the
customer perimeter, so transcripts, customer names and deal values never leave
it. Integrates with CRM, telephony and BI over API.
Time to first value: 2 working days from contract.
Links

Popular repositories
Loading
  1. 
.github
.githubPublic
    
SalesAI organization profile. Source of profile/README.md


Repositories

Showing 1 of 1 repositories
People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages
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Most used topics
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