AGI for Sales
AGI for Sales
On the Journey to General
Artificial Intelligence for Sales
On the Journey to General
Artificial Intelligence for Sales
Sales AGI Will Change How Companies Sell
Sales AGI Will Change How Companies Sell
Rippletide aims to unlock previously unreachable markets, where the economics of traditional sales teams breaks. Entire market segments remain unaddressed not because the demand isn't there, but because the unit economics don’t justify human involvement.
The first shift is happening now: those with this advantage are already outcompeting the rest. Soon, those without it won’t be able to compete at all.
Sales remains one of the most human, high-variance domains shaped by trust, ambiguity, and unpredictable interactions. In these conditions, current systems can’t adapt, can’t reason, and can’t sell. Only AGI can meet the complexity of real-world sales.
Rippletide aims to unlock previously unreachable markets, where the economics of traditional sales teams breaks. Entire market segments remain unaddressed not because the demand isn't there, but because the unit economics don’t justify human involvement.
The first shift is happening now: those with this advantage are already outcompeting the rest. Soon, those without it won’t be able to compete at all.
Sales remains one of the most human, high-variance domains shaped by trust, ambiguity, and unpredictable interactions. In these conditions, current systems can’t adapt, can’t reason, and can’t sell. Only AGI can meet the complexity of real-world sales.
Structural Shifts Ahead
Structural Shifts Ahead
Sales costs are unsustainable
Sales costs are unsustainable
Rising sales costs are no longer sustainable. Salaries, hiring, and training for SDRs, AEs, and RevOps teams are climbing rapidly while human-led sales motions now demand more touchpoints, personalization, and coordination just to deliver the same outcomes. This compounds the cost per meeting, per SQL, and per dollar of revenue. Low-ticket opportunities, especially in the long tail, become unscalable with humans alone, even though collectively, they represent billions in untapped ARR. Sales AGI unlocks this segment by scaling down cost per interaction while maintaining quality, making previously unaddressable GTM motions viable again.
Rising sales costs are no longer sustainable. Salaries, hiring, and training for SDRs, AEs, and RevOps teams are climbing rapidly while human-led sales motions now demand more touchpoints, personalization, and coordination just to deliver the same outcomes. This compounds the cost per meeting, per SQL, and per dollar of revenue. Low-ticket opportunities, especially in the long tail, become unscalable with humans alone, even though collectively, they represent billions in untapped ARR. Sales AGI unlocks this segment by scaling down cost per interaction while maintaining quality, making previously unaddressable GTM motions viable again.
Transactions are increasingly automated
Transactions are increasingly automated
Sales is becoming a system-to-system process, transactional sales are shifting from human-led conversations to software-to-software execution. As organizations seek to scale without adding headcount, automation handles increasing volumes of lead qualification, follow-ups, renewals, and upsells. The traditional cost structure designed for reps starts to collapse under this shift. Instead, a new architecture emerges: autonomous agents interacting in real time, responding, escalating, and closing, without requiring human intervention. In this model, software is not just a tool for efficiency, it’s the foundation of sales execution.
Sales is becoming a system-to-system process, transactional sales are shifting from human-led conversations to software-to-software execution. As organizations seek to scale without adding headcount, automation handles increasing volumes of lead qualification, follow-ups, renewals, and upsells. The traditional cost structure designed for reps starts to collapse under this shift. Instead, a new architecture emerges: autonomous agents interacting in real time, responding, escalating, and closing, without requiring human intervention. In this model, software is not just a tool for efficiency, it’s the foundation of sales execution.
Organisations will rebuild around a central AI core, not tools
Organisations will rebuild around a central AI core, not tools
Modern sales orgs are drowning in tools, 15+ per rep is the norm. But more tools often mean more friction: disconnected workflows, context switching, and lost time. Rather than accelerating productivity, this fragmentation inflates costs and slows execution. The next generation of sales organizations won’t be built around a patchwork of apps, they’ll be built around an AI core. This central intelligence will observe the full funnel, decide what action is needed, and coordinate agents across the GTM stack. Rippletide is architected to be that AI core, one that reasons, adapts, and drives outcomes.
Modern sales orgs are drowning in tools, 15+ per rep is the norm. But more tools often mean more friction: disconnected workflows, context switching, and lost time. Rather than accelerating productivity, this fragmentation inflates costs and slows execution. The next generation of sales organizations won’t be built around a patchwork of apps, they’ll be built around an AI core. This central intelligence will observe the full funnel, decide what action is needed, and coordinate agents across the GTM stack. Rippletide is architected to be that AI core, one that reasons, adapts, and drives outcomes.
The Hard Problem - Why LLMs will never make it?
Not Goal-Driven. Agentic LLMs follow workflows by design, they maximize text coherence over seeking to close a deal. They can simulate conversation, but they lack the ability to pursue outcomes, weigh trade-offs, or commit to a strategy - the core elements of what makes selling effective.
Not Goal-Driven. Agentic LLMs follow workflows by design, they maximize text coherence over seeking to close a deal. They can simulate conversation, but they lack the ability to pursue outcomes, weigh trade-offs, or commit to a strategy - the core elements of what makes selling effective.
Timing is Everything. Selling is about knowing when to act, when to follow up, when to escalate, when to hold back. This requires temporal reasoning. And beyond timing, great sellers are proactive, they don’t wait for inputs, they sense momentum and take initiative. LLMs can only react, they cannot anticipate or drive the process forward.
Timing is Everything. Selling is about knowing when to act, when to follow up, when to escalate, when to hold back. This requires temporal reasoning. And beyond timing, great sellers are proactive, they don’t wait for inputs, they sense momentum and take initiative. LLMs can only react, they cannot anticipate or drive the process forward.
Too Many Humans, Too Many Paths. Predefined workflows break due to unpredictable human behaviors. Real-world sales requires interpreting intent, reading subtext, and adjusting course dynamically. These capabilities demand situational reasoning, not scripted responses.
Too Many Humans, Too Many Paths. Predefined workflows break due to unpredictable human behaviors. Real-world sales requires interpreting intent, reading subtext, and adjusting course dynamically. These capabilities demand situational reasoning, not scripted responses.
Not Built for AGI. LLMs are auto-regressive, i.e. they generate tokens based on previous ones, with no true understanding of goals, plans, or consequences. They can imitate reasoning, but they don’t pursue objectives or model the world beyond text patterns. As Yann LeCun said: “If you’re interested in human-level AI, don’t work on LLMs” (Feb 2025).
Not Built for AGI. LLMs are auto-regressive, i.e. they generate tokens based on previous ones, with no true understanding of goals, plans, or consequences. They can imitate reasoning, but they don’t pursue objectives or model the world beyond text patterns. As Yann LeCun said: “If you’re interested in human-level AI, don’t work on LLMs” (Feb 2025).
Our Breakthrough Reasoning Engine
Foundational Evaluation
Outcomes are quantitatively evaluated before action. This enables the system to simulate multiple paths in advance and select the most effective sequence of actions to close a deal.
State-of-the-art decision-making methods often rely on Deep Reinforcement Learning, for sequential optimization tasks but can’t be applied in sales as there is not enough high quality sales data for most companies. Synthetic data make no sense in sales due to humans' irrational behaviors. Deepmind's AlphaGo required millions of Go games against itself to train.
Monte Carlo Tree Search is often used as a planning method, yet it tends to underperform in highly non-deterministic environments such as sales where human behavior introduces noise, unpredictability and non-stationary (i.e. depending on time) setup. Our neuro-symbolic combines the best of each world surpassing them on most planning benchmarks.
Autonomy-Ready Data Architecture
New paradigms require innovative data structures that support context awareness and proactivity.
State-of-the-art systems often employ knowledge graphs to represent and reason over complex relational data. Traditional knowledge graphs struggle in a dynamic environment with real-time updates and high dimensionality evolving contexts, preventing proactive actions. Our systems can proactively suggest a decision improving the probability to close a deal.
Explainable Decision Making
Each action can be explained and objectified. The engine elucidates why a decision was made and links it to the outcomes, exposing the reasoning behind every step.
This transparency builds trust, enables optimization, and keeps human oversight aligned with machine intent. Most current systems lack true explainability.
For instance, recent reasoning models based on chain-of-thought prompting have been shown to produce unfaithful explanations, Turpin et al. (2023). Our method falls into the Hybrid Transparent Methods category leading to transparency and explainability as defined in the standard taxonomy by Barredo Arrieta et al. (2020).
Our Breakthrough Reasoning Engine
Foundational Evaluation
Outcomes are quantitatively evaluated before action. This enables the system to simulate multiple paths in advance and select the most effective sequence of actions to close a deal.
State-of-the-art decision-making methods often rely on Deep Reinforcement Learning, for sequential optimization tasks but can’t be applied in sales as there is not enough high quality sales data for most companies. Synthetic data make no sense in sales due to humans' irrational behaviors. Deepmind's AlphaGo required millions of Go games against itself to train.
Monte Carlo Tree Search is often used as a planning method, yet it tends to underperform in highly non-deterministic environments such as sales where human behavior introduces noise, unpredictability and non-stationary (i.e. depending on time) setup. Our neuro-symbolic combines the best of each world surpassing them on most planning benchmarks.
Autonomy-Ready Data Architecture
New paradigms require innovative data structures that support context awareness and proactivity.
State-of-the-art systems often employ knowledge graphs to represent and reason over complex relational data. Traditional knowledge graphs struggle in a dynamic environment with real-time updates and high dimensionality evolving contexts, preventing proactive actions. Our systems can proactively suggest a decision improving the probability to close a deal.
Explainable Decision Making
Each action can be explained and objectified. The engine elucidates why a decision was made and links it to the outcomes, exposing the reasoning behind every step.
This transparency builds trust, enables optimization, and keeps human oversight aligned with machine intent. Most current systems lack true explainability.
For instance, recent reasoning models based on chain-of-thought prompting have been shown to produce unfaithful explanations, Turpin et al. (2023). Our method falls into the Hybrid Transparent Methods category leading to transparency and explainability as defined in the standard taxonomy by Barredo Arrieta et al. (2020).
Need help for your sales reps?
Rippletide toward Sales AGI
Rippletide Long-Term Vision
Rippletide Long-Term Vision
Build an AI-native GTM platform where agents don't assist, they act.
Evolve from modular agents to a reasoning-capable orchestration core (Eddy) that learns, adapts, and commands.
Replace repetitive workflows with autonomous execution, starting in sales, expanding to full revenue operations.
Drastically reduce the human cost of scaling revenue, especially in high-volume, low-touch segments.
Build an AI-native GTM platform where agents don't assist, they act.
Evolve from modular agents to a reasoning-capable orchestration core (Eddy) that learns, adapts, and commands.
Replace repetitive workflows with autonomous execution, starting in sales, expanding to full revenue operations.
Drastically reduce the human cost of scaling revenue, especially in high-volume, low-touch segments.
What ships today
What ships today
Rippletide reduces cost of sales regardless of the organization complexity or scale, expanding the applicability of our advancements.
Rippletide reduces cost of sales regardless of the organization complexity or scale, expanding the applicability of our advancements.
Agents generating autonomously revenue on a wide variety of use-cases.
Rippletide AGI path
Rippletide AGI path
Phase 1: Agent Utility
Launch task-specific agents (lead qual, CRM updates, note taking) that deliver value fast, with zero setup.
Phase 2: Centralized Orchestration
Introduce Eddy as the reasoning layer that oversees all agents, enabling coordination, fallback, and self-optimization.
Phase 3: Autonomous Organization
Eddy evolves into a persistent team leader: it remembers, adapts, and eventually spawns new agents autonomously.
Phase 4: Generalized Application
Extend beyond sales—applying the same logic architecture to support, hiring, and enterprise workflows.
Phase 1: Agent Utility
Launch task-specific agents (lead qual, CRM updates, note taking) that deliver value fast, with zero setup.
Phase 2: Centralized Orchestration
Introduce Eddy as the reasoning layer that oversees all agents, enabling coordination, fallback, and self-optimization.
Phase 3: Autonomous Organization
Eddy evolves into a persistent team leader: it remembers, adapts, and eventually spawns new agents autonomously.
Phase 4: Generalized Application
Extend beyond sales—applying the same logic architecture to support, hiring, and enterprise workflows.
FAQ
Frequently Asked Questions
What makes Sales AGI different from a traditional sales chatbot or copilot?
Why is AGI needed in sales, can’t automation alone solve the problem?
Will Sales AGI replace human sales reps?
What kinds of sales motions are best suited for autonomous agents?
What data does the engine need to perform well?
Can it understand and handle our specific GTM or sales process?
Is it possible to see results with only a first use case?
What is an AI Sales Agent?
What is the difference between an AI Sales Copilot and an AI Sales Agent?
Still have a question?
Get in touch with us and let's discuss it.
FAQ
Frequently Asked Questions
What makes Sales AGI different from a traditional sales chatbot or copilot?
Why is AGI needed in sales, can’t automation alone solve the problem?
Will Sales AGI replace human sales reps?
What kinds of sales motions are best suited for autonomous agents?
What data does the engine need to perform well?
Can it understand and handle our specific GTM or sales process?
Is it possible to see results with only a first use case?
What is an AI Sales Agent?
What is the difference between an AI Sales Copilot and an AI Sales Agent?
Still have a question?
Get in touch with us and let's discuss it.
FAQ
Frequently Asked Questions
What makes Sales AGI different from a traditional sales chatbot or copilot?
Why is AGI needed in sales, can’t automation alone solve the problem?
Will Sales AGI replace human sales reps?
What kinds of sales motions are best suited for autonomous agents?
What data does the engine need to perform well?
Can it understand and handle our specific GTM or sales process?
Is it possible to see results with only a first use case?
What is an AI Sales Agent?
What is the difference between an AI Sales Copilot and an AI Sales Agent?
Still have a question?
Get in touch with us and let's discuss it.
Rippletide deploys autonomous AI Sales Agents that engage every lead 24/7, adapt in real time, and turn conversations into revenue.
© 2025 Rippletide All rights reserved.
Rippletide deploys autonomous AI Sales Agents that engage every lead 24/7, adapt in real time, and turn conversations into revenue.
© 2025 Rippletide All rights reserved.
Rippletide deploys autonomous AI Sales Agents that engage every lead 24/7, adapt in real time, and turn conversations into revenue.
© 2025 Rippletide All rights reserved.