Meet Peakflo's AI Voice Agents – your humanlike, scalable, always-on team member that handles business ops calls, retains memory, triggers logic based actions and updates your system of record in real time.
I’m Saurabh, co-founder of Peakflo (YC W22), and I’m super excited to launch Peakflo AI Voice Agents!
During conversations with our clients, one challenge stands out across industries (whether thats in insurance, healthcare or even logistics): teams spend countless hours on calls, follow-ups, and manual system updates — a time-consuming, inefficient use of talent.
We launched AI Voice Agents (like Jason & Carrie) that have been in a closed beta with a leading regional insurance carrier for time-sensitive and high volume claims intake processing (https://peakflo.co/industries/insurance). We are now doing a full public rollout where these AI agents will be able to:
✅ Make calls with prior consent at scale
✅ Receive calls 24/7 with instant pickups and TAT
✅ Access your datastores to give contextual answers
✅ Integrate with your CRM, ERP, and helpdesk tools
✅ Remember context from past conversations
✅ Take action and trigger workflows based on responses
✅ Evaluate interactions with AI scoring and improve over time
✅ Speak multiple languages and dialects
We’d love your thoughts, feedback, and ideas. And if you’ve got a use case you want to automate — drop it below, we’re all ears!
You can signup on the website and we will give you an account that you can use to build out your own voice enabled workflows: https://peakflo.co/ai-voice-agents
Report
@saurabh_chauhan6 From a QA standpoint, what I love most about Peakflo AI Voice Agents is their ability to maintain context and follow logic-based workflows without deviation. That’s a huge plus for ensuring quality and compliance in high-volume operations. Curious how the system handles edge cases or ambiguous responses, does it learn from human review cycles?
You're right that manual review of every call would be too cumbersome at scale.
We've built an LLM-as-a-judge system that automatically listens to and evaluates all calls against multiple quality metrics — things like goal completion, conversation flow, tone appropriateness, and adherence to guardrails. Metrics can be configured during onboarding.
This creates automated feedback loops that periodically optimize prompts and agent behavior based on real performance data. So the system continuously learns from edge cases and ambiguous responses without requiring constant human intervention.
For critical scenarios or anomalies flagged by the LLM-as-Judge, we have alerts and human review, but the LLM judge handles the heavy lifting of quality assurance at scale.
When our AI detects a situation requiring human intervention—such as a frustrated customer or a complex dispute—the handoff typically completes within 2–3 seconds.
This includes the time to transfer the conversation and notify the human agent. The AI model itself operates with 1–1.5 seconds of real-time latency end-to-end, ensuring a smooth transition without leaving customers waiting.
Our system is designed to maintain conversation context during the handoff, so the human agent is immediately up to speed and can continue seamlessly from where the AI left off.
@saurabh_chauhan6 Congrats on the launch! If you can pardon my ignorant questions... how are you most differentiated from other players like Eleven Labs and Cartesia?
@cksaywise Honestly this is a fantastic question. We provide industry and usecase specific workflows. Check out examples from the insurance industry here relating to FNOL, Policy renewals and servicing. These require integration with industry CMS/ERPs that we also support: https://peakflo.co/industries/insurance
@ekulianova Great question! A few features that set us apart:
1. Purpose-built for finance operations — We've been building finance integrations for years at Peakflo. Our AI agents leverage these battle-tested, best-in-class integrations with:
ERP & accounting systems (QuickBooks, Xero, NetSuite, SAP, etc.)
Payment infrastructure for both local and cross-border payments
Communications: SMS, WhatsApp, email, and phone, wechat, Line etc
2. 2-tier memory system: maintains context within a conversation AND remembers customer history across all interactions
3. Enterprise-grade AI infrastructure:
Low latency for natural, human-like conversations
Automated feedback loops with LLM-as-judge for continuous improvement
Private cloud and on-prem deploymens
4. True omnichannel presence — Your AI agent can seamlessly switch between phone calls, emails, WhatsApp, and SMS based on customer preference or workflow requirements, with full context preservation across channels.
5. Deep system access — Beyond APIs, we can interact with legacy systems via browser automation, meaning we work with your existing tools without requiring extensive technical integration.
But what is more important is that this allows us to solve problems that noone else can solve!
I'm the CTO at Peakflo, and I want to share something we've been building in stealth: our AI agents that act as a real person across all possible channels of communications.
The problem we had to solve
Imagine this: a customer texts you on Monday, calls on Wednesday, then emails on Friday. Most AI systems handle that like the protagonist from movie "Memento" waking up with no memory and every conversation starts from scratch.
For insurance and finance, this is a dealbreaker. When someone asks “What’s the status on that thing we discussed?”, the agent needs to know exactly what “that thing” was whether it was mentioned in a phone call last week or a WhatsApp message yesterday.
Our omni‑channel approach
We built Peakflo agents to work seamlessly across:
📞Phone calls: The core voice experience.
💬SMS: Quick updates and reminders.
🟢WhatsApp: Increasingly popular for business communication.
📧Email: Formal correspondence and document sharing.
🧑💻Web chat: For those who prefer typing.
🧑💼CRM: Hubspot, Salesforce, ...
✚ many other channels...
The hardest part is making the AI remember context across all of them.
The two‑tier memory architecture
Short‑term memory: Message history
For recent interactions (last few days/weeks), we use the actual conversation history. This gives us:
Verbatim recall: Exact phrasing and context.
Precision: Ability to reference specific details.
Immediacy: Fast access to recent conversations.
Think of this like working memory with immediate access to what just happened.
Long‑term memory: Summarization + vector search
For older interactions or high‑volume customers, storing everything becomes impractical and slow. So we:
Generate structured summaries after each interaction:
Key topics discussed
Action items and outcomes
Customer sentiment and preferences
Important dates, amounts.
Use vector embeddings for semantic search:
Convert summaries and key data into embeddings
Retrieve relevant context even when wording differs
Example: “payment issue” finds “billing problem” from 3 months ago
Why this matters in regulated industries
In insurance and finance, you can’t afford to:
Ask customers to repeat themselves
Lose track of commitments made in previous calls
Miss context that affects compliance or risk
Our approach means:
Compliance: Complete audit trail across all channels.
Consistency: Same information regardless of how customers reach us.
Efficiency: Agents don’t waste time catching up on history.
Trust: Customers feel heard and remembered.
Technical choices we made
Vector DB: We chose pg_vector because we love postgres and generally find it the fastest and easy to use given our stack.
Summarization: Special summarisation agent is being triggered after 1 hour from interaction finish, and produces schema‑constrained summaries immediately after each conversation.
Hybrid retrieval: We combine:
Exact keyword matching for policy numbers, amounts, and dates
Vector similarity for semantic understanding
Recency weighting so fresh interactions rank higher
When a customer reaches out on any channel, our agent:
Instantly retrieves short‑term message history.
Searches long‑term memory for relevant past interactions.
Synthesizes a complete picture before responding.
Updates memory after the conversation.
Happy to answer any technical questions about our implementation!
Report
How does Peakflo ensure the AI voice agents handle complex, sensitive financial conversations accurately and compliantly, especially in scenarios involving disputes or escalations?
Does the system remembers customer history from previous calls, including details like payment promises, follow-up requests, or concerns raised earlier? And does it work from where it left? Curious because I am frustrated when a CS call is dropped and I have to repeat it
As one of the engineers behind Peakflo AI Voice Agents, I’m super excited to see this go live today! 🎉 I got involved early during the POC phase and worked through to deployment, focusing mainly on reducing real-time latency, and building the connections between the AI agent layer and both our core system and external systems. Seeing it all come together into something that sounds so human and executes logic-driven workflows in real time has been incredibly rewarding. Huge shoutout to the entire team for pulling this off. Can’t wait to see how users build their own voice-enabled workflows with this! 🚀
Stoked for our team for launching this today and so excited to finally share it with the world!🌎
From my perspective in People & Culture, this is more than just a cool product—it's a fundamental shift. We're always talking about freeing up human talent from robotic work, and this is the tool that actually does it. It lets our teams focus on the creative, strategic problems that matter.
The feature that I think is the real game-changer is the ability to not just talk, but to access data stores and actually trigger actions in a CRM or ERP⚡️. That's what makes it a true agent, not just a voicebot.
Peakflo AI
Hey Product Hunt Community! 👋
I’m Saurabh, co-founder of Peakflo (YC W22), and I’m super excited to launch Peakflo AI Voice Agents!
During conversations with our clients, one challenge stands out across industries (whether thats in insurance, healthcare or even logistics): teams spend countless hours on calls, follow-ups, and manual system updates — a time-consuming, inefficient use of talent.
We launched AI Voice Agents (like Jason & Carrie) that have been in a closed beta with a leading regional insurance carrier for time-sensitive and high volume claims intake processing (https://peakflo.co/industries/insurance). We are now doing a full public rollout where these AI agents will be able to:
✅ Make calls with prior consent at scale
✅ Receive calls 24/7 with instant pickups and TAT
✅ Access your datastores to give contextual answers
✅ Integrate with your CRM, ERP, and helpdesk tools
✅ Remember context from past conversations
✅ Take action and trigger workflows based on responses
✅ Evaluate interactions with AI scoring and improve over time
✅ Speak multiple languages and dialects
We’d love your thoughts, feedback, and ideas. And if you’ve got a use case you want to automate — drop it below, we’re all ears!
You can signup on the website and we will give you an account that you can use to build out your own voice enabled workflows: https://peakflo.co/ai-voice-agents
@saurabh_chauhan6 From a QA standpoint, what I love most about Peakflo AI Voice Agents is their ability to maintain context and follow logic-based workflows without deviation. That’s a huge plus for ensuring quality and compliance in high-volume operations. Curious how the system handles edge cases or ambiguous responses, does it learn from human review cycles?
Peakflo AI
@saurabh_chauhan6 @ashish_arora8 Great question!
You're right that manual review of every call would be too cumbersome at scale.
We've built an LLM-as-a-judge system that automatically listens to and evaluates all calls against multiple quality metrics — things like goal completion, conversation flow, tone appropriateness, and adherence to guardrails. Metrics can be configured during onboarding.
This creates automated feedback loops that periodically optimize prompts and agent behavior based on real performance data. So the system continuously learns from edge cases and ambiguous responses without requiring constant human intervention.
For critical scenarios or anomalies flagged by the LLM-as-Judge, we have alerts and human review, but the LLM judge handles the heavy lifting of quality assurance at scale.
PicWish
@saurabh_chauhan6 how long is the typical latency when the AI needs to pull a live human into the loop due to a customer's tone or a complex dispute?
Peakflo AI
@saurabh_chauhan6 @mohsinproduct
Great question!
When our AI detects a situation requiring human intervention—such as a frustrated customer or a complex dispute—the handoff typically completes within 2–3 seconds.
This includes the time to transfer the conversation and notify the human agent. The AI model itself operates with 1–1.5 seconds of real-time latency end-to-end, ensuring a smooth transition without leaving customers waiting.
Our system is designed to maintain conversation context during the handoff, so the human agent is immediately up to speed and can continue seamlessly from where the AI left off.
Saywise
@saurabh_chauhan6 Congrats on the launch! If you can pardon my ignorant questions... how are you most differentiated from other players like Eleven Labs and Cartesia?
Peakflo AI
@cksaywise Honestly this is a fantastic question. We provide industry and usecase specific workflows. Check out examples from the insurance industry here relating to FNOL, Policy renewals and servicing. These require integration with industry CMS/ERPs that we also support: https://peakflo.co/industries/insurance
Saywise
@saurabh_chauhan6 Thanks for a detailed answer!
Yolk
Congrats with the launch! Curious: are there any features that make you stand out from the competitors?
Peakflo AI
@ekulianova Great question! A few features that set us apart:
1. Purpose-built for finance operations — We've been building finance integrations for years at Peakflo. Our AI agents leverage these battle-tested, best-in-class integrations with:
ERP & accounting systems (QuickBooks, Xero, NetSuite, SAP, etc.)
Payment infrastructure for both local and cross-border payments
Communications: SMS, WhatsApp, email, and phone, wechat, Line etc
2. 2-tier memory system: maintains context within a conversation AND remembers customer history across all interactions
3. Enterprise-grade AI infrastructure:
Low latency for natural, human-like conversations
Automated feedback loops with LLM-as-judge for continuous improvement
Private cloud and on-prem deploymens
4. True omnichannel presence — Your AI agent can seamlessly switch between phone calls, emails, WhatsApp, and SMS based on customer preference or workflow requirements, with full context preservation across channels.
5. Deep system access — Beyond APIs, we can interact with legacy systems via browser automation, meaning we work with your existing tools without requiring extensive technical integration.
But what is more important is that this allows us to solve problems that noone else can solve!
Peakflo AI
👋 Hey Product Hunt!
I'm the CTO at Peakflo, and I want to share something we've been building in stealth: our AI agents that act as a real person across all possible channels of communications.
The problem we had to solve
Imagine this: a customer texts you on Monday, calls on Wednesday, then emails on Friday. Most AI systems handle that like the protagonist from movie "Memento" waking up with no memory and every conversation starts from scratch.
For insurance and finance, this is a dealbreaker. When someone asks “What’s the status on that thing we discussed?”, the agent needs to know exactly what “that thing” was whether it was mentioned in a phone call last week or a WhatsApp message yesterday.
Our omni‑channel approach
We built Peakflo agents to work seamlessly across:
📞Phone calls: The core voice experience.
💬SMS: Quick updates and reminders.
🟢WhatsApp: Increasingly popular for business communication.
📧Email: Formal correspondence and document sharing.
🧑💻Web chat: For those who prefer typing.
🧑💼CRM: Hubspot, Salesforce, ...
✚ many other channels...
The hardest part is making the AI remember context across all of them.
The two‑tier memory architecture
Short‑term memory: Message history
For recent interactions (last few days/weeks), we use the actual conversation history. This gives us:
Verbatim recall: Exact phrasing and context.
Precision: Ability to reference specific details.
Immediacy: Fast access to recent conversations.
Think of this like working memory with immediate access to what just happened.
Long‑term memory: Summarization + vector search
For older interactions or high‑volume customers, storing everything becomes impractical and slow. So we:
Generate structured summaries after each interaction:
Key topics discussed
Action items and outcomes
Customer sentiment and preferences
Important dates, amounts.
Use vector embeddings for semantic search:
Convert summaries and key data into embeddings
Retrieve relevant context even when wording differs
Example: “payment issue” finds “billing problem” from 3 months ago
Why this matters in regulated industries
In insurance and finance, you can’t afford to:
Ask customers to repeat themselves
Lose track of commitments made in previous calls
Miss context that affects compliance or risk
Our approach means:
Compliance: Complete audit trail across all channels.
Consistency: Same information regardless of how customers reach us.
Efficiency: Agents don’t waste time catching up on history.
Trust: Customers feel heard and remembered.
Technical choices we made
Vector DB: We chose pg_vector because we love postgres and generally find it the fastest and easy to use given our stack.
Summarization: Special summarisation agent is being triggered after 1 hour from interaction finish, and produces schema‑constrained summaries immediately after each conversation.
Hybrid retrieval: We combine:
Exact keyword matching for policy numbers, amounts, and dates
Vector similarity for semantic understanding
Recency weighting so fresh interactions rank higher
When a customer reaches out on any channel, our agent:
Instantly retrieves short‑term message history.
Searches long‑term memory for relevant past interactions.
Synthesizes a complete picture before responding.
Updates memory after the conversation.
Happy to answer any technical questions about our implementation!
How does Peakflo ensure the AI voice agents handle complex, sensitive financial conversations accurately and compliantly, especially in scenarios involving disputes or escalations?
Does the system remembers customer history from previous calls, including details like payment promises, follow-up requests, or concerns raised earlier? And does it work from where it left? Curious because I am frustrated when a CS call is dropped and I have to repeat it
Peakflo AI
As one of the engineers behind Peakflo AI Voice Agents, I’m super excited to see this go live today! 🎉
I got involved early during the POC phase and worked through to deployment, focusing mainly on reducing real-time latency, and building the connections between the AI agent layer and both our core system and external systems. Seeing it all come together into something that sounds so human and executes logic-driven workflows in real time has been incredibly rewarding.
Huge shoutout to the entire team for pulling this off. Can’t wait to see how users build their own voice-enabled workflows with this! 🚀
Peakflo AI
@nabid LFG!!! 🚀🚀🚀
A super cool way to deal with hassling tasks!
Stoked for our team for launching this today and so excited to finally share it with the world!🌎
From my perspective in People & Culture, this is more than just a cool product—it's a fundamental shift. We're always talking about freeing up human talent from robotic work, and this is the tool that actually does it. It lets our teams focus on the creative, strategic problems that matter.
The feature that I think is the real game-changer is the ability to not just talk, but to access data stores and actually trigger actions in a CRM or ERP⚡️. That's what makes it a true agent, not just a voicebot.
Let's gooo! 🚀
Peakflo AI
@mrinalbhatt Game-changer!
Middleware
AI voice agent is the hottest topic around the world. Congrats @saurabh_chauhan6 and team on launching Peakflow AI voice agents.
Peakflo AI
@sawarams It is indeed one of the most talked about topics as it touches the service economy directly! Thanks for your support Sam!