Building an agentic AI helpdesk from zero
Building an agentic AI helpdesk from zero
How design insights turned a DevOps platform into a customer support AI agent that drove $780K in ARR in 14 months.
Impact
$780K
ARR in 14 months,
from zero
780K
14 months
90%
human approval rate
on AI drafted emails
90%
Approval rate
60%
less agent workload
with QueryPal
80%
tickets resolved
at first contact
80%
tckts resolved
Role
Founding Designer
Product Design Lead
Team
CEO, VP of Marketing, LLM Engineers
Product Design Lead
Timeline
2024 - 2025, 14 months
2018 - 9 months
Outcome
$780K ARR
AI Cloud Software
QueryPal / O > 1 AI Startup
QueryPal / O > 1 AI Startup
My role
I WAS THE FOUNDING AND ONLY DESIGNER
I owned every design surface the company shipped: product UX, bot configuration UX, admin and analytics dashboard, brand identity, the GTM marketing site, and the prompt and eval system governing agent behavior.
I architected the admin object model from the ground up, designed the three surface component system shared across Slack, email, and the admin dashboard, and worked with the LLM engineers on the tone constraints governing every customer facing draft. No PM, no researcher, no second designer.
How we got here (Story arc)
TWO YEARS OF BUILDING THE WRONG PRODUCT TAUGHT US THE MARKET
QueryPal started as an observability platform designed to help SRE teams make sense of metrics, logs, and traces. But as we introduced AI, we saw a fundamental shift: people no longer needed another dashboard to interpret their data. They wanted to ask a question and get an answer.
That insight changed our direction.
Support teams had the same problem: hours spent answering repetitive questions that could be resolved from information already sitting across the organization. Unlike SRE teams, they had a clear business case for automation and a budget line to pay for it.
The product evolved from observability → AI-powered answers → customer support automation.
ACT 1
DevOps Observability
I designed an RCA tool unifying metrics, logs, and events into one interface. As the models matured, the dashboard started competing with the intelligence users actually wanted. The lesson: visualization wasn’t the product. Fast answers were.
Unified metrics, logs, and events into a single RCA interface.
The lesson: Users didn't want visual dashboards—they wanted fast answers.
ACT 2
QueryPal Slackbot
We shifted from dashboards to native chat. I designed QueryPal’s Slackbot so teams could resolve issues where they already worked, with no tab switching. The catch: high usage didn't mean high willingness to pay.
Brought issue resolution directly into native Slack workflows. The catch: High daily usage didn’t turn into paying customers.
ACT 3
AI Email Drafts for CX
We pivoted into customer support, generating on-brand email drafts from existing company knowledge. Agents reviewed and resolved tickets in minutes instead of hours.
The breakthrough: clear ROI, a clear buyer, and product-market fit.
Turned internal knowledge into instant, on-brand support replies.
The win: Clear buyer, measurable ROI, and true PMF.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
Object mapping
Mapped the admin model before drawing a screen
Card sorting set the object structure first: accounts, integrations, automations, billing. Screens followed the model.
Admin dashboard - Onboarding
Three steps to a bot that works.
Connect a source, invite the team, set the tone. Three tasks, one screen, progress visible.
Design System
One component set across Slack, email, and admin.
Integration pickers, tone controls, and approval states built as shared primitives. Three surfaces, one vocabulary, no redraws.
Analytics Dashboard
MAKING THE AGENT'S VALUE VISIBLE
Support leaders do not renew on ticket counts. They renew on proof the agent is absorbing work their team would otherwise do.
Outcomes
QUERYPAL WENT FROM CONCEPT TO PAID PILOTS IN 14 MONTHS
$780K
Grew from 0 to $780K ARR with design embedded across product, brand, and GTM
90%
human approval on AI drafted replies across seven pilot accounts.
60%
reduction in tier 1 support workload, freeing agents for cases that needed a human.
780K
14 months
90%
Approval rate
80%
tckts resolved
Testimonials
WHAT THE PILOT TEAMS SAID
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits
EXPLORE MORE
Building an agentic AI helpdesk from zero
Building an agentic AI helpdesk from zero
How design insights turned a DevOps platform into a customer support AI agent that drove $780K in ARR in 14 months.
Impact
$780K
ARR in 14 months,
from zero
780K
14 months
90%
human approval rate
on AI drafted emails
90%
Approval rate
60%
less agent workload
with QueryPal
80%
tickets resolved
at first contact
80%
tckts resolved
Role
Founding Designer
Product Design Lead
Team
CEO, VP of Marketing, LLM Engineers
Product Design Lead
Timeline
2024 - 2025, 14 months
2018 - 9 months
Outcome
$780K ARR
AI Cloud Software
QueryPal / O > 1 AI Startup
QueryPal / O > 1 AI Startup
My role
I WAS THE FOUNDING AND ONLY DESIGNER
I owned every design surface the company shipped: product UX, bot configuration UX, admin and analytics dashboard, brand identity, the GTM marketing site, and the prompt and eval system governing agent behavior.
I architected the admin object model from the ground up, designed the three surface component system shared across Slack, email, and the admin dashboard, and worked with the LLM engineers on the tone constraints governing every customer facing draft. No PM, no researcher, no second designer.
How we got here (Story arc)
TWO YEARS OF BUILDING THE WRONG PRODUCT TAUGHT US THE MARKET
QueryPal started as an observability platform designed to help SRE teams make sense of metrics, logs, and traces. But as we introduced AI, we saw a fundamental shift: people no longer needed another dashboard to interpret their data. They wanted to ask a question and get an answer.
That insight changed our direction.
Support teams had the same problem: hours spent answering repetitive questions that could be resolved from information already sitting across the organization. Unlike SRE teams, they had a clear business case for automation and a budget line to pay for it.
The product evolved from observability → AI-powered answers → customer support automation.
ACT 1
DevOps Observability
I designed an RCA tool unifying metrics, logs, and events into one interface. As the models matured, the dashboard started competing with the intelligence users actually wanted. The lesson: visualization wasn’t the product. Fast answers were.
Unified metrics, logs, and events into a single RCA interface.
The lesson: Users didn't want visual dashboards—they wanted fast answers.
ACT 2
QueryPal Slackbot
We shifted from dashboards to native chat. I designed QueryPal’s Slackbot so teams could resolve issues where they already worked, with no tab switching. The catch: high usage didn't mean high willingness to pay.
Brought issue resolution directly into native Slack workflows. The catch: High daily usage didn’t turn into paying customers.
ACT 3
AI Email Drafts for CX
We pivoted into customer support, generating on-brand email drafts from existing company knowledge. Agents reviewed and resolved tickets in minutes instead of hours.
The breakthrough: clear ROI, a clear buyer, and product-market fit.
Turned internal knowledge into instant, on-brand support replies.
The win: Clear buyer, measurable ROI, and true PMF.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
Object mapping
Mapped the admin model before drawing a screen
Card sorting set the object structure first: accounts, integrations, automations, billing. Screens followed the model.
Admin dashboard - Onboarding
Three steps to a bot that works.
Connect a source, invite the team, set the tone. Three tasks, one screen, progress visible.
Design System
One component set across Slack, email, and admin.
Integration pickers, tone controls, and approval states built as shared primitives. Three surfaces, one vocabulary, no redraws.
Analytics Dashboard
MAKING THE AGENT'S VALUE VISIBLE
Support leaders do not renew on ticket counts. They renew on proof the agent is absorbing work their team would otherwise do.
Outcomes
QUERYPAL WENT FROM CONCEPT TO PAID PILOTS IN 14 MONTHS
$780K
Grew from 0 to $780K ARR with design embedded across product, brand, and GTM
90%
human approval on AI drafted replies across seven pilot accounts.
60%
reduction in tier 1 support workload, freeing agents for cases that needed a human.
780K
14 months
90%
Approval rate
80%
tckts resolved
Testimonials
WHAT THE PILOT TEAMS SAID
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits
EXPLORE MORE
Building an agentic AI helpdesk from zero
Building an agentic AI helpdesk from zero
How design insights turned a DevOps platform into a customer support AI agent that drove $780K in ARR in 14 months.
Impact
$780K
ARR in 14 months,
from zero
780K
14 months
90%
human approval rate
on AI drafted emails
90%
Approval rate
60%
less agent workload
with QueryPal
80%
tickets resolved
at first contact
80%
tckts resolved
Role
Founding Designer
Product Design Lead
Team
CEO, VP of Marketing, LLM Engineers
Product Design Lead
Timeline
2024 - 2025, 14 months
2018 - 9 months
Outcome
$780K ARR
AI Cloud Software
QueryPal / O > 1 AI Startup
QueryPal / O > 1 AI Startup
My role
I WAS THE FOUNDING AND ONLY DESIGNER
I owned every design surface the company shipped: product UX, bot configuration UX, admin and analytics dashboard, brand identity, the GTM marketing site, and the prompt and eval system governing agent behavior.
I architected the admin object model from the ground up, designed the three surface component system shared across Slack, email, and the admin dashboard, and worked with the LLM engineers on the tone constraints governing every customer facing draft. No PM, no researcher, no second designer.
How we got here (Story arc)
TWO YEARS OF BUILDING THE WRONG PRODUCT TAUGHT US THE MARKET
QueryPal started as an observability platform designed to help SRE teams make sense of metrics, logs, and traces. But as we introduced AI, we saw a fundamental shift: people no longer needed another dashboard to interpret their data. They wanted to ask a question and get an answer.
That insight changed our direction.
Support teams had the same problem: hours spent answering repetitive questions that could be resolved from information already sitting across the organization. Unlike SRE teams, they had a clear business case for automation and a budget line to pay for it.
The product evolved from observability → AI-powered answers → customer support automation.
ACT 1
DevOps Observability
I designed an RCA tool unifying metrics, logs, and events into one interface. As the models matured, the dashboard started competing with the intelligence users actually wanted. The lesson: visualization wasn’t the product. Fast answers were.
Unified metrics, logs, and events into a single RCA interface.
The lesson: Users didn't want visual dashboards—they wanted fast answers.
ACT 2
QueryPal Slackbot
We shifted from dashboards to native chat. I designed QueryPal’s Slackbot so teams could resolve issues where they already worked, with no tab switching. The catch: high usage didn't mean high willingness to pay.
Brought issue resolution directly into native Slack workflows. The catch: High daily usage didn’t turn into paying customers.
ACT 3
AI Email Drafts for CX
We pivoted into customer support, generating on-brand email drafts from existing company knowledge. Agents reviewed and resolved tickets in minutes instead of hours.
The breakthrough: clear ROI, a clear buyer, and product-market fit.
Turned internal knowledge into instant, on-brand support replies.
The win: Clear buyer, measurable ROI, and true PMF.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
ACT 1: Devops observability
VIEW THE BEFORE DASHBOARD
I designed the RCA dashboard with a senior DevOps engineer: metrics, logs and events in one view.
ACT 2
We didn’t pivot to an AI Slackbot by accident. CtrlStack showed us that dashboards explained problems, but decisions happened in Slack.
ACT 2
I led GTM with our VP of Marketing. Ads, Whitepapers, and test marketing sites to check which ICPs signed up the most.
ACT 3
The Slackbot validated the behavior, but testing interest across marketing personas clarified the buyer. CX leaders responded the strongest.
Object mapping
Mapped the admin model before drawing a screen
Card sorting set the object structure first: accounts, integrations, automations, billing. Screens followed the model.
Admin dashboard - Onboarding
Three steps to a bot that works.
Connect a source, invite the team, set the tone. Three tasks, one screen, progress visible.
Design System
One component set across Slack, email, and admin.
Integration pickers, tone controls, and approval states built as shared primitives. Three surfaces, one vocabulary, no redraws.
Analytics Dashboard
MAKING THE AGENT'S VALUE VISIBLE
Support leaders do not renew on ticket counts. They renew on proof the agent is absorbing work their team would otherwise do.
Outcomes
QUERYPAL WENT FROM CONCEPT TO PAID PILOTS IN 14 MONTHS
$780K
Grew from 0 to $780K ARR with design embedded across product, brand, and GTM
90%
human approval on AI drafted replies across seven pilot accounts.
60%
reduction in tier 1 support workload, freeing agents for cases that needed a human.
780K
14 months
90%
Approval rate
80%
tckts resolved
Testimonials
WHAT THE PILOT TEAMS SAID
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits
"We completely removed Freddy AI in the first week after implementing QueryPal."
- Customer Support Lead, JetBrains
"Our agents rely on QueryPal daily & we’ve never handled peak season this smoothly"
- Operations Lead, SimplyBenefits