START

Scientific discovery shouldn't slow down because of software.

But every instrument in the lab ships its own, and someone has to learn all of them.

Researchers don't want to memorize instrument interfaces.

They want experiments to run.
They want answers.
They want data.

That's the problem Infera is solving.

Chloe Sow knows that side of it. Mechanical engineering at Harvard, research at Brigham and Women's, building medical devices and running the experiments by hand.

Her co-founder Troy Cheng spent an entire research summer figuring out how to run his lab's experiments on one machine. That was the summer.

So they built the thing they needed.

Describe an experiment in plain English.
Infera turns it into a validated, instrument-ready run across the equipment your lab already uses.

One system for protocol logic.
Vendor-specific scripts.
Laboratory data.
Inventory.
Institutional knowledge.

Today, running an experiment means going instrument by instrument.
Tomorrow, one place for all of them.

AI agents already design experiments and protocols.

But nothing connects those designs to real laboratory instruments.

One system.
From intent to execution.‍

Infera: "Control lab instruments with natural language"

🎙️ Chloe Sow, Co-Founder, Infera on Fondo START pod

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00:45 Claude Code for scientific instruments
01:27 Troy's entire job was one liquid handler
02:39 Every run captures a record. The missing layer between AI and instruments
03:12 Scientists pressing buttons without knowing the machine
04:22 Plain English to validated instrument-ready run. Academic labs and cores first
05:50 Every instrument ships its own closed software
06:02 Meeting at a virtual Caltech admit session
07:19 Applied to YC for feedback. Got in first try
08:35 Staying disciplined instead of chasing the SF event circuit

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Check out infera.bio

You can track a burrito from the restaurant to your door in real time

You donate to a family in a crisis zone. Usually you get a thank-you email. That's where it ends.

Nasrat Khalid couldn't understand why.

We expect complete visibility when we order dinner.
Why not when we're helping another human being?

So he built Aseel. 
A platform for "everything do good"

You buy a food package. 
You see exactly who receives it. 
You can ask for a photo of the delivery.
You can exchange messages with the recipients, if they choose to participate.

Instead of donating into a system and hoping, you know the aid reached the person who needed it.

That's the whole idea. No plane ticket. No project. No abstraction.

"Award-winning US-based platform connecting artisans to global markets and enabling transparent aid"

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🎙️ Nasrat Khalid, Founder & CEO, Aseel on Fondo START

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00:45  The platform for everything do good
02:25  The first 16 years, as a refugee
02:55  Why he left institutional aid to build Aseel
03:40  Leaving Afghanistan at 16 days old
04:05  No ID means no school, no bank account
04:35  Why identity comes before food
05:15  Seven years inside the World Bank
05:58  What happened to aid in Afghanistan after 2021
07:00  AidOS, for institutions
07:40  The Uber Eats insight
08:20  The $30 package you can trace to a family
09:35  Why he joined the residency
12:30  How to get started

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Check out aseelapp.com

AI changed how software gets built.

The tools we use after we ship haven't caught up.

Code is getting cheaper.

Production is getting harder.

That's the gap Sherwood Callaway saw after building with Cursor and Claude Code. AI had transformed how software gets built, but when something broke in production, he was back clicking through observability tools that hadn't kept pace.

Sazabi is built for AI-native engineering teams.

→ Autonomous alerts with root-cause context
→ Conversational debugging instead of endless dashboards
→ AI that learns your codebase, architecture, and past incidents

AI is making it easier than ever to build software.

Sazabi makes it just as easy to understand what's happening in production—helping engineering teams detect issues earlier, identify the root cause faster, and get back to shipping.

Observability is becoming one of the most important layers of the AI software stack.

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🎙️ Sherwood Callaway, Founder & CEO, Sazabi on Fondo START pod

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01:08 — Building AI-native observability for fast-moving engineering teams
01:45 — Why AI made observability a much bigger problem
02:07 — "Code is basically free"
02:36 — Why architecture and product thinking are the new bottlenecks
03:09 — "Measure twice, cut once" — how Sazabi stays focused
03:35 — History major to Dev Bootcamp: turning down investment banking
05:50 — Joining Brex at employee 70 through hypergrowth to $12B
07:10 — Starting Brex's observability team — the seed of Sazabi
07:48 — What observability actually is (it started with 1960s rocket science)
09:45 — "I will leave Brex today" — the Dalton Caldwell moment
12:16 — Building one of the first production AI agents at 11x
13:33 — The Cursor vs. Datadog contrast that sparked Sazabi

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Check out www.sazabi.com

Your product didn't fail.
No one saw it.

The best product often loses
Not because it's worse
Because no one saw it

Teams ship daily

Marketing got faster too

It just never stopped needing you

Open the recorder.
Fumble the demo.
Redo it fifteen times.

Edit
Design
Post

Your flow is gone.

So next time you don't bother

That's how good products disappear

Cyrus Kelly and co-founder David McDonough were involved in 11 startups.

Every time, they became the marketing team

Never once enjoyed it

So they built tday

Connect your GitHub repo

Or point it at your product

Every release becomes a demo video of the actual feature.

On-brand graphics

Published to your channels

Ship an update and the demo updates itself

Because it's tied to what your product does

Not a one-time recording.

Not three hours in tday instead of four hours in Canva.

Minutes.

Then never thinking about it again (unless you want to)

The problem was never building

It was that no one saw it

Their mission is to make it so the best products do win.

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🎙️ Cyrus Kelly, Co-Founder & CEO, Tday on Fondo START pod

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00:20 — What tday does: marketing on autopilot for software companies
00:50 — Origin story: three engineers, 11 startups, always stuck doing marketing
01:35 — Why the best product doesn't always win
01:50 — Starting university at 13
02:15 — Building a student club platform with banking at 16
03:05 — The tech stack behind it
03:50 — Applying to YC four times
04:05 — Interviewing with Tom Blomfield
04:55 — How the product changed during the batch
05:35 — Not a better design tool: two minutes instead of hours
06:00 — Why repeated launches matter
06:30 — The idea of a product that records itself
06:50 — Who tday works best for
07:45 — Getting the domain
08:00 — What tday.com actually cost
08:45 — Eight months of waiting, and how the domain shaped the product
10:00 — Sam Altman offering $2 million in tokens
11:15 — Token maxing: agents running in parallel
12:20 — Where to find tday and Cyrus

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Check out tday.com

A phone call used to mean trust.

Now it could be AI.

DetectifAI is building the next layer of cybersecurity for the AI era

Not another chatbot

Not another AI wrapper

Foundational models built to detect audio deepfakes

It started with something personal.

Tarini's grandfather lost thousands of dollars to a simple deepfake.

That moment became the reason to build.

Tarini had already been in machine learning since grade six

Nearly a decade studying how AI works

She saw where the technology was headed - and what was missing

Fraud has existed for a very long time

Cybersecurity has existed for a very long time

Deepfake detection is that same discipline extended into the AI world.

Today, DetectifAI serves clients across banking, finance, security, telecoms, and OEMs

An API that integrates into existing enterprise infrastructure

And on mobile, it goes even deeper - into the OEM layer itself.

Which makes the vision tangible.

You answer a call.

Your phone gives you a rolling update on whether that voice is likely AI or a real human

Detection during the call.

Not after the money is gone.

The scale comes from where it's deployed.

A single OEM integration can unlock protection for millions of people.

And deepfake detection is only the first step.

The larger vision is an anti-fraud landscape that helps people navigate scam calls, robocalls, and anything that falls under the purview of fraud.

The next generation of AI companies won't just build intelligence

They'll build trust

🎙️ Tarini Padmanabhuni, CEO, DetectifAI on Fondo START pod

00:57 Founder origin story
01:18 ML background
01:42 Audio deepfake detection
01:53 Enterprise customers
02:15 Growing AI fraud
02:39 AI cybersecurity
03:16 Enterprise integration
03:29 Telecom and OEM strategy
03:46 OEM distribution vision
04:13 Real-time call detection
04:33 Broader anti-fraud vision
04:58 AWS grant journey
05:31 Building in San Francisco
05:52 Residency experience
06:15 Advice for founders

Check out detectif.ai

100,000 users sounds like a dream

Until you're personally answering 50+ support tickets a day...

That was Dawson Chen.

He'd scaled Martin, an AI personal assistant, to more than 100,000 users, but growth came with a new problem: 

Refund requests

Subscription cancellations

Login issues

And the same support questions over and over again...

To keep up, the team hired a virtual assistant and relied on tools like Zendesk and Freshdesk. 

But every ticket meant jumping between their inbox, Stripe, their database, and their knowledge base. 

The software wasn't built for how startups actually operate.

So instead of accepting it, they built the tool they wished they'd had.

Then shared it with a few founder friends.

The response was immediate.

That internal tool became Letterbook.

Today, Letterbook is the AI-native alternative to Zendesk, Freshdesk, Zoho Desk, and Intercom - built specifically for startups.

It connects to your inbox, Stripe, database, and knowledge base, then drafts a complete resolution for every support ticket: the reply, the refund, the cancellation, the next action.

For many customers, 70–80% of tickets become a single approval instead of minutes of manual work.

Their Product Hunt launch reached #3 Product of the Day.

Most customer support platforms were built for enterprise support teams.

Letterbook was built for founders.

"The modern AI-native support platform"

🎙️ Dawson Chen, CEO & Founder, Letterbook on Fondo START pod

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00:32 — From a 100,000-user AI app to discovering a support bottleneck

01:08 — Turning an internal tool into Letterbook

02:09 — Why AI-native support beats legacy help desks

02:47 — How context-aware AI drafts actions, not just replies

03:23 — Automating 70–80% of repetitive support work

04:20 — Why Letterbook is purpose-built for B2C startups

04:55 — Two YC founders, two co-founder breakups, one new company

06:42 — Building the default customer support platform for startups

08:08 — Lessons from launching to #3 on Product Hunt

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Check out www.letterbook.ai

As a founder, Emma Lawler felt like SOC 2 was a major distraction from building.

But when she started selling to enterprise logos, it became something that was impossible to ignore.

Now, as Product Lead at Rippling, she channeled her first-hand experience of getting SOC 2 compliant to rethink how all automated compliance frameworks are run. 

It started with a retro. 

Every other SOC 2 vendor works the same way: 

Detect a problem
Alert your team
Fix it...somewhere else

The problem with this checklist is that "somewhere else" requires dozens of additional integrations. Because these vendors are just reporting layers on top of your stack.

Rippling is your stack. 

Which means most of your compliance evidence is collected before you start AND when there's a gap in compliance, Rippling fixes it.

Unencrypted device --> encrypt it.

Wrong app access --> de-provision automatically

Incomplete security training --> gate system access until its done. 

Because compliance really isn't about the report. It's about operating your company securely -- and proving it without a yearly fire drill. 

Emma shares more about how her own founder journey influenced building Rippling's powerful new automated compliance product, available today for SOC 2 Type 1 and 2. 

🎙️ Emma Lawler, Product Lead, Rippling on Fondo START pod

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00:57 Introducing Rippling Automated Compliance
02:07 The evolution of compliance software
03:02 AI's role in compliance workflows
03:41 Understanding SOC 2 requirements
04:20 Compliance and enterprise procurement
05:03 Building secure operating practices
05:46 Product development at Rippling
06:28 Building with a lean team
07:15 Lessons from founding startups
08:55 Learning through product pivots
11:01 AI in modern product development
12:41 Planning and research with AI
16:05 Defining product-market fit
17:56 Using first-party data for compliance
21:35 Advice for early-stage founders

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Check out www.rippling.com/products/it/automated-compliance

96% of users aren't saying no to your product.
They're saying no to your paywall.

And the only tool most teams have for that is manual A/B tests.

Thousands of dollars per experiment. Weeks to trust one result. One variable at a time. Every test starts from zero.

Stop guessing. Start compounding.

Tranzmit AI builds, tests, and evolves paywalls instead.

Analyze the behavioral data, generate variants, score them against simulation and conversion history, ship the winner with guardrails and auto-rollback.

50x faster than manual A/B.

25M paywall views a month feeding the loop. Every result sharpens the next one.

Four months ago, Agaaz Singhal was pointing the same engine at the opposite end of the funnel.

We recorded this episode in March, when Tranzmit sat on top of the cancel button:

Detected at-risk customers before they cancelled
Intervened with personalized conversations
Identified the root cause of frustration
Filed live Jira tickets for engineering
Recovered customers who were ready to leave

Five lines of code. Three-minute setup.
Not just to stop churn. To understand it.

Read the behavioral data, generate the intervention, measure it, loop.
Same engine. The screen changed.

In the episode Agaaz describes product teams flying blind for weeks, stitching together analytics dashboards while customers walked. Behavioral data, an intervention at the moment that decides revenue, measured and fed back.

Same loop, pointed at the front door. Running 24/7. "Building self-improving software infrastructure"

🎙️ Agaaz Singhal, Founder, Tranzmit AI on Fondo START pod

00:26 Building AI product teams to reduce churn on consumer platforms
00:42 AI-native cancel buttons that understand why customers leave
01:11 Five lines of code. Three-minute implementation.
01:25 Predicting churn before users hit cancel
02:05 Recovering up to 28% of customers at the point of cancellation
02:35 Why product teams spend weeks trying to understand churn
03:20 Scaling a consumer platform to 700K monthly active users
03:35 The insight that led to building Tranzmit AI
03:50 Building in San Francisco at Founders Inc

Check out tranzmitai.com

Right now most AI agents interact with software the way humans do

They open a browser, look at a screenshot, and try to figure out what to click

That's like ripping the LIDAR off a Waymo and handing it a camera

Tarun & Alex kept running into this problem. 

They were building an AI receptionist for dentists, and at conference after conference people asked the same thing: do you integrate with X or Y platform?

Seven other people were doing the same thing. None of them could integrate. Alex and Tarun could, because they knew how to reverse engineer network requests

That's when they stopped digging for gold and started selling shovels

Zatanna turns legacy software into APIs by working at the network layer. No browsers. No screenshots. Just requests

10x faster on the workflows they benchmarked. Near-zero browser costs. Reliability goes from 70% to nearly 100%.

Computers shouldn't navigate the web like humans do. Humans use screens. Computers use requests

"No browser bots to babysit"

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🎙️ Tarun Vedula & Alex Blackwell, Co-founders, Zatanna on Fondo START

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00:57 From jury research to API infrastructure

02:04 The "sell the shovels" realization

03:18 Why browser agents break at scale

04:42 Turning legacy systems into APIs

05:47 Why repetitive workflows shouldn't use browsers

06:37 Teaching AI agents to navigate like computers

08:34 Founder-market fit and knowing when to pivot

10:20 From sneaker bots to reverse engineering anti-bots

12:23 The future of AI vs anti-bot warfare

13:37 Why companies are still stuck at 70% reliability

Check out www.zatanna.ai to learn more. 

Before AI, marketing teams built five creatives for every campaign.

Now they can generate fifty.

But more content didn't solve marketing.

It exposed a new bottleneck.

Most of it is slop.

AI solved the quantity problem.

It created a quality problem.

Kyle Wong and co-founder Colin Tseung built InstaAgent to solve the second one.

A consumer brand hands InstaAgent a single marketing brief. From there, the team handles the strategy, the creative, the distribution, and the analytics.

The result isn't 50 generic ads.

It's personalized content built for hundreds of distinct audiences - each with its own messaging, creative, and targeting.

Think about a global CPG company.

One hundred SKUs.

Ten countries.

That's a thousand creative variations before you even start optimizing performance.

The hard part isn't generating content anymore.

It's knowing what to create.

Anyone can pay $25 a month for an AI model.

Very few know how to turn that model into marketing that actually performs.

That's why InstaAgent isn't positioning itself as another self-serve AI tool.

It's an AI-native marketing partner - combining human judgment with AI execution to produce creative that brands can actually scale.

The market is responding.

From $0 to $1M ARR in just 10 months.

Enterprise customers including Nestlé and P&G.

Enterprise sales cycles closing in less than a month.

The first wave of AI made content abundant.

The next wave will reward the companies that know exactly who that content is for.

🎙️ Kyle Wong, Co-Founder & CEO, InstaAgent on Fondo START pod

00:15 — What InstaAgent actually does: scaling one campaign across hundreds of personas
00:40 — The full loop from a single brief: strategy, creation, distribution, analytics
00:55 — Why the bet is on TikTok and Meta, where AI video is moving fastest
01:20 — The old way: five creatives per campaign, slow and expensive
01:39 — How AI multiplied output to 50 — and why most of it misses
02:06 — Moving upmarket from SMBs to enterprise names like Nestlé and P&G
02:30 — The CPG math that makes manual creative impossible: 100 SKUs, 10 countries
03:20 — Replacing the Canva-and-Photoshop resizing grind
03:44 — Why an agency model beats self-serve SaaS for AI creative
04:45 — On token-maxing and the tokens offer from Sam Altman at YC
05:47 — From eight years at Goldman to a game that hit 1M users in a month
07:20 — How a hot sauce brand used synthetic personas to broaden its audience

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Check out instaagent.com