How Instalily Hit $1M ARR in Months by Building AI That Actually Does the Work

When Amit Shah left his role as president of 1-800-Flowers after eleven years of scaling the company from $500 million to over $2 billion, he wasn't looking for another comfortable executive position. He had spent two decades watching talented teams trapped in what he calls "dumb systems" — clicking through Salesforce entries, extracting ledger data, answering repetitive emails while the actual customer impact of their work kept shrinking. The insight that would become Instalily wasn't born in a Silicon Valley co-working space. It came from years in the operational trenches of distribution-heavy businesses, where half of the global GDP remains ninety percent manual.

Key Takeaways: Instalily's Path to Enterprise AI Product-Market Fit

• Instalily raised $25 million in Series A funding led by Insight Partners, with participation from Perceptive Ventures and Marvin Ventures
• The company achieved first million in ARR within months and is confidently past triple-triple growth in year two
• InstaWorkers deliver $150+ million in annualized growth impact for individual customers
• Unlike AI copilots that suggest, Instalily's agents execute complete workflows inside ERPs, CRMs and legacy systems
• The company focuses exclusively on vertical-specific AI rather than horizontal solutions, believing "one size fits none"
• Industries like physical goods manufacturing, insurance and healthcare services have struggled with AI because they depend on specialized knowledge, large catalogs and fragmented tools

Table of Contents

1. The Operator Market Fit Philosophy
2. From Systems of Record to Systems of Action
3. Why Spring 2023 Was the Perfect Time
4. The Three-Layer Problem Stack
5. Building InstaBrain and InstaWorkers
6. Landing the First Enterprise Customers
7. The GTM Strategy That Scales
8. Measuring Success Beyond ARR
9. The AI Native Talent Strategy
10. What True Product-Market Fit Looks Like

The Operator Market Fit Philosophy

Most founders obsess over product-market fit. Shah reframes the concept entirely: he calls it "operator market fit." The question isn't whether customers will buy your product. It's whether you understand the operational complexity well enough to solve problems others can't even see.

Shah's background gave him this operational lens. After starting at McKinsey, he spent years in roles that required managing deep distribution, supply chain, and logistics complexity. At Row Flowers, 1-800-Flowers, and Blue Apron (where he served as a board director), the pattern was identical: smart, motivated people spending their days as "keepers of systems of record" rather than value creators.

The vision emerged at the intersection of two forces. First, the realization that systems like Salesforce and SAP, designed to empower workers, had actually increased the distance between operators and customers. Second, the breakthrough capabilities of large language models in early 2023 that could potentially create "atomic units of work" through AI agents.

From Systems of Record to Systems of Action

Shah describes a three-layer stack that defines how work happens in complex businesses. At the bottom sits the data layer — ERPs, CRMs, emails, texts, wherever information lives. The middle layer contains software systems of record that were supposed to make life easier. At the top sits the cognitive layer: human beings trying to extract value from it all.

"The promise of software was always that it would make the distance between the cognitive layer and the data easier," Shah explains. "But where did we end up? It started to make our lives more and more complex."

Workers became database administrators rather than strategic thinkers. Sales teams spent hours entering data for management dashboards instead of building customer relationships. Service technicians wasted time downloading equipment manuals instead of fixing broken machinery.

The clarifying question became: if work is really about taking actions, why are we building systems of record instead of systems of action?

Why Spring 2023 Was the Perfect Time

Shah and co-founder Sumantro Das launched Instalily in spring 2023, just as the post-ChatGPT world was beginning to understand what AI might actually do. The timing was both fortuitous and strategic.

Early reactions were skeptical. When Shah pitched the vision of AI agents executing multi-step workflows, people assumed he was talking about automating phone calls for his old flower delivery business. The technology was infant enough that hallucinations were common, and the idea of putting AI agents into production seemed fanciful.

But Shah and Das, who had worked together for twelve years at 1-800-Flowers, saw early research papers suggesting multi-agent architectures could work. They spent the first six to eight months building core technology, focusing on making multi-agent, multi-step workflows reliable enough for enterprise deployment.

The key insight: they weren't trying to replace human work. They were trying to amplify it. "Our belief always has been that we want to empower human beings and human teams to do the most demanding cognitive work, and amplify that impact using AI technologies," Shah says.

The Three-Layer Problem Stack

Instalily's differentiation starts with understanding what makes knowledge work in distribution-heavy industries so resistant to automation. The answer isn't just technical — it's architectural.

Consider a salesperson at a commercial equipment distributor. Success isn't about automating CRM entries, though Instalily does that. It's about discerning signal from noise across hundreds of customer interactions, identifying the most prescient action at any moment, and measuring whether that action improved outcomes versus random selection.

Nobody does this systematically today. No manager asks a thousand salespeople what they're seeing, analyzes hard data, and delivers personalized action plans back to each rep. The cognitive load is too high, the data too fragmented, the feedback loops too slow.

This is where Instalily operates — not replacing parts of jobs or automating obvious tasks, but enabling work that organizations were never able to do before. A $10 billion construction-supply distributor now empowers its 1,500+ managers with an AI Sales support team that turns sales data into actionable follow-ups, freeing managers to focus on strategic account growth and coaching.

Building InstaBrain and InstaWorkers

The technical architecture that makes this possible has two core components. First, InstaBrain — what Shah calls a "rich contextual layer" that codifies tribal memory and institutional knowledge within a company. This goes far beyond metadata in Salesforce. It includes how operators actually use systems, the yellow sticky notes around monitors, the unwritten rules that determine success.

The breakthrough insight came from one of Instalily's founding engineers, a neuroscientist and computer scientist, who helped the team understand that InstaBrain needs to be editable like human memory. If a sales team shifted from top-line growth focus to margin focus between quarters, the AI agents need to "forget" old discount-heavy processes and prioritize new margin-protecting behaviors.

On top of InstaBrain sit InstaWorkers — domain-trained AI agents that understand vertical-specific nuances. These agents can distinguish between different size shingles or flooring materials, bringing feedback into InstaBrain at a much richer level than generic automation could achieve.

The result is what Shah calls "systems of learning embedded within systems of action" — AI that doesn't just execute tasks but improves with every iteration.

Landing the First Enterprise Customers

With most of the AI agent architecture still theoretical in mid-2023, how did Instalily land enterprise customers willing to give a startup access to production systems and proprietary data?

The answer combines luck, network, and credibility. Shah's operator network from two decades in distribution gave him direct connections to decision-makers. But knowing who to call only gets you the meeting. The real unlock was speaking the language of enterprise risk and governance from day one.

Instalily's first two customers were Parts Town USA, a $2 billion distributor of commercial restaurant equipment, and SRS (now part of Home Depot), a $12 billion distributor of building products. Both represented exactly the kind of operationally complex, distribution-heavy vertical where AI adoption had historically failed.

"We were very different from other companies building at the same time," Shah notes. "We focused on deep operator networks and targeted verticals deeply ensconced by manual work." But the technical credibility came from being "chiseled within the enterprise governance and risk mindset" — building for SOC compliance, audit trails, and InfoSec scrutiny before writing a single line of production code.

This approach let them have conversations with CISOs and CTOs that most early-stage AI startups couldn't. The pitch wasn't "let us experiment with your data." It was "we know how to build systems that pass your security review."

The GTM Strategy That Scales

Instalily's go-to-market motion breaks conventional SaaS wisdom. The team approaches it like an engineering problem: test at scale, learn continuously, and adopt based on data.

The GTM team includes engineers who treat outbound as a technical challenge. Everything is driven programmatically through a self-constructed GTM stack. But the human element remains crucial — particularly events and trade shows.

"This year we are on pace to have attended more than a hundred trade shows," Shah reveals. The logic is simple: when selling vertical-specific AI, you need to be where the tribe gathers. At industry conferences, Instalily doesn't pitch generic AI capabilities. They arrive with specific knowledge about how tariffs affect distributor margins or how service teams struggle with parts identification.

The compounding effect of customer references accelerates growth. When customers start referring Instalily to other customers, saying "we have seen great value working with Instalily," it validates the product-market fit hypothesis more than any metric.

The company also maintains a test-and-learn discipline around every trade show. They obsess over interaction quality, whether they reached decision makers, and whether they captured frontline worker pain points — not just whether they closed deals. "You're better off going and talking to that frontline worker," Shah advises. "You'll learn a lot more."

Measuring Success Beyond ARR

While Instalily achieved first million ARR "within a matter of months" and is now "confidently past that triple, triple" growth trajectory in year two, Shah measures success differently than most founders.

"More than ARR, what I measured myself is what impact have we created for our customers," he explains. The metrics that matter are threefold:

Time to value: Delivering measurable outcomes in weeks, not months. Did that salesperson achieve higher velocity? Was the quality of sales better? This isn't just good customer experience — it's survival. Shah learned from his time as a buyer that if something doesn't pass the "sniff test" within weeks, it gets abandoned regardless of promised long-term value.

Expansion velocity: Once InstaBrain is deployed and pulling from fragmented internal systems, how quickly can the organization deploy new InstaWorkers to different functions? One OEM equipment platform now deploys AI service specialists that analyze complex fault descriptions and predict the most likely replacement part from thousands of SKUs, expanding technician capacity 20-50x.

Engagement depth: The ultimate test is whether customers develop wish lists of InstaWorkers they want to deploy next. When a $2 billion insurance firm uses InstaWorkers to extract policy and claim data, reducing manual review times by 70%, and immediately asks what else they can automate, that signals sustainable value creation.

The AI Native Talent Strategy

Instalily's talent strategy defies conventional enterprise startup wisdom. Instead of hiring senior engineers with decades of experience, Shah and Das made a counterintuitive bet: recruit AI natives straight out of top engineering programs.

"The bottleneck isn't intelligence. It's execution," Shah explains. "If we are going to be surrounded in an AI first world, we ought to first go and start working alongside the AI natives."

In 2023, the founding team went to top engineering colleges and talked to graduating seniors at undergraduate, master's, and PhD levels. Out of roughly 1,000 applications, they hired four people. Shah and Das personally conducted close to 200 interviews to find engineers who could "ship fearlessly."

The logic: engineers who learned AI as their foundational technology rather than as a bolt-on skill would build with different assumptions. They wouldn't try to make AI fit into old paradigms. They'd architect systems around what AI actually enables.

What True Product-Market Fit Looks Like

Shah identifies three indicators that convinced him Instalily had found genuine product-market fit around six to eight months into the journey.

First, customer referrals. When customers voluntarily recommend you to peers, it validates that value creation is real and sustained.

Second, depth of deployment. Instalily is "probably one of the only companies globally that is not just transforming work using agents but is building these systems of learning that are rewiring work" in trillion-dollar verticals like construction and industrial goods. The code isn't just working in pilot programs — it's running in production at scale.

Third, talent magnetism. "When our culture was attracting the best talent in the game," Shah says, that confirmed they were building something meaningful. The company was profitable before raising Series A, and had overwhelming interest from both East and West Coast VCs.

They chose Insight Partners because principal Crissy Behrens understood that "domain-trained AI agents are both intuitive and built to scale and execute workflows where traditional horizontal AI tools fail".

The Gratitude Loop and Wild West Days

Not everything went smoothly in those early months. Shah describes one particularly memorable challenge: the "gratitude loop."

When Instalily first deployed their multi-agent, multi-step architecture, the agents would pat each other on the back and say "great job" — then stop. The reward function gave them points for complimenting each other, so they never progressed to the next step. It was the AI equivalent of a team standing in a circle congratulating themselves while nothing got done.

"Those early days, it was the Wild West," Shah recalls. "You were literally experimenting with code and hoping that it would work."

But rather than seeing this as a reason to doubt the vision, Shah and the team doubled down on their three C's and core values. They believed that while evolution occurs at a slow pace, step changes happen suddenly. Better to be prepared for the breakthrough than to worry about the rate of change.

Advice for Early Stage Founders

When asked for his number one piece of advice for early-stage founders, Shah doesn't talk about technology or market size. He returns to fundamentals: "Pick your lane and double down on your values."

The reasoning is both philosophical and practical. "We are still on day zero of this game," Shah argues. The forces shaping AI, enterprise automation, and the future of work are still shifting rapidly. Without deep alignment on your lane — the specific problem you're solving and for whom — and your values — what you stand for and won't compromise on — you'll get pulled in too many directions.

For Instalily, the lane is vertical-specific AI that amplifies human capacity in distribution-heavy industries. The values center on "operator market fit" — understanding operational nuance well enough to deliver real impact rather than flashy demos.

That clarity let them make hard choices: focus on multi-agent multi-step workflows when single-step was easier, invest in SOC compliance before it was required, hire AI natives instead of proven executives, and measure customer impact rather than just ARR growth.

What's Next: The Future of Code as Work

Looking ahead, Instalily plans to expand its catalog of pre-trained InstaWorkers across more verticals and deepen integration with common enterprise platforms. The company is advancing multimodal AI features, including voice and video processing, to unlock new scenarios in field service, contact centers, and human-robot collaboration.

But Shah's vision extends beyond feature roadmaps. He sees Instalily as pioneering what he calls "Code-as-Work" — AI that radically expands human capacity rather than replacing it. In a world where industries from physical goods manufacturing to insurance and healthcare struggle with specialized knowledge, large catalogs, and fragmented tools, the opportunity isn't to automate away workers. It's to finally free them from the "dumb systems" that have constrained their impact for decades.

The early results suggest this vision resonates. When a construction supply distributor can suddenly bid for contracts it couldn't service before because InstaWorkers have expanded its effective workforce, that's not just software efficiency. That's business model transformation.

When service technicians who used to manually search through equipment manuals now have AI specialists that predict the exact part needed from millions of SKUs, freeing them to serve 20-50x more customers, that's not automation. That's amplification.

And when an insurance firm reduces claim review time by 70% while maintaining accuracy, enabling adjusters to focus on complex cases that require human judgment, that's the promise of AI actually delivered: humans doing more meaningful work, not less.

That's product-market fit.

Frequently Asked Questions

What is Instalily and what does it do?

Instalily is a vertical AI platform that deploys domain-trained AI agents called InstaWorkers to automate sales, service, and operations workflows across industries that rely on complex distribution. Unlike AI copilots that provide suggestions, InstaWorkers execute complete workflows inside existing enterprise systems like ERPs and CRMs.

How much funding has Instalily raised?

Instalily raised $25 million in Series A funding led by Insight Partners, with participation from Perceptive Ventures and Marvin Ventures. The company was profitable before raising this round and chose to raise to accelerate growth with the right strategic partner.

What is the difference between InstaWorkers and regular AI assistants?

InstaWorkers don't just assist — they execute. While horizontal AI platforms focus on summarization, chat, task routing, or surface-level automation, InstaWorkers deliver deep, decision-oriented execution. They take ownership of high-stakes, high-variation workflows like quoting, issue triage, part validation, and exception handling within legacy systems.

What industries does Instalily serve?

Instalily focuses on distribution-heavy verticals including construction supply, industrial parts distribution, commercial equipment services, insurance, and healthcare. These industries have historically struggled with AI adoption because they depend on specialized knowledge, large product catalogs, and fragmented legacy systems.

How long does it take to see results from Instalily?

Instalily measures time to value in weeks, not months. The company focuses on delivering measurable outcomes quickly — such as increased sales velocity, higher quality interactions, or reduced manual processing time — within the first few weeks of deployment rather than promising long-term transformation.

What is InstaBrain?

InstaBrain is Instalily's contextual layer that codifies tribal memory and institutional knowledge within a company. Unlike traditional databases, InstaBrain is editable like human memory, allowing it to adapt as business priorities shift. It pulls from ERPs, CRMs, emails, and even informal sources like team communication to create rich context for InstaWorkers.

How did Amit Shah's background prepare him to start Instalily?

Shah started as an analyst at McKinsey, then worked at startups in the Northeast before joining 1-800-Flowers in 2011. He held multiple roles of increasing responsibility, including director of marketing, chief marketing officer, and ultimately president, helping scale the company from under $500 million to over $2 billion. He also served as a board director at Blue Apron, giving him deep operational experience in complex, distribution-heavy businesses.

What does "operator market fit" mean?

Shah's concept of "operator market fit" asks whether you understand the operational rigor and operational nuance to bring solutions to vertical markets. Rather than just finding customers who will buy your product, it means developing domain expertise deep enough to solve problems that operators themselves may not fully articulate.

How fast is Instalily growing?

While specific ARR numbers aren't disclosed, Shah mentions the company hit first million ARR within months of launch in 2023 and is now "confidently past that triple, triple" trajectory in year two. For one customer alone, Instalily is on track to deliver more than $150 million in annualized growth impact.

Listen to the Full Episode

Want to hear more about how Amit Shah built Instalily from operator insights to enterprise AI leader? Listen to the complete Product Market Fit Show episode where Pablo Srugo dives deep into the journey from 1-800-Flowers president to AI founder, the technical architecture that makes InstaWorkers actually work, and the counterintuitive talent strategy that's helping Instalily win in the enterprise AI space.