Tag: forward deployed engineers

Inside a $15B Autonomy Powerhouse: Founder Grit, Multi-Product Strategy, and GTM Wins

I’m fascinated by how world-class founders translate deep domain expertise into durable products and category-defining companies. Qasar Younis is the co-founder and CEO of Applied Intuition, a leading vehicle intelligence platform that helps companies develop and deploy autonomous systems at scale. In June 2025, the company raised $600M at a $15B valuation. Before Applied Intuition, Qasar was the COO and a group partner at Y Combinator, and earlier founded TalkBin, which was acquired by Google. He’s also held engineering roles at General Motors and Bosch.
From my vantage point leading product teams, the throughline in Qasar’s journey is the disciplined fusion of vertical SaaS focus with an enterprise-grade product-led GTM. It’s a masterclass in choosing a hard problem, building undeniable technical leverage, and then scaling with operational rigor. Below, I unpack the ideas that stood out and how I map them to day-to-day product management leadership and product-market fit lessons.
Two founder traits Silicon Valley undervalues came up early. I read this as stamina and operational discipline — the unglamorous habits that compound into advantage. In practice, that looks like tight execution cadences, brutally clear roles, and a willingness to slow down to make faster decisions later. In my experience, these traits are the difference between momentum and motion.
On productivity, the goal is to gain 1–3 extra months of work every year without burning people out. I’ve seen teams unlock this by standardizing operating rhythms (weekly operating reviews, quarterly product strategy resets), protecting deep work time, and eliminating decision latency with crisp escalation paths. If you’re looking for a playbook, “High Output Management” and “Only the Paranoid Survive” remain gold standards for building repeatable management systems.
Founders should read outside the startup canon. Industrial history like “The History of the Standard Oil Company” teaches power, platform strategy, and regulatory dynamics in ways Twitter threads never will. When you’re building in autonomy, defense, or other regulated arenas, these mental models become execution tools.
From YC, the big lessons were pattern matching and clean feedback loops. Pattern matching helps you see when a problem is fundamental versus incidental. But it only works if paired with fast, unvarnished feedback from customers and the board. I encourage PMs to institutionalize this with pre-briefs and debriefs for every major customer interaction and launch.
Qasar’s battle-tested startup formula resonated: start with a hard, valuable problem; recruit top 1% technical talent; instrument the business like an operator; and make the market come to you by shipping undeniable value. The founding insight for Applied was that companies needed robust simulation, tooling, and infrastructure to safely accelerate autonomy development — not just a single application.
Applied’s playbook — “vertical SaaS, product-led GTM, and leveraging VC networks” — is a blueprint I’d happily hand to any B2B founder. Product wins the first meeting; credibility and references win the second; value-delivery speed wins procurement. How Applied expanded beyond automotive and why Applied went multi-product early show the value of building a platform surface area that compounds learning, data, and revenue resiliency.
Why co-founder fit is make-or-break is a reminder that alignment on pace, product philosophy, and customer promise matters more than complementary résumés. The moment you become a real founder often coincides with choosing the harder path when an easier, shinier option appears. How great founders master luck is straightforward: maximize surface area with smart bets, tighten the feedback loop, and keep fixed costs low so you can wait for the right wave.
I appreciated the contrarian takes on startup culture, compensation, and cost control. Why being cheap is a startup superpower isn’t about austerity — it’s about optionality. Every dollar you don’t spend buys time to learn. And on the myth of “competition doesn’t matter,” the truth is it absolutely does; it shapes your positioning, pricing, hiring narrative, and customer urgency. Track it like a core product metric.
The early scrappiness — the Sunnyvale house setup — is a great reminder that proximity and speed matter in the zero-to-one phase. One tactic I’ve found powerful in enterprise motion is deploying forward deployed engineers to collapse the distance between product, implementation, and value realization. It converts “pilot purgatory” into production faster.
Why domain knowledge is making a comeback is obvious in autonomy and defense. In complex, safety-critical spaces, credibility, toolchain depth, and integration expertise drive trust as much as UI polish. That’s also why a multi-product strategy, when grounded in a coherent systems view, can accelerate product-market fit across adjacent verticals.
The mentors who shaped Qasar underscore the value of learning from operators across eras. Names like Paul Graham, Sam Altman, Marc Andreessen, Elad Gil, Kyle Vogt, and builders at companies like Waymo and Zoox reflect a network that pairs ambition with practical judgment — a useful pattern for any founder assembling their own advisory bench.
Referenced resources worth exploring: Applied Intuition; Ansys; Bosch; General Motors; Waymo; Zoox; “Google’s Acquisition of TalkBin”; “High Output Management”; “Only the Paranoid Survive”; “The History of the Standard Oil Company”; and profiles of Bilal Zuberi, Elad Gil, Marc Andreessen, Paul Graham, Peter Ludwig, and Sam Altman. These links provide context across autonomy, simulation, company building, and the investor-operator network that helps compound advantage.
Where to find Qasar: LinkedIn: https://www.linkedin.com/in/qasar/

October 20, 2025
From Yale Dorm Room to Lifesaving AI: How Prepared Disrupted 911 and Won an Axon Acquisition

I’m fascinated by products that earn their right to exist in the toughest markets, and Prepared is one of those rare cases. Michael is the co-founder and CEO of Prepared, the AI assistant for 911 calls that helps dispatchers capture information faster, translate emergency calls in real time, and deliver lifesaving context to first responders. Founded out of Yale in 2019, Prepared grew from a school safety app into a critical platform for emergency communications, disrupting a notoriously tough market. This mission-driven journey just reached a major milestone: Prepared was acquired by Axon, the global public safety technology company. From a product leadership lens, several choices stand out. The catalyst—tragically, school shootings—anchored the team’s conviction and sharpened their definition of value: every second saved and every bit of context delivered could change an outcome. That clarity enabled an unusual go-to-market motion for govtech: give away the first product for years to earn trust, validate workflows, and build a wedge that later expanded into an AI-driven suite. Counterintuitive? Yes. But in a market defined by risk, compliance, and procurement inertia, it was precisely the kind of strategy that compounds. I’ve spent years navigating complex buyers, and Prepared’s approach to government and public safety agencies is a case study in disciplined product discovery. When systems are “so outdated,” pushing a modern layer requires empathy for the incumbent stack, forward deployed engineers who embed with users, and a readiness to translate mission need into procurement-friendly outcomes. It’s also a reminder that in govtech, distribution is a feature: partnerships, integrations, and interoperability often unlock more value than any single UX improvement. One lesson I keep returning to is mission as competitive moat. Mission creates resilience during headwinds—endless rejections, long sales cycles, and the grind of security reviews—and it focuses prioritization when tailwinds arrive. Along the way, the team balanced conviction with customer feedback, asking not just “What did we hear?” but “Which signals matter?” That’s the only way to move from a wedge product to a robust platform without drifting into feature sprawl. A few moments from the story hit me personally. Staying mission-oriented under pressure is more than a slogan; it’s the muscle memory of teams doing the work when no one’s watching. Negotiating an acquisition from a hospital bed underscores how founder endurance and timing often collide in ways you can’t plan for. And the self-aware quip—“I want to be terrible at sales”—captures a product ideal: build something so indispensable that champions sell it for you. It’s not anti-sales; it’s pro-traction. On the AI front, Prepared’s evolution mirrors what I see across high-stakes operations: start with a narrow, high-value job-to-be-done and expand as trust accrues. Real-time translation and structured data capture are obvious force multipliers for dispatchers. Expanding the product surface area with AI requires rigorous guardrails, model performance transparency, and tight human-in-the-loop workflows—especially in public safety. That’s where gen ai earns its keep: augmenting judgment, not replacing it. For founders and product leaders, here are the takeaways I’m carrying forward. Use a wedge that maps to urgent, measurable outcomes; then earn the right to broaden. Consider free or subsidized entry when trust and standardization are prerequisites to adoption. Treat procurement like a product: reduce friction, de-risk the choice, and make integration paths obvious. Balance conviction with a learner’s mindset to keep the signal-to-noise ratio high. And build investor relationships early and often so capital is an accelerant, not a lifeline. If you’re exploring product-market fit in an enterprise or govtech context, ask the hard questions: How much should you listen to customers? Are you building in headwinds or tailwinds—and why? What partnerships both de-risk and differentiate? And when the mission is non-negotiable, how do you sustain it across phases—from first user to acquisition—without losing the soul of the product? Where to find Michael: LinkedIn: https://www.linkedin.com/in/michaelchime/ References: Axon: https://www.axon.com/ Dylan Gleicher: https://www.linkedin.com/in/dylan-gleicher/ March for Our Lives: https://marchforourlives.org/ Neal Soni: https://www.linkedin.com/in/neal-soni/ OpenAI: https://openai.com/ Peter Thiel Fellowship: https://thielfellowship.org/ Prepared: https://www.prepared911.com/ Sam Altman: https://x.com/sama Slack: https://slack.com/ Uber Eats: https://www.ubereats.com/ Yale University: https://www.yale.edu/

October 20, 2025
The AI Support Blueprint: From Zero Playbook to 75% Resolution and a Reimagined Team

Rolling out an AI Agent doesn’t just change how your team works – it changes who your team is.

I learned that in the crucible of a fast-moving launch. Before we launched Fin publicly, our Support team became its first alpha/beta tester and we had to move fast. No roadmap. No step-by-step guide. Just a powerful new technology, and a steep learning curve.

That experience is exactly what led us to create The AI Agent Blueprint – a resource we wish we’d had when we were starting out, and one we hope will give other support teams a clearer path forward.

Looking back, I won’t lie and say I was cool, calm, and confident about how to do this – I was nervous as hell. I had no idea how to implement an AI Agent and ensure it resulted in huge cost savings and stellar customer experiences.

We had older machine learning technology available to us (shout out to our first-gen chatbot, Resolution Bot), but as a complex software business, we really only used it for basic FAQs. In all honesty, we still had a way to go – both in using automation more effectively and in making the chatbot experience actually enjoyable for our customers.

So why the urgency?

When ChatGPT burst onto the scene nearly three (!!) years ago, Intercom’s Machine Learning team immediately spotted the opportunity and dived into building the world’s first (and objectively best) AI Customer Service Agent.

Suddenly, we were being asked to pilot this brand new technology with real customers and go all in ASAP. Because we were selling this powerful new functionality, we had to use it ourselves and show it off in the best possible light so customers would want to use it too. #nopressure

There was no playbook, just a lot to figure out. As a product management leader, I had to switch into rigorous product discovery while staying execution-minded.

Steady involvement, rising resolutions. From February to July, teams maintain a high 87–93 involvement range as resolution rates climb from 65 to 82—signaling how AI-driven workflows can boost support efficiency and outcomes.

How do we do a phased rollout, but scale very quickly?

How do we QA Fin’s responses and make continuous improvements?

How will we produce and manage all the content Fin needs?

What will we do about all the outdated content we already have?

What are the success metrics now? Should they be different to original Support KPIs?

Who’s responsible for the success metrics? Who manages this newcomer to our team?

It was daunting. We had to take a brand new technology, figure out how to use it, build a team around it, and move at breakneck speed to implement every new feature that rolled out. It was ambiguous, fast-moving, and a massive lift.

But we got there and the results speak for themselves: Fin is now resolving over 75% of our inbound support volume.

An isometric blueprint reveals how an AI agent powers modern support—from triage to resolution—linking chat, knowledge, and workflows so teams scale service without losing accuracy, context, or the human touch.

That outcome didn’t happen by accident. We embedded forward deployed engineers with Support, treated our AI Agent like a product creator in its own right, and used gen ai for product prototyping to tighten our iteration loops. We prioritized a customer support AI strategy that balanced containment with quality: containment rate, CSAT on AI-resolved conversations, first-response latency, and recontact rates became our core scorecard.

That success led to real change for me and my team: new roles, new responsibilities, and new career paths. I now run a whole new function that didn’t exist before: AI Support. We’ve created new and elevated roles like Conversation Designers and Knowledge Managers. Fin hasn’t just changed how we support customers – it’s transformed the structure of our team and the trajectory of our careers.

And now, we’re helping our customers do the same.

In all transparency, if I hadn’t been this close to the work, I might have waited to see how generative AI played out before committing. I might have waited for a blueprint for how to deploy and scale an AI Agent. I wish I had something like that when we got started, or even later when we had a solid foundation but needed to scale our AI strategy.

How much less scary would it be to implement an AI Agent if something like that existed?

Whether you’re just getting started or already using AI in some way, you’re not early anymore—and you shouldn’t have to figure it all out alone. Strong product management leadership, a clear change plan, and tight feedback loops are what separate experiments from outcomes.

That’s why we created The AI Agent Blueprint – a practical map for launching and scaling AI in support. It brings together everything we’ve learned from our own journey, and from working closely with our customers who are doing the same.

If you’re ready to operationalize gen ai in support, align on the right metrics, and redesign roles for the future, this blueprint will help you move from pilots to pervasive impact with confidence.

Inspired by this post on The Intercom Blog.

October 20, 2025
Mastering Intelligent Products: Proven Strategies to Transform Product Development with Gen AI

I’m focused on the future of the products we’ll build—and how we’ll build them. To see where we’re headed, I find it essential to reflect on the past four decades of product development, from on-prem software to cloud-native platforms, from waterfall delivery to agile and DevOps, and now to Generative AI reshaping how we imagine, design, and ship value.

Those cycles taught us a consistent lesson: when technology shifts, our product practices must evolve with it. We learned to ship smaller, measure better, and iterate faster. Today, we’re at another inflection point where the very process of product discovery, prototyping, and delivery is being augmented by intelligence.

Consider this quote: “Applying AI to the software development process is a major research topic. There is tremendous…”

That unfinished thought captures exactly where we are right now—on the cusp of tremendous potential. I see AI accelerating the full lifecycle: transforming ambiguous problems into testable hypotheses, turning research signals into prototypes within hours, and translating product intent into working code and test suites. Gen AI is becoming a collaborator in product discovery, a catalyst for engineering velocity, and a force multiplier for product management leadership.

When I talk about creating intelligent products, I don’t mean bolting on a chatbot. I mean systems that learn from real usage, adapt to context, and continuously improve outcomes. Intelligent products are instrumented end-to-end: they observe, predict, and personalize—while giving users clear control and transparency. They reduce cognitive load, anticipate needs, and create compounding value over time.

How we create these products must change too. In discovery, I pair structured customer interviews with gen AI summaries to surface patterns quickly. I use gen AI for product prototyping to explore solution spaces before we commit code. Forward deployed engineers work alongside PMs and designers to ship high-signal experiments into real environments, shortening the feedback loop from weeks to days.

Operationally, the playbook includes four foundations. First, a robust data strategy: clean pipelines, privacy by design, and event models that map to user value. Second, a model lifecycle: from prompt engineering and fine-tuning to continuous evaluation and rollback plans. Third, a product discovery cadence that treats experiments as first-class artifacts. Fourth, a design system that includes AI interaction patterns—confidence indicators, explainability, and safe defaults—so experiences feel trustworthy and consistent.

Intelligent products demand responsible guardrails. I define clear acceptance criteria for safety, bias, privacy, and reliability, and I use evaluation harnesses with real-world scenarios to test them. Human-in-the-loop checkpoints remain essential for sensitive decisions. Governance is not a blocker; it’s a quality system that protects users and the business while allowing teams to move fast with confidence.

If you’re getting started, focus your next 90 days on three moves. Identify one high-friction workflow where intelligence can remove toil or accelerate time-to-value. Stand up a lightweight experimentation pipeline that logs outcomes and quality signals by default. And empower a small cross-functional squad—PM, designer, forward deployed engineer—to ship a measurable improvement, not a demo.

The destination is clear: product creators who master intelligent capabilities will deliver outsized impact. The path is practical: blend rigorous product discovery with gen AI acceleration, build trust through transparency and safety, and keep users at the center of every decision. That’s how we’ll create intelligent products that compound value—and why I’m optimistic about what we’ll build next.

Inspired by this post on SVPG.

October 20, 2025
Mastering Pilot Teams: Proven Strategies to Navigate Product Model Politics and Win

I’m seeing more companies than ever commit to the product model, and the shift is unmistakable. Boards are leaning in, CEOs are being pushed, and the subtext is clear: valuation. That pressure can be a powerful accelerant, but it also introduces a very real dynamic—when pilot teams become the vehicle for transformation, the politics around them can either unlock momentum or quietly poison the well.

In my experience, the politics of pilot teams surface fast: who gets on the team, which domain gets picked, how success is framed, and whether the rest of the organization views the pilots as an elitist “special ops” unit or a path for everyone to follow. If I don’t address these dynamics head-on, I watch pilot teams deliver isolated wins that never translate into a durable product operating model.

Here’s how I approach it. I start by being explicit about purpose: pilot teams exist to de-risk the transformation by proving that empowered product teams, operating on clear outcomes, can deliver business impact in weeks—not quarters. I select problems that are meaningful enough to matter (activation, retention, expansion, cost-to-serve) and bounded enough to win. I staff a cross-functional triad—product manager, product designer, and a senior engineering lead—augmented with forward deployed engineers so the team can learn with customers in real contexts and rapidly ship. The language is deliberate: these are product teams, not projects, and discovery is not optional.

To neutralize the politics, I make the rules visible and fair. Team selection is transparent, criteria-based, and time-boxed. Success measures are defined up front and mapped to valuation drivers—retention, net revenue retention, conversion, and CAC payback—so the board and CEO see line of sight from product outcomes to enterprise value. I secure executive air cover for autonomy and decision rights, and I hold the same governance bar every two weeks: discovery evidence, shipped increments, customer signals, and outcome movement.

Execution-wise, I emphasize product discovery as the engine of speed and learning. The team commits to a tight loop: frame the problem, explore multiple solutions, test with real users, instrument everything, and ship small but frequent increments. We visualize the bets, we narrate the learnings, and we make trade-offs explicit. This cadence builds credibility quickly and reduces the urge to micromanage—because the evidence is always on the table.

The most consequential decision comes after the first 6–12 weeks: what do we scale? I codify the ways of working that made the pilot succeed—team topology, discovery practices, decision rights, metrics, and tooling—and then distribute them through enablement, not edict. I avoid the trap of permanent “hero teams.” Instead, we use the pilots to seed a repeatable product operating model that any team can adopt.

When I present progress, I speak in outcomes and learning, not activity. I show how the pilot teams shortened time-to-insight, increased the pace of value delivery, and built the muscles we’ll rely on at scale. I’m candid about what didn’t work and why; that honesty reduces organizational resistance and builds trust with leadership.

If you’re standing up pilot teams now, start by aligning the board and CEO on the outcomes that matter, pick one or two high-impact domains, staff a truly cross-functional team without hoarding all-star talent, and time-box the effort to about 90 days. Publish a one-page charter, instrument the metrics, and pre-commit to decisions based on thresholds: scale, iterate, or stop. Do this well, and the politics fade into the background while the product model—and your product management leadership—speaks for itself.

Inspired by this post on SVPG.

October 20, 2025
Forward Deployed Engineers: My Proven Playbook to Transform Product Discovery and Outcomes

As VP of Product Management at HighLevel, Inc., I’ve seen firsthand how forward deployed engineers can transform product discovery, speed up learning, and deliver outcomes that matter. When engineers sit with customers, observe real workflows, and prototype in the moment, we turn assumptions into evidence and reduce the time from insight to impact.

“Note: This is part of the product creator series of articles, based on the overview article, The Era of the Product Creator. This series is intended for anyone that wants to create a successful product, whether or not the person has had professional training or experience in product management, product design, or engineering. In the…”

When I talk about Forward Deployed Engineers, I’m describing highly capable product engineers embedded directly with customers and the product discovery team. They partner closely with product management and design to run focused, time-boxed experiments, build rapid prototypes, and validate riskiest assumptions early. This approach is especially powerful in product discovery and gen ai initiatives where fast iteration and tight feedback loops are essential.

In practice, I’ve found that a forward deployed engineer becomes the bridge between what customers say and what the team can test today. For example, while exploring a gen ai workflow concept with a key customer, we co-created an interactive prototype in a single working session. That prototype turned abstract requirements into something concrete the customer could react to, which gave us high-quality signal and accelerated our decision-making without overcommitting to a full build.

My playbook is simple and disciplined: pair the forward deployed engineer with a product manager and designer, define the learning objective for each discovery sprint, and instrument prototypes to collect actionable data. We keep the scope small, the cycles short, and the bar high for code hygiene so that successful experiments can graduate into production safely. Most importantly, we measure learning velocity—how quickly we answer the critical questions that de-risk value, usability, feasibility, and viability.

There are guardrails. Forward deployed engineers are not on-call firefighters or ad hoc professional services. They are discovery accelerators. To avoid thrash, I time-box engagements, maintain a clear discovery backlog, and capture decisions and learnings so the broader team benefits. Rotating engineers through these assignments also builds stronger product instincts across engineering, which pays dividends well beyond a single initiative.

Ultimately, this is the product creator mindset in action: empowering cross-functional teams to discover what works before scaling what doesn’t. Forward deployed engineers help us validate real customer value quickly, particularly in fast-moving spaces like gen ai, and they elevate the entire product discovery practice.

The post Forward Deployed Engineers appeared first on Silicon Valley Product Group.

Inspired by this post on SVPG.

October 20, 2025