The Workflow Company

We help B2B startups build and scale a consistent pipeline. We use AI and automation for precision in targeting, hyper-personalization in messaging, and ease in scaling.

Founder and CEO

First Round Fast Track

Mentor

Integromat

I help startups integrate Integromat into their operations to streamline and automate processes

Registered Partner

joerhew.com

I partner with early stage startup founders to bring clarity to their strategy and improve their operations. I help companies prioritize what’s truly important by leveraging data, then help implement a culture of quick iterations and learnings through no code. I like to get my hands dirty and execute, but also act as a strategic thought partner to the founders.

Clients include: Karrot (Daangn Market, Series D, backed by DST), Groove (Seed, backed by Resolute)

Fractional Head of Operations

Ops Hacks

Built a community of ops folks that work at early stage startups

Community Builder

Tyltgo (YC S20)

White-label, same-day delivery service for local merchants across Ontario

Co-founder & COO

Uber

(Sensitive figures redacted)

• Lead a team of 4 operations managers to perform continuous marketplace management of over 100+ municipalities across Canada with focus on growth, reliability, and efficiency
• Launched a series of experiments, features, and processes to consistently exceed targets on growth and costs - increased year-over-year deliveries by x% while reducing courier costs by y% 
• Provided managerial oversight to launch walkers as a delivery mode in Canada, which accounted for c. x% of deliveries in downtown Toronto within y months of launch

Head of Marketplace Operations, Uber Eats Canada

• Managed a cross-functional launch team for 3 months in leading the largest single-day launch in the history of Uber Eats across 30 cities in Canada, which increased national population coverage by c. x percentage points
• Built out Reporting & Performance Management function using SQL and GSheet, providing transparency and enabling accountability on organization’s OKRs for key stakeholders
• Conducted a technical analysis and modeling on x million+ historical transactions to determine the optimal pricing structure and project-managed the pilot of service fees in Canada

Business Operations, Uber Eats Canada

• Collaborated closely with the product team to launch a new carpool product, uberSHARE, the first P2P product since uberX the country's banning of uberX in March 2015
• Launched UberTRIP, an hourly rental product, to target tourists and business travelers and increased weekly city gross bookings by x% without incurring any additional costs
• Successfully overhauled the driver partner incentive program, saving costs equal to c. x% of weekly gross bookings on average with negligible impact on churn and engagement

Rides Country Lead, Uber Korea

Startups

1. One Hive: e-commerce app for international customers interested in purchasing Korean fashion items
• Supported UX and UI design of a mobile e-commerce service to market and sell Korean fashion items to international customers
• As business lead, gained 10K+ users and generated revenue within the first 6 months of launching

2. Foodle: mobile social networking platform with algorithmic recommendations for food enthusiasts 
• As product and business lead, designed UX of the mobile app and led marketing efforts across social and offline channels
• Collaborated with UI designer and front- and back-end programmers to launch product on iOS

Co-Founder / Business Lead

Morgan Stanley

• Built detailed financial valuation Excel models, based on assumptions and hypotheses derived from financial, operational, and economic data, to test feasibility and profitability of envisaged transactions
• Advised large Korean and multinational corporate clients on cross-border M&A transactions and other business development initiatives through providing financial and strategic analyses
• Selected transaction experiences include SK hynix’s US$ 248MM cross-border acquisition of Link_A_Media Devices and MBK Partners’ KRW 1.2Tn (c. US$ 1.0Bn) acquisition of a 30.9% equity stake in Woongjin Coway

Investment Banking Analyst

Citi

• Selected transaction experiences include serving as the Global Coordinator and Joint Bookrunner in Hyundai HCN’s KRW 83.6Bn (c. US$ 74MM) initial public offering and SK Networks’ US$ 700MM minority equity investment in MMX Mineração e Metálicos

I build a GTM system for early stage VC-backed B2B startups. 

Some examples of what we do for our clients below:

1. Outbound
- End-to-end email infrastructure setup and maintenance
- Diversification of mailboxes across 3 ESPs
- Hyper-personalized and relevant copies
- Methodical testing of permutations
- Granular segmentation of TAM
- Waterfall enrichment of emails
- LinkedIn connection requests
- Lead magnet strategy

2. Inbound
- Lead qualification
- Lead enrichment
- Lead routing

3. Signal-based
- Website visitor de-anonymization
- Linkedin engagements
- Second party intent
- Recent fundraising
- Hiring for a role
- New in role

4. CRM enrichment
- AI prompting for enrichment
- Data hygiene & deduping
- Fully automated triggers

Building a v0 of an automated account research workflow when a Deal gets created in CRM. 

The account research includes:
1. A company overview
2. GTM overview, based on website content
3. Technologies overview
4. Team overview
5. Case studies
6. Recent news 

This workflow spans across Attio (CRM), AirOps (Workflow), Notion (Interface) with calls to a handful of external APIs. 

Two things that stood out to me about AirOps as I was building this automation:

1. The Javascript / Python code module. So nice to be able to write code inside the tool, instead of no-code gymnastics.

2. The ability to modularize workflows and call them inside another workflow. Don't have to recreate the same functionalities over and over again.

Walkthrough coming soon!

Joe Rhew

GTM Engineering | Founder & CEO of The Workflow Company | ex-YC, Uber

Designing DB schema for LinkedIn entities on a Sunday afternoon. Having too much fun, might have to dial it back a bit.

The idea is to integrate this DB into our CRM. We could have bypassed the DB and built directly inside Attio, but certain analyses would have become unnecessarily complex (multiple join's, engagement scoring model, etc.).

Diving deeper into the schema design - Went with UUID as primary keys (over LinkedIn usernames) to future-proof against potential username changes. Immutability ftw.

Also, some interesting decisions around jsonb vs. regular columns.

Jsonb is great for things like company locations (array of structured data) and profile metadata, but gets messy when you need complex aggregations or range queries. The classic "flexibility vs. query performance" tradeoff. 

Initially wanted to just dump everything into a single "profiles" table, but normalized it out into people, positions, education, etc. Makes the join logic a bit more complex but way cleaner for running temporal analyses.

I spent the holidays re-working our landing page to align with where we're heading in 2025: AI + Automations for scaling GTM teams.

This is our 3rd update since launching The Workflow Company in October 2023:

1. Started with: "Automate all mind-numbingly repetitive and painful tasks from your business" 

Purposefully (and maybe naively) broad language to capture any interest around automations. After running some pilots, we niched down to GTM.

2. Refined to: "We build AI-powered outbound automations for B2B startups" 

Much more specific in our targeting (B2B startups) and offering (outbound automations). This served us well for a year.

3. And now: "AI-automated workflows that convert data into qualified pipeline" 

While we loved (and still love) doing outbound email for our clients, we've learned that outbound email is a "softer science" - the same inputs don't always lead to the same outputs, due to deliverability issues as well as the nascency of the software ecosystem around it.

With this new direction, we're doubling down on our zone of genius - combining a technical, scientific approach to problem-solving with business know-how. We wanted to focus on areas where we could build upon solid, stable foundations that enable the right approach we feel great about.

That's why we're building AI-automated systems and workflows for GTM teams. From TAM building to automated CRM enrichments to contact-level microsite creation, the possibilities feel endless and I'm really excited to let our creativity + advances in AI guide our path this year. 

More to come in the form of walkthroughs and lessons learned - excited to kick off the year!

Built an API endpoint over the weekend to pull a list of GTM technologies installed on any website, similar to Builtwith.

Builtwith is obviously a much more robust solution, but the reason I built this is that Builtwith is not real-time, but rather caches data on sites. So you don't always get the most updated techs, or there might be newer GTM technologies that haven't been listed on Builtwith, which will go unidentified.

More to come on how I'll be using this for our own GTM efforts.

Been thinking about the emergence of GTM Engineering, and especially trying to answer the "Why Now?" question.

I think these are the main factors (but please add any that I've missed in the comments):

1. The Silo Problem 

GTM functions today are fundamentally siloed - both in organizational structure and tooling ecosystem. Teams operate in isolation, each with specialized tools, resulting in a deeply fragmented tech stack. These silos aren't accidental; they're a direct reflection of how GTM teams traditionally operate.

2. The Qualitative Nature of GTM 

GTM inherently resists pure quantification. You're working with people, content, and qualitative elements that can't be reduced to mathematical formulas. This has historically made it challenging to systematize and scale GTM operations.

3. The AI Bridge 

The advent of AI has fundamentally changed how we handle qualitative data. We can now transform fuzzy, qualitative inputs into structured, actionable insights - bridging a gap that previously seemed insurmountable.

4. The Evolution of GTM Tools 

The new generation of GTM tools (Clay, Cargo (YC S23) 🧱, Copy.ai, AirOps) has evolved to be substantially more technical and integration-friendly. 

They expose API capabilities directly in their interfaces - not just as a feature, but as table stakes. With established incumbents dominating the space, these newer tools had to embrace integration as a survival strategy.

^

These factors combine to create the perfect conditions for GTM Engineers to emerge. They serve as the technical bridge across siloed functions and fragmented tools, traversing across walls between teams and enabling end-to-end visibility of GTM initiatives.

A recently trending type of post that's cringey:

Leaders boasting about replacing their teams with AI. 

You've probably seen it too - someone built an AI workflow so good they let go of xyz team. 

I feel it's extremely short-sighted. 

When we celebrate replacing humans with AI, we're missing a massive opportunity to future-proof our teams.

Instead of creating AI systems that replace our teams, what if we taught our teams to build and leverage AI themselves? 

Imagine your customer support team automating their routine tasks, then using that freed-up time to connect with your customers in ways AI never could. 

And I hate to be the one to tell you this, but the truth is... If you can replace your team with AI today, someone else's AI might replace you tomorrow. 

The real opportunity isn't about replacing humans with AI, but rather enabling humans with AI.

/rant

Had an interesting realization while diving into TypeScript yesterday that I think perfectly explains why outbound email campaigns can be so frustrating...

I was working on a project heavily dependent on external APIs and started using Zod - a TypeScript validation library that ensures data coming from external sources matches what your application expects. 

While implementing Zod, I came across this concept of "trust boundaries" - points where data moves between different environments with varying levels of trust (think: your backend talking to an external API).

The concept stuck with me because it perfectly captures why scaling outbound email campaigns feels like building a house atop multiple black boxes.

Every time you run an outbound campaign, you're crossing multiple trust boundaries:

First, you've got to get your email infrastructure set up. Multiple domains, multiple sending accounts, all at the mercy of Google and Microsoft's spam detection algorithms. That's your first trust boundary - you can't even fully trust if your emails will send!

Then there's your email automation tool (Instantly, Smartlead, etc.). Another trust boundary where reliability isn't guaranteed. Anyone who's run campaigns knows that moment of doubt: "Did it actually send properly?"

Finally, the biggest trust boundary: actually landing in your prospect's inbox. Enterprise security layers, email filters, spam folders... so many obstacles between your outreach message and your target audience.

Just like in software where each trust boundary crossing requires careful validation and error handling, each step in outbound requires its own set of precautions and fallbacks.

This realization actually influenced my recent pivot from outbound email campaigns to GTM engineering. While both involve crossing trust boundaries, I found myself drawn to systems where it's possible to build more reliable, predictable processes. 

By definition, you can't fully eliminate trust boundaries in GTM as you're connecting with people outside your org. But minimizing the number of trust boundaries in workflows gives me more visibility and control over the outcome of my work.

Not sure if this makes a ton of sense unless you've used Zod and run extensive outbound campaigns, but got pretty excited about the parallel while implementing Zod and wanted to share :)

Me waiting to see if that new LinkedIn connection starts with a pitch slap

Taplio

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