The 4:30am Wake-Up Call
It’s 4:30am. I’m not woken by an alarm—I’m woken by anxiety. Pulled straight out of a dream by the crushing, existential kind that makes you question every decision that led to this moment.
$9 million run rate. Seven production SKUs sitting on shelves across 480 stores in 24 states—and that was just our first retailer’s store brand. Other national chains were coming fast behind them. Three manufacturers depending on our purchase orders. A team counting on paychecks. And me, lying in bed, calculating runway and wondering if today is the day everything collapses.
This was my life for three years as a co-founder at Always Cool Brands. Not the highlight reel you see on LinkedIn—the real thing. Building side by side with experts through the entire lifecycle, from quote to cash. Taking popular products full of dyes and additives, reverse engineering them, and rebuilding them dye-free with health and function in mind. Learning from food scientists, compliance specialists, and operations veterans. Finding ways that safety, quality, and profit could be protected and scaled using software, AI, and a little bit of elbow grease.
The weight of knowing that one missed EDI order, one compliance violation, one cash flow miscalculation could bring it all down.
The fear wasn’t irrational. In CPG, the margins are razor-thin, the timelines unforgiving, and the consequences of mistakes severe. Miss a delivery window? You’re paying chargebacks. FDA finds an issue with your nutrition label? Product recall. Manufacturer invoice doesn’t match the PO? Your cash flow projections are toast.
I didn’t know it then, but that anxiety was about to become my greatest asset.
Twenty Hats, One Chain
Let me paint you a picture of what running a CPG startup actually looks like.
It’s not just the co-founders. It’s an entire ecosystem. Retailers who believe in your product. Manufacturers who figure out how to scale your recipe. Distributors who get it from point A to point B. Shipping companies, packaging suppliers, design teams, printers. Dairies, bottlers, co-packers. Brand owners, brand managers, category buyers. Brokers who open doors. Compliance specialists who keep you legal. And on and on and on.
The supply chain is a chain for sure. It’s actually amazing how anything gets done.
Monday: The team’s working with our food scientist on a new smoothie flavor. Tuesday: Negotiating packaging costs with the dairy bottler. Wednesday: Coordinating distribution logistics from the dough plant. Thursday: Label design review with the printer—is that font size FDA compliant? Friday: Processing EDI 850 purchase orders from a national distributor. Saturday: Reconciling invoices against BOLs, trying to figure out why the numbers don’t match.
And me? I’m learning. Sitting in on every call, watching every process, finding the failure points. Where do things break? Where can we go faster? What can scale? How do we increase safety, quality, profitability—all at once? And then I write code.
And through it all: emails. So many emails. Scans of BOLs. PDFs of invoices. Spreadsheets everywhere—tracking orders, reconciling payments, managing deductions. The unglamorous reality of CPG operations.
Seven production SKUs doesn’t sound like much until you realize each one requires:
- Recipe formulation and testing
- Packaging design and FDA-compliant labeling
- Nutrition facts validation
- Manufacturer coordination
- EDI integration for orders and invoices
- Compliance tracking across 10 states
- Distribution coordination
- Payment processing and reconciliation
- Deduction management (the bane of every CPG company’s existence)
It takes a village to get a single product on a shelf. I’m just a nerd along for the ride, learning from experts across every link in that chain and writing code. But even with talented people everywhere, six-day weeks were the norm. The business was growing, but everyone was stretched thin.
The challenge I faced? So much operational knowledge lived in people’s heads—scattered across dozens of partners and vendors. Every process that wasn’t documented was a single point of failure. If someone got sick, burned out, or just made a mistake at 9pm when they should have stopped working hours ago—things could slip through the cracks.
The Decision That Changed Everything
Somewhere around year two, I had a realization: I wasn’t going to survive by working harder. The only path forward was working differently.
What if I could take the anxiety-inducing knowledge rattling around in my head—all those edge cases, compliance rules, reconciliation procedures—and encode it into software? Not just automate tasks, but capture the thinking that makes the tasks possible?
I started small. A script to validate nutrition labels against FDA requirements. An automated check for California Redemption Value compliance (trust me, CRV regulations are a nightmare). A state machine to track purchase orders through their lifecycle.
Each small win built confidence. More importantly, each automation bought me time—and that time went right back into building more automation.
Three years later, we’ve built something I never could have imagined.
In some cases, the software automates—taking repetitive processes and consolidating them into code that just runs. But in many cases, and this is critical, it validates. Because you can’t have one without the other.
Here’s the thing: humans will screw up. I can assure you, a person making a mistake at 9pm on a Friday will put you out of business just as fast as a misbehaving AI agent. You need to manage both. Software gives you that quality control. AI makes it so much easier. Both allow you to scale.
It’s not just AI. It’s humans accelerated and supported by AI that really makes things happen.
Building the AI Co-Founder
The automation evolved across three interconnected systems, each handling different aspects of the supply chain.
System One: The Claude Code Skill
I wrote a 645-line knowledge base that teaches Claude how to run broker operations. When invoked, the AI becomes an expert on cash flow constraints, EDI processing, state machine enforcement, and deduction management. It’s literally our operational brain encoded into a skill file.
This connects to something bigger: the skill is an AI agent persona. Claude doesn’t just follow instructions—it understands the why behind each step, the edge cases to watch for, the red flags that should trigger human review.
System Two: The Always Cool AI Web Platform
A Next.js application with over 250 TypeScript files, 66 reusable UI components, and 18 API endpoints. This handles compliance, FDA validation, label analysis, and nutrition tracking.
The crown jewel is the Label Compliance Engine. You wouldn’t think a simple label could hold up a product release for a quarter—but it can. Six-month slips weren’t unheard of as the value stream got stuck with too many cooks in the kitchen, waiting on legal teams to review every word, every font size, every claim.
Each round of review costs thousands in legal fees. Weeks in turnaround time. Teams across the chain sitting idle, waiting to get a product to market. And that delay doesn’t just cost time—it costs hundreds of thousands of dollars to the retailers and manufacturers who only get paid when a customer actually buys.
The engine is a LangGraph state machine that takes a product through classification, rule application, image analysis, regulatory checks, and assessment generation. What used to take weeks of back-and-forth now runs in seconds—catching issues before they ever reach legal.
We’ve automated compliance for 10 states with full legal citations. The Kosher certification validator catches ingredient issues before they become problems. The nutrition API pulls from both USDA and FDA databases to generate accurate nutrition facts.
System Three: The EDI Orders System
A Python CLI that manages the full PO lifecycle across our manufacturers—dairy bottler, smoothie producer, dough plant. It integrates with distributors and national retailers for EDI 850 purchase orders, 810 invoices, and 820 remittance processing.
The reconciliation engine automatically verifies PO → BOL → Invoice matches with a 96% verification rate, catching discrepancies before payment processing.
But the automation I’m most proud of is the K-Solve LangGraph agent. K-Solve is where CPG companies manage deductions—those frustrating chargebacks that retailers take out of your payments. The agent takes data that’s been exported from the K-Solve portal, imports it into our system, collects evidence PDFs in parallel, tracks dispute status, and keeps everything organized for resolution.
The Cost Wins
Here’s a metric that made me smile: we reduced monthly AI costs from $3,300 to $480—an 85% reduction. Strategic model selection, auto-caching integration, and knowing when GPT-4o-mini is sufficient versus when you need the heavy hitters made all the difference.
The Pattern That Changed Everything
Here’s what I learned building all of this—and it’s bigger than CPG.
When you’re dealing with FDA compliance, you can’t just trust that your AI “probably got it right.” You need to prove it worked the right way. The same is true for nuclear operations, healthcare, aerospace—anywhere lives or money are on the line.
The pattern I landed on: Observability-Driven Evaluation.
Step one: Encode business processes in agent graphs. LangGraph state machines enforce the exact workflow, making business rules code instead of documentation. Each step is a node with defined inputs and outputs.
Step two: Log everything via OpenTelemetry. Every agent action gets traced with full context. Send it to LangSmith for cloud observability, or reflect to OpenTelemetry collectors for self-hosted infrastructure. You get a complete audit trail.
Step three: Run evaluator agents over the workflow. Domain-specific AI reviews the traces: “Did this compliance check run correctly? Were all required validations performed?” A second layer of AI reviewing the first layer.
I’ve used this exact pattern in nuclear configuration compliance—domain-specific evaluators watching agent workflows. The architecture is the same whether you’re validating nutrition labels or safety protocols.
This isn’t just “I automated my job.” It’s a pattern that makes AI-driven business processes auditable and verifiable at scale. When regulators or auditors ask how you ensure compliance, you can show them the traces, the evaluations, the complete decision trail.
Waking Up Without Dread
Last week I finished the reporting automation—the task that prompted me to start this whole journey. As I watched the reconciliation dashboard generate itself, I realized something had fundamentally changed.
I’m at 20% utilization now. Down from 100%. And the automation I just deployed is pushing that toward 10%.
The recurring revenue from Always Cool Brands covers my mortgage, healthcare, and employee salaries. The business runs. Not because I’m grinding 80-hour weeks, but because I encoded my anxiety into software that doesn’t need sleep.
For the first time in three years, I’m sleeping through the night. The 4:30am alarm still goes off—old habits die hard—but I don’t lie there calculating runway anymore. I get up because I want to, not because panic makes rest impossible.
What’s Next
Something unexpected happened last month: I started reaching out to friends. Real conversations, not the rushed “sorry, crazy busy, let’s catch up sometime” deflections that had become my default.
“What should I focus on?” I’ve been asking. It’s a question I couldn’t have asked when I was drowning. There was no bandwidth for exploration when survival consumed every waking hour.
Here’s what I’ve learned: we don’t automate jobs to eliminate work. We automate to take on new challenges. The patterns I built for CPG supply chain—observability-driven evaluation, agent state machines, AI-as-domain-expert—they apply everywhere complex operations need to be both automated and trustworthy.
I’m excited to explore where these patterns lead next. Nuclear operations. Healthcare systems. Aerospace maintenance. Anywhere the stakes are high and the answer to “did it work?” needs to be provable.
Three years ago, I was a founder having panic attacks at 4:30am. Today, I’m someone who built AI agents capable of running a supply chain—and I finally have the headspace to think about what comes next.
The anxiety isn’t gone. But it’s encoded now. And that makes all the difference.
Always Cool AI powers the automation behind Always Cool Brands. If you’re interested in the technical architecture or want to talk about applying these patterns to your domain, reach out.