I'll find the highest-leverage automation in your business. You'll leave with a plan even if we never work together.
Pricing is one of those things that gets set once and then never really revisited. You look at what competitors charge, pick something that feels fair, and move on. But what if you could charge 15-20% more without losing customers? Most brands never test this because they do not have the data to feel confident about it. I built a system for Marmon Holdings that solved this problem for industrial assets worth over $500 million. They needed to know the optimal price point for buying, selling, and moving cranes across countries and continents based on where each asset would generate the most value. The system tracked every asset, modeled all the costs, factored in depreciation and location-specific economics, and told them exactly what each crane was worth in each context. That system ended up saving them $12 million per year.
Get the price your competitors charge, the price customers will pay, and the price that maximizes your profit. With the math to back it up.
A one-time price analysis tells you where to start. But prices should change with demand, seasons, and competitor moves. I build systems that watch all of this and adjust automatically, so every sale happens at the right price.
Discover the audience segment that values what you sell at 30-50% higher prices. Get their pain points, where they hang out, and the exact words they use.
When customers constantly push back on price, it is usually a positioning problem, not a product problem. The same product sold to a different audience, or framed around a different pain point, can command significantly higher margins. Finding that audience just takes research that most people do not have time to do. I built a system that researches Quora, Reddit, Google Trends, market reports, Facebook groups, and other sources to find the exact pain points of any target demographic. It aggregates everything, identifies which problems come up most, pulls direct quotes you can use in copy, and surfaces patterns you would miss looking at any single source. The kind of research that would take a person about two weeks and cost $3,000-4,000 at typical analyst rates takes this system a few minutes.
Markets shift. Competitors copy you. New pain points emerge. I build systems that scan Reddit, Quora, and reviews weekly and alert you when something changes, so you always know what your customers care about.
Influencer marketing works, but finding the right creators is genuinely hard. Follower counts do not tell you if engagement is real or bought, and there is no easy way to know what creators charge or whether the math works at your margins. So most brands either skip it entirely or try a few random partnerships that go nowhere. I built a system that takes a subject, explains it in simple terms, and frames it to show credibility in a format that actually performs. That system increased followers and engagement by 45%, which translates to roughly $2,000 per month in organic reach that would otherwise cost $4,000-6,000 in paid ads. The work taught me exactly what makes content convert versus just get impressions.
Real engagement rates, actual past results, what they charge, and whether they will be profitable at your margins. No more guessing.
Finding good creators is step one. Managing dozens of relationships, tracking what actually worked, and catching rising stars before they get expensive is where the real leverage is. I build systems that run this for you.
Get 3PL providers ranked by what they will actually cost you, based on your products, volumes, and where your customers are. Apples-to-apples comparison.
Most people pick their 3PL based on a recommendation or whoever had the best sales pitch, not based on who actually fits their specific situation. Comparing providers properly is a nightmare because everyone structures pricing differently. I was on the team at Amazon that built systems to determine which warehouse should fulfill each order and which carrier should deliver it, using real-time data on inventory positions, carrier rates, and transit times to make optimal decisions across millions of daily orders. That system supports $175 million per year in value by reducing average fulfillment cost by 8%. I also built a health insurance comparison tool that helped 400 users save a combined $1.2 million by doing proper apples-to-apples comparisons across every available option.
Picking a good 3PL is the beginning. Routing each order to the optimal warehouse and carrier based on real-time rates and inventory, that is where the real savings are. I build systems that do this automatically.
Manufacturer relationships tend to drift over time, and the changes are so gradual you barely notice them. Quality slips a little, prices creep up, lead times stretch out. But switching feels risky and complicated, and there is no easy way to know if a new option would actually be better or just come with different problems. So most brands stick with what they have longer than they should. I audited third-party vendors at CME Group where they had accumulated relationships over years without ever stepping back to evaluate whether they were still the best options. I analyzed 240 datasets covering usage, pricing, and performance across the organization, then identified which vendors to cut, which to consolidate, and which to renegotiate. That audit ended up saving them $1.2 million per year.
Get suppliers matched to your volume, quality requirements, and payment terms. Verified reviews, real lead times, and what to watch out for.
A new supplier solves today's problem. But quality drifts and prices creep. I build dashboards that track defect rates, benchmark costs, and tell you exactly when it is time to renegotiate or switch.
Get exact reorder quantities based on your sales velocity, seasonality, and lead times. Month-by-month plan with the math behind it.
The inventory problem is real: too much stock ties up cash, and too little means lost sales. It is hard to get right because demand is not steady, and between seasonality, trends, and lead time variability, there are a lot of moving pieces that spreadsheets only go so far in tracking. I built financial models for Clarkson Grain that projected demand and revenue streams over 10 years, tracing every variable through the system to figure out exactly where to invest and where to pull back. Those models found a $2.5 million annual revenue opportunity they were missing and stopped them from making a $10 million investment in a revenue stream that would have failed.
Calculating stock levels once is a start. But velocity changes. Seasons shift. I build systems that watch your sales in real-time, generate POs automatically at the right moment, and alert you before problems happen.
Answering the same questions over and over feels productive because tickets get closed, but it is actually a trap. Every repeated ticket is a signal that something upstream is broken: unclear product descriptions, missing tracking notifications, confusing checkout flows, shipping delays that go uncommunicated. I built a system for McDonald's that catches errors before they become customer complaints. The system compares what outgoing emails say against what the actual order data shows, flags discrepancies in real time, and stops bad messages from going out. They process millions of order confirmations daily, so even a small error rate adds up. The detection system saved $220,000 per year in support costs and compensations by catching around 50 errors per day that would have become angry tickets.
Find the 3-5 root causes generating most of your tickets. Get specific fixes that eliminate them at the source, not band-aids that treat symptoms.
Root cause analysis tells you what to fix. I build systems that catch errors before customers see them, auto-respond to the predictable stuff, and only ping you when something actually needs your brain.
Describe what is eating your time and get a complete build plan: what to automate, how to build it, paste it into Claude Code and you will have a working prototype.
There are probably things in your business that should be automated, but figuring out where to start is the hard part. Zapier works for simple stuff but breaks down for anything complex, and hiring a developer means a $15,000+ quote with no guarantee it will actually solve the problem. So the manual work continues. I built a system that takes internal processes, codebases, and team knowledge and documents everything automatically, then keeps it updated as things change. That system helped onboard over 200 developers and saved $55,000 per year by turning undocumented tribal knowledge into actual systems that anyone could follow.
A blueprint shows what is possible. I take the plan we create together and actually build it so that it scales with your business and is secure. You get custom software created by an Amazon trained engineer without the headache of debugging code.
Every e-commerce business has dozens of time sinks hiding in plain sight. Here is how I approach the most common ones.
You didn't start a business to debug Zapier at 11pm
I learned how to build this stuff at Amazon, where "it broke when we got busy" isn't an excuse. Seven years of doing this, and I've seen what happens when brands outgrow their systems. The tool that worked at $50K becomes a nightmare at $200K. I build things that grow with you, so you're not ripping everything out and starting over in two years.
I'll give you $50 if I waste your time as long as you make $50K+/month