CuprBot Labs · Robby Singh
I help mid-market operators do twice the work without twice the people, by turning manual bottlenecks into systems that run themselves. AI is the engine. Margin is the result.
The knowledge on this site is free. The implementation is what you hire.
When a company grows, headcount usually grows with it, and that eats the margin. Every dollar of revenue you add without adding payroll compounds through one chain:
Revenue per employee ↑
The number PE buyers check first. Cross-industry median: ~$350K.
Operating margin ↑
Revenue added without hires is revenue that mostly becomes profit.
EBITDA ↑
Margin expansion lands here, dollar for dollar.
The multiple ↑
Efficiency 30% above your industry's benchmark earns a premium multiple. Below it, you get discounted.
What your company is worth ↑
Same revenue. Leaner operation. A meaningfully larger exit.
Companies that lean on AI show roughly 3× higher revenue per employee than their peers (PwC). The work I do lives at step one, and the rest of the chain follows.
Most companies come asking for the thing they've heard about: a chatbot, a pilot, "some automation." What they need is usually upstream. The manual loop quietly costing a salary. The data nobody mines. The process that breaks every time they grow. The distance between the two is where the value is.
What you want
"We need an AI pilot."
What you need
"Most of our revenue is repeat customers, and winning a lapsed one back costs a fraction of finding a new one. The system that does that on its own is what to build first."
A company has two levers. Bring in more money, or spend less making it. AI moves both, and most operators are leaving room on each.
Sales is advertising, marketing, and outreach. The edge now is personalization: ads and outbound written to the person in front of you, not the segment they fall into. The same first-party data that should drive your campaigns is usually the data nobody mines.
Personalized advertising lifts conversion 15–25% and ROAS 20–35%, and a sequenced win-back of lapsed customers reactivates around 15% of them.
Industry benchmarks (McKinsey, HubSpot, Meta, win-back studies)
Two costs quietly grow with you: the hours your team spends on work that repeats, and the cloud bill nobody owns. AI takes the repeated work off people's desks, and disciplined cost engineering takes the waste out of the infrastructure.
AI workflow automation returns 30–40% of knowledge-work time, and FinOps optimization trims 20–40% of cloud spend, with roughly a quarter of cloud spend being waste.
Industry benchmarks (McKinsey, NBER, Flexera, FinOps Foundation)
I have built across the stack for ten years: petabyte-scale data pipelines, LLM platforms for products with 100M+ users, and an ecommerce P&L I run myself. Whatever technology a lever needs, I can build it and hand it to your team.
Start wherever you are. The knowledge is free; the implementation is what you hire.
How to find trapped value, decouple growth from payroll, and make AI actually ship. Complete and free, with no email wall in front of the knowledge.
Read the playbooks →DiagnoseTwo minutes: your industry, revenue, and headcount against the benchmarks. See the gap in dollars, and which playbooks close it.
Run the diagnostic →BookBring your bottleneck. You leave with the best next step, even if that's not me.
Book a call →From the work section
Each one links to a full breakdown of how it was done.
30 PB/mo
of data processed on a pipeline I migrated to Kubernetes at Yelp, cutting several thousand dollars a day
Read the breakdown →30% faster
across a 5,000-server fleet at Index Exchange, holding 95% of revenue, with a $10K/month cost cut found in the telemetry
Read the breakdown →$37.59
to produce 256 production AI assets, every run written to a cost ledger you can audit
Read the breakdown →$29K net
in five months operating my own ecommerce P&L: pricing, fees, fulfillment, the full loop
Read the breakdown →If one of these sounds like your Tuesday, we should talk.
Revenue grew 40%, and so did payroll. Margins didn't move.
Years of customer data sit in exports nobody has ever mined.
Your back office runs on manual work your margins can't keep paying for.
You're preparing to sell, and your buyer will check efficiency first.
AI is on your board agenda, but nobody internal owns it.
Your first AI pilot stalled and nobody can tell you why.
Full breakdowns of real systems: the problem, the architecture, what shipped, and what it freed up. The knowledge is free; the implementation is what you hire.
At Yelp I co-designed the infrastructure that moved a 30+ PB/month data pipeline onto Kubernetes with a team of five, cutting several thousand dollars a day in cost while keeping storage, ML, backups, and customer reporting running.
30+ PB/month processed; several thousand dollars/day saved; a workflow state manager that returned hundreds of engineering hours a month
At Index Exchange I built an ML-enhanced rate-limiting model that lifted server performance 30% across a 5,000-server global fleet while holding 95% of revenue, and found a $10,000-a-month cost reduction in the infrastructure data.
30% performance gain at 95% revenue retention across 5,000 servers; $10,000/month cost reduction identified and acted on
On contract at a consumer social platform with 100M+ users, I architected the centralized LLM orchestration that put premium AI features into production, migrated models to clear a 40M-event backlog, and chartered the internal AI Guild.
Premium AI features in production for 100M+ users; a 40M-event backlog cleared on a model migration; an internal AI Guild chartered
I operated a real battery ecommerce P&L on eBay, $29,138 net over five months, then built the owned WooCommerce channel to escape marketplace rent.
$43,643 gross / $29,138 net (Jan–May 2026); 313 sales at 100% positive feedback
Every engagement is fixed-scope and priced on the outcome, never on the hour. Each step earns the next.
A fixed-scope audit of your data, operations, and systems. You get a ranked map of where money is leaking or hiding, a risk register, and a 90-day roadmap your team can execute, with or without me.
I build the highest-ROI system from the diagnostic, the one that adds capacity without adding payroll, and hand it over working, instrumented, and documented.
Hands-on workshops that teach your team the tools and the organizational patterns that make them pay: orchestration, verification, cost governance. The knowledge is free on this site; the teaching is how your team absorbs it.
Ongoing technical leadership: roadmap ownership, governance, vendor and hiring decisions, and a team trained to operate what we built. A CTO's judgment without the full-time cost.
What you get is capacity your payroll does not have to carry.
Book a 30-minute call. We'll identify your biggest bottleneck and the best next step, even if that's not me.