§ 01Snapshot
- Levelwell over a decade leading technology, including as co-founder and CTO of a consumer platform with millions of users. Today an independent fractional CTO and due-diligence adviser to investors. Reads far more code than they write; does not take hands-on code review.
- Roles heldco-founder and CTO of a consumer platform · tech co-founder of short-cycle consumer products · independent fractional CTO and due-diligence adviser
- Scalehas built several engineering teams in companies from about a dozen to several hundred engineers, including teams spread across several regions
- Industriesconsumer platforms and apps · data and AI · investor portfolio companies
- Company typesfunded companies with 15+ engineers · investors' portfolio companies · founder-only experiments
- Specialtiestechnical due diligence for investors · engineering governance (predictability, risk, who decides) · infrastructure and data architecture · computer vision and AI in production · fast build-test-close experiments
- Best forinvestors before a deal · funded companies where releases slip and nobody owns engineering · founders who lost their CTO · teams putting computer vision or AI into production at scale
- Core stackLinux infrastructure, Kubernetes, Airflow, PostgreSQL, MongoDB, Cassandra, S3-compatible storage; Python and TypeScript in recent projects; computer vision and ML in production; self-hosted AI models. Language choice matters less to them than who owns the system.
§ 02Services
| — | PR/MR code review | Not offered by this expert |
| ✓ | Architecture review | |
| ✓ | Architecture design | |
| ✓ | Audit / due diligence | |
| — | Vibe-code rescue | Not offered by this expert |
| ✓ | Team mentoring |
§ 03Track record
An investor's portfolio company: "the tech looks wrong"
An investor sees a portfolio company that has raised a round, and its technology looks wrong. The founders say the problem is not the technology but management: predictability, risk and who decides what. One of our experts comes in at due-diligence stage, then either sets up the missing skills and steps away, or steps in and runs engineering for a while.
A consumer app: live within days, closed within months
A few founders split roles and shipped a consumer product within days. Usage took off through people sharing it, with no paid marketing. One of our experts was the tech co-founder. When growth stalled outside the first market, they closed it within months on a small budget instead of feeding it for a year.
Several ventures a year, closed fast when they fail
One of our experts runs short build-test-close cycles as a tech co-founder: several projects a year. If the market doesn't respond, the project closes within weeks; if the next step fails, within a few months. Money and time go to the next idea instead of a slow decline.
Moderating user uploads with self-hosted AI
Once users can upload photos and clips, some uploads are things no app can show. One of our experts built automatic moderation by comparing many open vision models on real uploads and running them on the team's own servers. This kept cost and data under the team's control before they trained a model of their own.
A system of hundreds of services someone could still draw
As the company grew, the platform reached a couple of hundred services. One of our experts, as CTO, designed dozens of them and could map most of the rest from memory, which kept every change traceable to what it might break.
Computer vision that saved minutes on every document
The product processed documents at high volume, and every second per document cost users time. One of our experts built a computer-vision pipeline, combining classic image algorithms with machine learning, that made each document several times faster to process.
Click data that multiplied a segment's lifetime value
The company had a large base of users' click data and one undervalued customer segment. One of our experts grouped users by behaviour and personalised the product for that segment, which grew its lifetime value several times over and lifted the business's core metrics.
Building engineering teams, including across regions
One of our experts has built several engineering teams, for their own companies and for others, including teams spread across several regions and time zones. They set up roles, hiring and delivery so the team runs without them in every decision.
Architecture that starts below the code: servers and data
Architecture is software, infrastructure and data, and most of the risk sits in the last two. One of our experts, more an infrastructure engineer than a developer by habit, has consolidated scattered servers into shared, managed infrastructure and run large data pipelines on containers.
Advising founders without running anything
Through investor partners, founders ask one of our experts for advice rather than money. They take advisory roles with no operational duties, paid in cash or equity, and review technology and team decisions at the points where a wrong call is expensive.
§ 04What you can book
A business that sells well but can't ship
Sales and product are strong, the CEO and CFO are capable, but there is no engineering leadership. The product lead assigns tasks to about fifteen engineers, technical leaders don't last, and releases slip. The expert starts with careful interviews (people under pressure don't need to hear "it's all bad"), then sets up who decides what, the delivery process, and either hires a CTO or acts as one.
An ownership map, a delivery rhythm and a hiring plan.
Due diligence: is the AI in the pitch real?
An investor is about to put money into a company that sells "advanced AI". The expert opens the repositories, documents and transaction records and checks each claim made in negotiations. Sometimes the "neural network" in the pitch is a simple statistical formula.
A yes/no answer on each claim, not a list of nice-to-have fixes.
Getting ready for investors to read your code
A founder is raising and expects technical due diligence. The expert goes through the pitch, the data room and the code the way an investor's adviser would, and flags every claim the evidence won't support, so the founder corrects the pitch before someone else finds the gap.
A list of claims with "supported / adjust / remove".
"We need a full rewrite": the senior answer
The team tells the founders the product must be rewritten before anything new can ship. The expert reviews what can change in place and finds the smallest path to the next feature within the current system, keeping a rewrite as a last resort with a stated cost.
The next feature's path without a rewrite, plus an honest cost of the rewrite if it's still needed.