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Let's talk about your project.

I take on a mix of paid projects and selected experimental collaborations where the case study itself has public value. If you're working on something that involves knowledge work, content, data, or workflow automation—and you're willing to let me measure and share the results—let's explore it together.

The goal is simple: solve your real problem, measure what actually happened, and publish it so others can learn.

How engagements work

Most projects are fixed-scope consulting engagements where you pay for the work and results. Some exceptional projects are collaborations where the case study becomes part of my published research—the value of public documentation justifies reduced or waived fees. Either way, you get structured assessment, honest measurement, and a writeup that will be useful to others facing similar challenges.

What kind of projects fit?

—Customer support — responses take too long, follow the same patterns, or require constant human review

—Reporting — weekly or monthly reports that are manual, repetitive, and time-consuming

—Content — translations, summaries, formatting, or publishing work that repeats every cycle

—Websites — a small business needs a professional site but the agency quote is weeks and thousands

—Operations — a founder or small team drowns in tasks that feel like they should be automatable

—Feasibility — you want to know if AI actually helps your specific workflow before committing to a platform

The common thread: you do it regularly, it involves thinking work, and AI might accelerate or improve it. Scale doesn't matter. What matters is that the problem is real and the results are measurable.

How a project starts

You describe the problem. I do a 30-minute review to understand the workflow and whether AI can meaningfully improve it. If it looks promising, I propose a scope, timeline, and assessment plan. If it doesn't, I'll tell you — no charge, no pressure. The goal is to only take on work where the measurement will be worth publishing.

sergei[at]kodulabor.ai

Sergei Anikin

What makes a good Kodulabor project?

✓A specific, repeatable workflow—something you or your team does regularly where AI might help.

✓Clear success metrics—time saved, cost reduced, quality improved, or something measurable.

✓Willingness to document and share findings publicly (anonymized if privacy is required).

✓Scope that's real but bounded—weeks of focused work, not years of development.