Everything a result needs, in one team
Revenue recovery, AI products, AI content, search, analytics, platforms and integrations.
Blog / Category
How we work
Our process: diagnosing with data, shipping small reviewed changes, and reporting every week.
Fast and careful are usually traded off. In our work they come from the same habits: diagnose with data before changing anything, test on a full copy, ship small reviewed changes, and report every week what changed, what it did and what is still unproven.
These notes are the practices behind our results, written so you can use them on your own projects, with or without us.
This is for you if
- You want a partner who reports facts, not forecasts
- You have been burned by a big release that broke what earned
- You want to see how a team works before you hire it
Articles
A site that updates itself, and checks every page before it does
A new affiliate site publishes fresh data twice a day and new content every morning, with no one at the keyboard. How the automation works, and the one rule that makes it safe to leave alone.
A new site engine in one day, with every page proven identical
We moved evdevs.com from hand-uploaded static files to Astro on Vercel in one day. A parity check proved all 111 URLs matched, email never moved, and the site gained a lead pipeline.
Decide what success means before the test starts
One way of wording a title matches 28,413 searches a month; the other, 24. Before we test which wins, we write down how we will judge it. Why that order matters.
Know which channel, post and offer brings clients: every lead should arrive with its source
How we made every lead on evdevs.com carry where it came from, what the person needs and when they want to start, so marketing spend and content can be judged by the clients they produce.
Give the answer first: lead magnets that earn the contact
Our free self-checks give a scored result, the gaps and their fixes on the page, with no email required. Then they offer two ways forward and a report built to be forwarded. Here is how they work.
101 releases in 10 weeks, without breaking what earns
Speed and safety are usually traded off. On our current engagement we shipped 101 reviewed releases in 10 weeks on a site that earns every day. How.
Production: what a prototype skips and a real system can’t
A prototype assumes every system answers and every input is clean. Production plans for the day they don’t. What we build into every system so it keeps working, and keeps someone responsible for it.
Value: the goal isn’t AI adoption
Nobody needs AI; they need recovered revenue, a coached team, a product that ships. How we pick the outcome before the model, and use AI only where it earns its place, with examples from a school platform and a traffic recovery.
From zero automated checks to 918 in under nine weeks
Sixteen weeks of a product had no automated checks. By its September release it had 918, and a gate that blocks any release when one fails. What we built, and the two times the safety net itself needed fixing.
Trusted systems, not AI demos: the five stages behind every project
Ingestion, normalization, trust, value, production. The five stages every project of ours goes through, from a website losing revenue to an AI assistant for clinicians, and why the model comes fourth.
Your investors will check your numbers
Traction is the story of the next round, and investors increasingly check it. Two real cases of numbers that looked fine and were not, and the checks that would have caught them before a board deck did.
Forward-deployed engineering: engineers beside your team, close to the problem
Forward-deployed engineers work directly with your leadership, people, workflows and data, from strategy to production, instead of building from a spec at a distance. What it changes, what you need to provide, and how it differs from staff augmentation and report-only consulting.
Write the rulebook before the first page
Before building a new affiliate site, we audited 26 sites in its market against 13 rules. None of the affiliates met all of them. Why the rules came first, and how they became part of the build.
AI speeds the work. People stay responsible.
AI makes investigation, comparison and iteration affordable at a scale that used to be impractical. It also makes confident mistakes. How we split the work: what AI does, what stays human, and how we review what it produces.
Building a school platform with an AI pair programmer
97 code changes, 74 of them written together with an AI coding assistant. How we built SuperEscuela’s student app and teacher records system, and the rules that kept the AI useful and the data safe.
Test the paths that have never run
A billing error would have given top-tier customers a fifth of what they paid for. It was waiting in a feature nobody could test yet. How one round of hands-on review found it, and 30 other problems, before the first invoice.
Proven, likely or unknown: a weekly report your CEO can trust
Most status reports mix facts, hopes and guesses in the same tone. Three labels fix that. How we write the weekly report on every engagement.
Human in the loop, placed where it counts
Human review makes AI safe to act on, but only when it sits at the right points and people can actually do it well. Where to place reviewers, how to design the approval step, and how to turn every correction into evidence.
Senior team or first hires? The first 90 days after a round
After a raise, the instinct is to hire. Hiring is right, but it is slow, and the first months decide what the next round sees. When a senior team that ships from week one makes more sense, and how it should hand over.
Want a team
that works this way?
Tell us what you are working on. We will tell you how we would approach it, and what the first two weeks would look like.
Prefer to write directly? enable JavaScript to see the address