AI · Websites · Apps · SEO
ADJL Technology builds AI that handles the busywork, websites people can find, and apps for iPhone and Android. Everything on this page was built by us. Scroll down and try it.
This page’s own structure, drawn at build from its HTML.
People read the top layer, crawlers follow the middle one and machines parse the bottom one.
31 headings · 70 links · 12 schema nodes
◆ BuildWhat we do
You don’t need the jargon to work with us. Here’s what we build and what you get.
AI Engineering
We build assistants that read your documents, pull out what matters and fill in your systems. When a mistake would cost money, a person checks it first.
You get
SEO & AI Search
Search engines and AI assistants read your site’s code before they show you to anyone. We make that code clear, complete and fast.
You get
Websites & Apps
We build websites, web apps and apps for iPhone and Android. The phone apps share one codebase, so a fix lands on both at once.
You get
Systems we run
We built the engine behind property decisions at ADJL Capital, a private investment firm, and it’s still running. Paste in a listing link and it returns a file where every figure names its source.
How we run them
“We need a booking page that takes a deposit.”
The code for that page.
A working booking page with a deposit button.
What we’ve built
Each card opens a page that walks one system step by step. We show how each one works. We don’t publish what it returned, and since the firm is new, you won’t find client logos here either.
Paste a property listing link. It finds the property, pulls current rents and similar properties, reads years of public records and estimates the property tax after a sale. Every figure names its source.
Resolves the parcel, pulls live rents and comparables, reads years of public record and models tax through reassessment. Every figure names its source. Anything unaudited says so.
How the engine worksA research tool for people who aren’t professionals. Give it an address, a stock ticker or a business for sale. It shows the records behind the price, and AI can add a note that explains the figures.
A property scanner, company reports read from SEC filings, a business analyst and an indicator our research tested. The engines are pure modules that run in the browser. AI writes one optional note from figures the code computed.
How Loep worksIt takes in market data and works out orders. Tests replay recorded data through the same code that runs every day, and each evening it writes a research brief.
Market-data ingestion, order logic and backtests that share the production code path. Plus a daily research pipeline built on point-in-time (as it was known on each date, without later fixes) index membership.
How the pipeline worksAI agents write the code and run the studies. A person approves the plan and decides what goes forward. The most promising results are followed on paper after every market close.
Agents write the spec, the plan, the code and the reviews. A person approves each gate. Ideas face a blind control on the same days, and rules-based programs follow the chosen ones on paper after every close.
How the research runsAn iPhone and Android app. The full property math runs on the phone with no signal, and tests hold it to the same answers as the website.
One Expo and React Native codebase for iOS and Android. The underwriting engine runs on the device, and tests hold it to the same answers as the web.
How the app worksA website with a members’ area. Those pages are never published to the public web, and each request is checked before one is served.
Members’ pages are kept out of the public build. An edge function checks a signed session before it serves any of them.
How the lock worksThe site you’re on. Every page is complete before any code runs, nothing is loaded from other companies, and the build stops if the copy overclaims.
Static HTML, a content policy that allows nothing from other origins, structured data on every page, and a build that fails on hype.
How this site is builtSend a few lines about what you need, and Daniel will answer you himself.
From here down, it’s the proof, and it gets more technical. You can try the next two demos yourself.
SEO, demonstrated
Nobody outside a search engine can tell you what it’ll do with a page. We can show you exactly what it’s handed. When scripts run, the panel below fetches this page again and reads the raw HTML with no script run. Switch readers to see what an answer engine gets.
GET /status needs JStime to first byte needs JSbytes needs JS
AI Engineering, SEO and Software Studio | ADJL Technology57 of 60
ADJL Technology builds AI agents and automation, websites and native apps, and the technical SEO that gets them found by search and AI answer engines.150 of 160
https://adjltechnology.com/index, follow (default)Words readable without JavaScript
2,237 of needs JS
outline · 31 headings
structured data
9 entities · 10 references · 1 block · ✓ parses
machine-readable files
markdown copy · /index.md · first 40 lines
# AI, websites and apps, built for what’s next. > ADJL Technology builds AI agents and automation, websites and native apps, and the technical SEO that gets them found by search and AI answer engines. Page: https://adjltechnology.com/ Updated: 2026-11-12 AI · Websites · Apps · SEO ADJL Technology builds AI that handles the busywork, websites people can find, and apps for iPhone and Android. Everything on this page was built by us. Scroll down and try it. [See what we’ve built](https://adjltechnology.com/#systems) [See what AI sees on your site](https://adjltechnology.com/scan/) We build AI that reads documents, fast websites, iPhone and Android apps, pages that search engines and AI can read, automation and dashboards. People read the top layer, crawlers follow the middle one and machines parse the bottom one. What we do ## What we can build for you. You don’t need the jargon to work with us. Here’s what we build and what you get. AI Engineering ### AI that does the busywork We build assistants that read your documents, pull out what matters and fill in your systems. When a mistake would cost money, a person checks it first. You get - Invoices, contracts and forms read for you - A person’s sign-off where it counts - Code and tests, handed over [How we build AI systems](https://adjltechnology.com/services/ai-engineering/) SEO & AI Search ### Easy to find, easy to quote
- [AI Engineering, SEO and Software Studio](https://adjltechnology.com/): ADJL Technology builds AI agents and automation, websites and native apps, and the technical SEO that gets them found by search and AI answer engines.Nobody can promise you a place in an AI answer. We can make your pages easy to read, quote and credit, and then measure what changes.
Retrieval, demonstrated
This is the pattern under most of our AI work, small enough to run in your browser. Your question is matched against every paragraph on this site. The best passages come back quoted, with links. If nothing scores well enough, it says so and points you to a person.
Your words, lowercased and cut to stems. Common words drop out.
Nothing under the floor gets quoted.
Up to three passages from different sections, each with a link.
Nothing on this site answers that well enough. Ask Daniel at daniel@adjlcapital.com.
With JavaScript off, these were answered when the site was built, by the same code your browser runs.
The search index didn’t load, so here are the answers the build computed with the same code.
Q1 · answered at build3 passages cited.
Who owns the code?
Source, prompts, evaluation sets and infrastructure code assigned to you in full
/approach/#ownershipYou own the code, the data, and the right to leave.0.61
A runbook, a handover session, and code your own engineers can read
/approach/#engageThree shapes of work, each with a defined end.0.54
You do. The code, the prompts, the evaluation set and the documentation, in your repository, with no license back to us and no runtime dependency on anything we host. There’s no version of this where you have to keep paying to keep using what you paid to have built.
Q2 · answered at build1 passage cited.
Do you build mobile apps?
Yes. Native iOS and Android from one codebase, with Expo and React Native. The app we built for ADJL Capital runs its whole underwriting engine on the phone, offline.
Q3 · answered at build1 passage cited.
What happens to our data?
Those agreements carry a standing position. Client data stays in the client’s own environment wherever that’s technically possible. Where some part of it has to leave, say to a model provider’s API, the client is told before anything is built exactly what leaves, where it goes, and how long it’s retained there.
Q4 · answered at build3 passages cited.
What’s llms.txt?
Machine-readable summaries like llms.txt
llms.txt is a newer idea. It’s a plain markdown file at the root of a site that lists the pages, each with a line about what it covers. It’s a proposed convention and not a standard. Some AI tools read it and many don’t.
/notes/what-ai-crawlers-read/#llms-txtllms.txt is a summary, and only a proposal.0.59
The site now writes llms.txt, a plain summary of every page for AI tools, and llms-full.txt, the full text of each. They’re made from the built pages on every deploy, so they can’t drift from what a browser gets.
Retrieval only. No language model writes these answers, and your question never leaves this page.
How we build
It barely matters whether the thing is an agent, a website or an app. We define right before we write code. Then we build the smallest version we can check, and hand it over running.
We follow the real process with the people who do it. Every workflow has a step nobody wrote down.
▲ friction
Expensive, repetitive, and survivable when it’s wrong now and then. Picking a glamorous one is how projects stall.
For an AI system it’s an evaluation set built from real cases. For a site it’s a speed budget and a crawl test. Either way, it exists before the build does.
from real cases
| input | expected | got |
|---|---|---|
| •••• | •••• | not run |
| •••• | •••• | not run |
| •••• | •••• | not run |
| •••• | •••• | not run |
for a site
One step, one system, one output you can measure. It runs on your data, as code your team can read.
@@ extract()+ const fields = await extract(doc, InvoiceSchema)+ if (!fields.total) return review(doc, 'no total found')from real cases
| input | expected | got |
|---|---|---|
| •••• | •••• | ••••✓ |
| •••• | •••• | ••••✓ |
| •••• | •••• | ••••✓ |
| •••• | •••• | ••••✓ |
for a site
A person reviews anything that’s expensive to get wrong. The build fails on anything a machine can check. This site won’t deploy if its copy uses a hype word, counts clients or puts a number on a result nobody measured.
$ npm run build✓ check-css✓ check-links✓ check-claims✓ check-contact✓ check-motion✓ check-markers✓ check-usage✓ check-voice✓ check-seo✓ check-provenance✓ check-budgetIf you’re reading this, every check passed.You get the code, the prompts, the evaluation set and the docs. Your team can change it without calling us.
your-repo/ /code /prompts /evals /docs /runbookowner · your org
Working with us
ADJL Technology is Daniel Laskowski and a small senior development team. He takes the first call, writes the scope and reads every message sent to the address on this page. Nobody stands between you and the person accountable for the work.
The team builds AI systems, websites, native apps and the search work around them. The software behind ADJL Capital was built here, including its underwriting engine, its investor app and its private website. Client work is held to the same standard.
Four steps, and the first one’s free. After the second you have a written scope with a price on it. After the fourth your team owns a running system.
A conversation
A call with Daniel and whoever will lead the build. No qualifying call first, and no salesperson.
Week one
Time with the people doing the work and the systems holding the data. The real problem often turns out to be somewhere else.
Written down
The one step we’d build, what it costs, how long it takes and what has to be true for it to work. Priced and dated before anyone commits.
Then it ships
It runs against an evaluation set from day one and goes live behind the review gate the risk deserves. It ends with your team owning it.
What the work is built out of. The constraint decides the tool.
Fair questions
Daniel founded the firm and leads every engagement. He scopes the work and stays on it through delivery. The build is done by a small senior development team, and you meet the people who are on yours. There’s no bench of juniors a brief gets handed down to, and nobody translating between you and the engineers.
The model is one component, and usually the least interesting one. The work is in getting your data to a state where a model can act on it, deciding where a human has to stay in the loop, and building the evaluation that tells you whether any of it works. Swap the model out in a year and that scaffolding is what you keep.
Usually alongside them. We’re not there to replace anyone. An in-house team knows the business far better than we will in week one. What they often don’t have is spare capacity, or a reason to have built this particular thing before. We build the piece, work in your repository against your conventions, and hand it to your people to carry. They’re the ones who’ll still be here in a year.
You do. The code, the prompts, the evaluation set and the documentation, in your repository, with no license back to us and no runtime dependency on anything we host. There’s no version of this where you have to keep paying to keep using what you paid to have built.
It stays in your environment wherever that’s possible. Where it isn’t, you’re told exactly what leaves, where it goes and how long it lives there. You get that before anything is built, not in an appendix afterward.
ADJL Capital is a private investment firm with three partners. ADJL Technology is a separate company, founded and led by Daniel Laskowski with its own development team. The software the fund runs on was built here, which is why we can describe it in this much detail. It’s our own. Neither firm owns the other, and nothing on this site is an offer of any security.
Nobody can promise that, and you should be wary of anyone who does. We can make your site easy to read and hard to misquote. That means prerendered pages, structured data that matches the copy, one clear name for each thing you sell and a plain-text summary for machines. Then we check what the engines actually say.
Yes. Native iOS and Android from one codebase, with Expo and React Native. The app we built for ADJL Capital runs its whole underwriting engine on the phone, offline.
No. Those are the systems we built for ourselves first, so they’re the ones we can describe in detail. The patterns carry over to any business that runs on documents, data and decisions.
Start here
One paragraph is plenty. Tell us what you want built, the step that costs the most, or the page that should rank and doesn’t. If you know why the obvious fix hasn’t worked, say that too.
Daniel reads and answers these himself, usually within a day or two. No sequence, no drip, and nothing sold to anyone.