Services

AI, SEO and software, from one team.

The practices share people, tools and a habit of measuring. Most projects touch more than one. A new web app needs to be found. An agent needs somewhere to live.

See the work Talk to Daniel

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.

8 headings · 55 links · 5 schema nodes

Build

What each practice does

What each practice hands over.

Each practice has its own page. Here’s what goes in, what we build, how we measure it and what you keep.

AI Engineering

Agents, retrieval, extraction, automation

Agents that call real tools. Retrieval that cites its sources. Extraction from documents nobody structured. Each build ships with an evaluation set, and a person signs off wherever a mistake costs money.

  • Evaluation sets (examples with known right answers, used to check the work) written before the build
  • A review gate (a step where a person signs off before anything goes out) where being wrong is expensive
  • Code, prompts and evals handed over
Inputs
Documents, records and tools
Builds
Agents, retrieval, extraction, routing
Measures
An evaluation set and a trace of every run
Hands over
Code, prompts, evals and docs
How we build AI systems

SEO & AI Search

Technical SEO for two kinds of reader

Search has two readers now. One ranks pages and one quotes them. Both read your markup first, so we fix that layer. Prerendered HTML, structured data about real entities, clean crawl paths and fast pages.

  • Structured data (code that tells search engines what a page is about) that matches the copy
  • Pages that read in full without JavaScript
  • Machine-readable summaries like llms.txt (a plain-text file written for AI tools to read)
Inputs
Your site and its build
Builds
Prerendering, structured data, crawl paths
Measures
What crawlers are served, and real page speed
Hands over
The changes and the checks
How we do technical SEO

Websites & Apps

Sites, web apps, native apps, backends

Marketing sites like this one. Web apps and the APIs behind them. Native iOS and Android apps from one React Native codebase. The data pipelines and scheduled jobs underneath.

  • Fast before anyone tunes it
  • Strict security headers by default
  • One codebase for iOS and Android
Inputs
A problem and whatever already exists
Builds
Sites, web apps, native apps, APIs
Measures
Speed budgets, security headers, pinned outputs
Hands over
Repository, tests, build guards
Websites & Apps

Systems we run

Property and market infrastructure

We built the underwriting engine behind ADJL Capital’s property decisions and the market-data plumbing under its research. They’re still running, and new methods get tested on them first.

  • A listing URL in, a sourced file out
  • Point-in-time (as it was known on each date, without later fixes) market history
  • Backtests on the production code path
Inputs
Listings, public records, market data
Builds
Underwriting, research pipelines, execution
Measures
Provenance on every figure
Hands over
Running for ADJL Capital today
See the systems

Where they meet

Most work crosses a line.

This site is a software build and an SEO build at the same time. The investor app is software with the underwriting engine inside it. The underwriting engine is an AI system where the model writes the narrative and never a figure. We scope the work as one job, so nothing gets lost between teams.

Start here

Tell us what’s slow, or what can’t be found.

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.

Start a project
Email
daniel@adjlcapital.com
Email Daniel

Daniel reads and answers these himself, usually within a day or two. No sequence, no drip, and nothing sold to anyone.