The work.
Across the stack.

What these systems have in common

Most of them are the kind of software where being almost right is expensive. An arbitrage engine acting on an order book with a missing update is not slightly wrong, it is placing a real trade against a price that no longer exists. A wallet processing two of the same user's swaps at once is not slightly wrong either. A payout system that releases funds before an eligibility check is a compliance problem rather than a bug report.

So the engineering that shows up repeatedly is unglamorous: sequence-gap detection, decimal-only money math, idempotent handlers, per-user sequencing, transactional outboxes, and an approval standing between an automated system and a hot wallet. The interesting part of an on-chain product is rarely the chain; the interesting part of an AI product is rarely the prompt.

The second thing they share is a boundary drawn deliberately around what a model may do. Wen's in-app agent calls typed, validated tools that pass the same checks as a tap in the interface. Axon Wallet lets a model propose an intent and nothing else, with schema validation, policy limits and two-factor standing between the proposal and the transaction. ProTrader's coach is given a member's own computed numbers and guardrails that refuse trade recommendations outright. Separating proposing from deciding is what makes an agent shippable.

Where a project, testimonial or example here is illustrative rather than delivered client work, the page carrying it says so. There are no traffic figures, revenue claims or client performance metrics on this site.

If you need this, start here

The case studies say what we built. These say which service your problem probably is:

• People cannot finish a task, or finish it by copying data between systems — Web Development
• The product needs to be useful on a phone, away from a desk — Mobile App Development
• A model should read, retrieve or draft inside an existing product — Generative AI Integrations
• Something needs classifying, scoring, extracting or automating at volume — AI Development
• The product has to touch a chain, a wallet or an on-chain ecosystem — Blockchain Development & Integrations
• The numbers nobody trusts need to become numbers someone decides on — Data Science
• The audience already lives in Telegram, and the task should finish there — Telegram Development
• An artist needs one online home instead of a page of scattered links — Artist Platforms & Community

Not sure? Describe what happens today and what you want to be different. Sorting it into a service is our job. The glossary defines the vocabulary these pages use, and the planning guides work through the decisions that come before a build.