LLM-embedded software development, not prompt engineering workshops. Launch features and products faster with standardised, LLM driven steps.
"What you actually have is a genuinely useful tool. One that's roughly 80% accurate at specific coding tasks."
The gap between what's been sold and what's real is where most engineering teams are losing time.
Everyone's been sold the same story: an intelligent AI colleague that makes every developer dramatically more efficient. Strip the marketing away, and what you actually have is a genuinely useful tool. One that's roughly 80% accurate at specific coding tasks. The gap between those two ideas is where most engineering teams are currently losing time, money, and trust in the tooling they've already bought.
We don't run workshops on how to write better prompts. We embed LLMs into how your team actually builds software, so features and products ship faster, and the quality bar stops depending on which developer happens to be working on it.
We find the parts of your software delivery process that are secretly translation problems — requirements into code, tickets into tests, legacy systems into documentation — and build a repeatable process around them, so the value sticks around after we leave.
LLMs work best when as much of the context as possible is available upfront, so we will help your team to build its requirements into a simple Markdown wiki that lives inside your repository instead of putting requirements into a project management tool where the LLM can't easily discover them.
Once we've made the job easy for the LLM, we'll give it simple, repeatable instructions to map the requirements into the first iteration of the new feature, saving time especially on standard, repeatable tasks.
Our expert engineer will then work with your team to complete the implementation of the feature to the same standard as human written code, making sure that generated code and tests do not suffer from AI hallucinations or out-of-context AI outputs.
Engineering teams who've already invested in AI tooling — Copilot licenses, an LLM API bill, ChatGPT Enterprise — and can't yet point to a clear return on it. If that's you, you're not behind. You're normal. Most teams are here.
Copilot, ChatGPT Enterprise, or direct API access already licensed and running.
Output quality varies wildly depending on which developer is prompting. There is no shared process behind it.
Spend is real. Efficiency gains are hard to measure. The story for the next budget cycle is unclear.
You've added AI tools to an existing workflow, but the workflow itself hasn't changed to match what the tools are actually good at.
A 2025 randomised controlled trial by AI safety research nonprofit METR found that experienced open-source developers using AI coding tools took 19% longer to complete tasks than developers working without AI.
Even after experiencing the slowdown, most still believed AI had sped them up. How AI feels to use and what it actually does to a process are two different things, which is exactly the gap we're in business to close.
According to The Economist, as AI agents spread through businesses and drive up token consumption, companies that once celebrated heavy usage are now scrapping leaderboards, capping spending, and switching to cheaper models to rein in costs.
AI spending has exploded as agents proliferate and token-heavy applications like reasoning models become more common, with some firms' bills rising more than tenfold in a year and one reportedly spending $500m on tokens in a single month.