The demo works. Then the users arrive.

Writing the code stopped being the hard part. Deciding what is worth building, and checking what comes back, did not. A sign-in screen takes a morning. One that survives ten million accounts and somebody deliberately poking at it does not.

~45%

of AI-generated code ships with a known security flaw, and the figure has not moved in two years.

Source: Veracode, GenAI Code Security Update, Spring 2026. 100+ models tested. Accessed Aug 2026.

~243%

rise in the incidents-to-pull-request ratio as teams move from low to high AI adoption.

Source: Faros AI, The AI Engineering Report 2026: The AI Acceleration Whiplash, March 2026. Two years of telemetry, 22,000 developers across 4,000+ teams. Reported as 242.7%. Accessed Aug 2026.

$7,000+

per employee per month on AI, among the top 1% of US firms by AI spend.

Source: Ramp, AI Index, June and August 2026 editions. Card transaction data across 70,000+ US businesses. Reported as $7,449 per employee in June, and a median $7,400 for July. Accessed Aug 2026.

This is measured, not our opinion.

The same tools produce worse software when nothing about the process changes. Capability on tap is not the same as someone applying judgement and taking responsibility for what runs in production.

The ownership

Named engineers answerable for your system, one or a whole team, inside your team and on your stand-ups in your timezone. Not a ticket queue and not an agency layer.

The management

Engineering management over the work, included from five engineers up and available below that for a fee.

The mess you already have

Most of our work starts in a codebase that was built fast and is now fragile. Reading it, stabilising it and owning it from there is the job.

Start now, or have us read the code first

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