Realisier Labs

We read every line of your codebase, in days.

  • Every application, not the two or three a sample would cover
  • An architect checks every finding before you see it
  • You get a to-do list, not a report

Delivered for BOLD Precious Metals and MineralView

Audit my codebase See all case studies
Why we built it

Nobody has time to read all of it. A model does.

A normal audit samples a few services, takes weeks, and ends in a slide deck.

~500
findings delivered
2
companies, every app
4
pages tuned for speed
Days
not months
How it works

The model reads. The architect decides. Your team merges.

WHAT GOES IN WHAT THE MODEL READS HUMAN GATE WHAT YOU GET BOLD Precious Metals every application source, config, schemas MineralView every application source, config, schemas Live measurements what the database is asked how fast real pages load Three reading passes 1 · The code bugs · security · speed and what will age badly 2 · The database what nothing reads any more 3 · The slow pages home · listing · product · blog read in module-sized pieces, in context Architect triage 👤 Wrong ones thrown out Repeats merged Ranked by severity Effort estimated ~500 findings ranked, and scoped to one file and one fix Your team merges on its own schedule, worst first candidates The model reads everything · the architect verifies everything · nothing reaches your team unchecked
Everything the model produces passes a person before it reaches your backlog.
What we look at

Four things, across everything you run

No sampling, no representative subset. All of it.

The code itself

Every application read for bugs, security holes, slow paths, and the kind of mess that makes the next change expensive.

The database

Tables and data nothing reads any more, listed with the evidence. Nothing is dropped on a hunch: two independent checks have to agree.

The pages that earn

Home, listing, product detail and blog measured and taken apart, page by page: what's slowing each one, why, and what to change.

Then a person checks it

An architect reads every candidate finding and throws out the wrong ones before you ever see them. Only verified items reach your backlog.

Why this is hard

Pointing an AI at a repository is the easy part

Anyone can generate findings. The work is in making them trustworthy.

  • A codebase doesn't fit in one read

    Real applications are far too large to hand over whole. Each one is split along its own seams, with the surrounding context attached, so the model judges how code actually behaves rather than guessing at a fragment.

  • Raw AI output burns trust

    Left alone it repeats itself, argues about style, and states wrong things with total confidence. Hand a team two thousand comments and they stop reading at fifty. Every finding is checked before it counts as one.

  • "Unused" is until it isn't

    A table nothing has touched in months may be what the year-end run depends on. Nothing is marked safe to delete unless usage data and a search of every repository both say so, and the evidence travels with it.

How we work

Models do the reading. Engineers do the judging.

The best available AI models give one architect the reading speed of a whole team.

  • Real reading, not a scanner
  • Separate pass per concern
  • Read in context, not fragments
  • Traced across every repository
  • Wrong findings thrown out
  • Ranked by severity and effort
  • Evidence attached to each item
  • Nothing deleted on a hunch
  • Handed over in your own tracker
Talk to Sachin

Want your codebase audited?

One architect, the best available AI models, days rather than months, and a list your team can start working through the same week.

Start the conversation