An honest read on your codebase, your AI readiness, and what to do about both.

Overview

A second opinion, written down

Sometimes the useful thing is not more delivery — it is knowing whether the current approach will hold, which AI bets are worth money, and what to do first.

Every engagement ends with a written report: what we found, what it costs you today, and a prioritised plan your own team can execute.

Technology consulting
  • A fixed-scope review, usually one to three weeks
  • AI use cases ranked by ROI and risk
  • Findings ranked by business impact, not taste
  • A plan your team can run without us
  • Straight answers about build versus buy

What’s included

What we review

AI readiness

Data quality, use-case fit, cost model, and whether a pilot will survive production.

Code review

Structure, test coverage, and the places changes — including AI — are most likely to break.

Architecture

Whether the current shape supports the next two years, and what to change if not.

Performance

Where time and money go — queries, caching, jobs, and model spend.

Security and compliance

Dependency risk, access control, and how prompts and data leave your boundary.

Database planning

Schema, indexing, vectors, and migration strategy for the volume you expect next.

Process

How the engagement runs

  1. 01

    Scope

    • Agree questions to answer
    • Access and context
    • Fixed timeline
  2. 02

    Investigate

    • Code and AI review
    • Team interviews
    • Measurements, not opinions
  3. 03

    Report

    • Written findings
    • Prioritised plan
    • Walkthrough session

Stack

Tools we reach for

  • Ruby on Rails
  • PostgreSQL
  • pgvector
  • Redis
  • Docker
  • AWS
  • Datadog
  • Brakeman

Let's talk about the AI you want to ship

Tell us where you are — an idea, a half-built product, or a system that needs AI that earns its keep. We will reply with a practical next step.

Start a conversation Email us