AI Advisory for Mid-Market & Enterprise

AI can write the code. It can't own the architecture.

Your teams are already building with AI. We make sure what they build is designed right, works as intended, and is safe to run, before you find out in production.

Book a 30-min conversation See the two services

The fastest-growing software team in your company may not report to IT.

Operations, finance, and customer teams now ship internal tools and agents with AI coding assistants in days. That speed is real. So is what gets skipped: architecture decisions, access controls, testing, and anyone checking whether the logic matches how the business actually works.

Built fast, reviewed never.

Tools reach daily use without a design review or a security check.

Right code, wrong decision.

An agent can be technically clean and still approve the wrong invoice, skip an exception, or apply an outdated rule.

No owner, no trail.

When something breaks, nobody knows who built it, what data it touches, or why it decided what it did.

44%

In Veracode's 2026 testing, about 44% of AI code generation tasks introduced a security vulnerability.

Source: Veracode, 2026 GenAI Code Security Report

Two ways we help: before you build, and after.

AI Framework Consultancy

Build it right the first time.

The question it answers

"We want to solve this with AI. What's the right way to do it here?"

What we do

We start from the business problem and design the framework around it. That means which models and tools actually fit, what data they may touch, the reference architecture, the standards your teams build to, the review gates before anything reaches production, and where people stay in the loop.

You walk away with

A documented framework your own team builds on, plus the decision record behind every choice, so leadership and auditors can see why, not just what.

Good fit when

you're starting an AI initiative

you're standardizing how business units build with AI

you're choosing between tools and models that look identical from the outside

AI Implementation Audit

Know what's running, and whether it holds.

The question it answers

"We've already built and deployed AI. Is it doing what we think it's doing?"

What we review, on two layers

Technical: architecture, security, access and data exposure, code quality, monitoring, and ownership.

Logical: whether it applies your business rules correctly, handles exceptions, produces outputs you can trace and explain, stays within the decisions it was approved to make, and gets checked by a person before an error reaches a customer or the books.

You walk away with

A plain-English risk map for leadership, specific fixes for your builders, and the safeguards to install: validation checks, approval points, logging, and fallback behavior.

Good fit when

AI tools or agents are in daily use

an audit or compliance cycle is coming

you've had an incident or near-miss

leadership asks "how much AI do we actually run?"

Start with either one. Most clients find that each leads naturally to the other.

Why a code review isn't enough

Scanners check whether code is secure. Cleanup shops fix one app. We look at the whole process (the system, the logic, and the people around it) and leave your teams with a framework so the next build doesn't need rescuing.

We don't want to write your software.

Your team builds. We make sure it holds.

Model- and tool-agnostic.

We recommend what fits and work with the tools you already own.

Plain English for leadership, specifics for builders.

Every deliverable serves both audiences.

How we Engage

Conversation

We learn what's being built, what's already live, and what worries you.

Scoped proposal

Scope sized to the processes and systems in play, with clear outputs.

Phased delivery

Defined phases and defined deliverables. No open-ended engagements.

Handover

Your team owns the framework, the fixes, and the documentation.

Antonio Velazquez Bustamante, CTO of AramLabs

Who leads the work

Advisory engagements are led by Antonio Velazquez Bustamante, our CTO. He has more than two decades of experience building systems that have to hold up in the real world, and he tests claims against evidence, not hype.

AI Advisory

Questions teams ask before an AI advisory engagement

What mid-market and enterprise teams ask before an advisory engagement.

Do you replace our developers?

No. Your team keeps building. We give them the framework, the review gates, and a second set of eyes on what's already running, so they can move fast without creating risk.

We already use code scanning tools. Where do you fit?

Scanners judge one change at a time. We review the system as a whole: architecture, data access, business logic, and who is accountable. We also make the tools you already own work harder instead of adding new ones.

Do you only review code?

No. The logical layer is often where the real risk is: an AI that applies the wrong rule, skips an exception, or makes a decision nobody approved. We review both.

Which AI tools and models do you work with?

We're model- and tool-agnostic. We work across the major model providers and the coding assistants and no-code builders your teams already use.

Does an audit guarantee our AI is safe?

No audit can guarantee future behavior. We review a defined scope at a point in time, show you where the risk is, and help you install the safeguards that keep it contained. The scope is agreed in writing before we start.

How is this priced?

Every engagement is scoped after a first conversation, based on the processes and systems involved. You get a fixed proposal before any work begins.

What has your team built with AI in the last six months?

Let's talk about what's running, what's planned, and where the risk sits.

Book a 30-min conversation