What do we actually get?

A written report covering: data quality assessment with specific examples, reproducible model evaluation with accuracy numbers, infrastructure and scaling findings, security review, and a prioritized task list with time estimates. Quick wins separated from architectural fixes – so you know what to do this week vs what to budget for next quarter.

We don't have AI in production yet — is the audit still for us?

Yes. Many audits happen before any AI is built – checking whether your data is good enough, whether your architecture can handle what you’re planning, and whether AI is actually the right answer for the workflow. Sometimes the recommendation is “don’t build AI for this – here’s a simpler approach.” Better to know before you spend.

What kind of problems do you typically find?

Common patterns: data labeling errors that no amount of GPU compute can fix, prototypes built fast with no scaling plan, missing security basics (exposed API keys, no rate limits, no prompt injection protection), inefficient model choices that quietly drive up monthly bills, and architecture decisions that look fine at low load and break at scale. The audit catches issues that look small until they hit production.

What about NDA and data security?

Standard NDA before any access. Data stays in your environment whenever possible; when we need samples, we use your secure transfer of choice. We don’t reuse client code or data for anything outside your engagement. Same protections we use on long-term builds for Stevens Industries and Internet Archive.

Won't you just try to upsell us on fixing what you find?

The audit report is yours – take it to any vendor or hand it to your in-house team. Many clients do exactly that for at least some of the fixes. We’ll also tell you upfront if a different team is better suited for the build (e.g., niche domain expertise we don’t have). Independence is the point of an audit.

AI Audit

An independent technical review of your AI system. Accuracy numbers, defect list, fix plan with time estimates.

When You Need an Audit

[1]
You plan to add AI to an existing software

Start by checking your data and knowing the limits of your current system. This helps integrate AI without rewriting all your code.
[2]
Your architecture cannot handle the user load

The first step is reviewing what you have now. The result  is a list of specific changes to make it work at scale.
[3]
Accuracy is lower than expected

Often the problem is hidden in the experiments or metrics. A review of the data and system logic finds the real source of the errors.
[4]
Development has stalled

The cause might be technical debt, architecture mistakes or the wrong training approach. The audit helps figure out what is blocking progress.
[5]
The system works but costs too much

The audit shows where the money goes. It could be the LLM itself or server settings. The result is a concrete list  of ways to lower costs.

What an Audit Would Have Caught

Bad data on expensive servers

More computing power does not fix labeling errors.

If the data is inconsistent, the model will not learn. Teams often waste months before realizing the issue is the data.

Vibecoded prototypes without a scaling plan

You can build a demo in a weekend.

But the cost of development and support is often a surprise when moving from a prototype to a production system

Missing security checks

Security is often the last thing teams think about.

But basic mistakes can be expensive. Exposed API keys, visible system prompts, no spam protection. These are common issues that put the whole product at risk.

How It Works

[1]

Scope call

You describe what you're building or what's broken. We tell you whether an audit makes sense and what it would take.

[2]

Technical review

We look at workflows, data, infrastructure, costs, security - and the AI system itself if you have one.

[3]

Audit report

Every finding prioritized and scoped, with time estimates your team can plan against.

Audit Pricing

Tech Verification

Have hypothesis how AI can boost your operations?

We’ll check what is technically possible to do. Just a clear ‘yes’ or better alternatives from our core tech team.

Always free*

$0k / week

Ideal for:

Any project idea, big or small
Non-technical founders or executives who need tech validation
Businesses that are looking into AI, and how it can boost their operations

Team:

CTO - Iryna (LinkedIn)
COO - Volodymyr (LinkedIn)

Deliverables:

A clear "green light" to proceed, or suggestions for alternatives
A deliverables list for the audit

* We don’t charge for this step because we only want to help build projects we believe in. This is a true Win-Win!

AI Audit Package

We analyze your workflows and data to find where AI can create impact — then deliver a clear plan with the right tools, priorities, and timelines.

$2k-5k / 1-2 weeks

Ideal for:

Accuracy is not where it should be
Development has stalled
Maintenance costs are too high
You are planning to add AI and want to start right

Team:

CTO - Iryna (LinkedIn)
COO - Volodymyr (LinkedIn)
VP of Delivery - Stepan (LinkedIn)

Deliverables:

Full review of code, data and infrastructure
Reproducible model evaluation
Data quality assessment
Task list with time estimates
Scaling recommendations
Written audit report

Frequently Asked Questions

A written report covering: data quality assessment with specific examples, reproducible model evaluation with accuracy numbers, infrastructure and scaling findings, security review, and a prioritized task list with time estimates.

Quick wins separated from architectural fixes – so you know what to do this week vs what to budget for next quarter.
Yes. Many audits happen before any AI is built – checking whether your data is good enough, whether your architecture can handle what you’re planning, and whether AI is actually the right answer for the workflow.

Sometimes the recommendation is “don’t build AI for this – here’s a simpler approach.” Better to know before you spend.
Common patterns: data labeling errors that no amount of GPU compute can fix, prototypes built fast with no scaling plan, missing security basics (exposed API keys, no rate limits, no prompt injection protection), inefficient model choices that quietly drive up monthly bills, and architecture decisions that look fine at low load and break at scale. The audit catches issues that look small until they hit production.
Standard NDA before any access. Data stays in your environment whenever possible; when we need samples, we use your secure transfer of choice. We don’t reuse client code or data for anything outside your engagement. Same protections we use on long-term builds for Stevens Industries and Internet Archive.
The audit report is yours – take it to any vendor or hand it to your in-house team. Many clients do exactly that for at least some of the fixes. We’ll also tell you upfront if a different team is better suited for the build (e.g., niche domain expertise we don’t have). Independence is the point of an audit.