Implementation

How to Run an AI Code Review POC: A Step-by-Step Walkthrough

An AI code review POC is where vendor claims meet your actual codebase, workflows, and constraints. This chapter covers how to prepare the evaluation, what to watch during the trial, and how to turn the results into a clear go/no-go decision.

Filip Hric

August 11, 2026 • 8 min read

What You’ll Learn

• How to assess whether your organization is ready for a POC, and when not to start one
• How to map AI code review onto your existing tool stack before the trial
• The readiness gate: the checklist to clear before kickoff
• A week-by-week walkthrough of the trial: what you will actually see, and what to do about it
• How to handle false positives, overlapping tools, and metric disputes during the trial
• How to run the decision at the end, including cost and scaling behavior

The Five Stages of an AI Code Review POC

The trial itself is 2 weeks. The full journey is five stages, and the first two happen before any tool touches a repository.

Stage 1: Assess Fit Before You Talk Timelines

Start with the gap, not the tool

Before scoping anything, answer one question in one sentence: what is failing in your review process today? These are the problems that actually bring engineering teams to an AI code review POC,  each one leads to a different trial with different success criteria:

If the diff-only vs. full-context distinction behind several of these criteria is new, the AI Code Review chapter covers what review tools actually analyze. The tools comparison chapter covers how to shortlist before a POC.

Then map your stack

AI code review does not land in a vacuum. Write down what is already touching your code before the vendor call:

• Source control. Your Git provider (GitHub, GitLab, Bitbucket, Azure DevOps). This determines the integration path
• Existing review layers. An AI assistant already commenting on PRs (Copilot), SAST scanners (SonarQube, Snyk), and your human review process. Every one of these will interact with the trial tool
• Issue tracker. Jira or similar. Ticket linking feeds requirement context into reviews, and integration friction here is one of the most common early-POC time sinks
• IDEs and coding agents. IntelliJ / VS Code mix, and whether developers work with coding agents in the terminal. This determines whether shift-left (IDE-stage) review is in scope for the trial or a later phase
• Communication tooling. Slack, Teams, or neither allowed for external vendors. Decide the shared-channel question early; at some enterprises it needs its own approval

Your organization profile determines your path

The same POC does not fit every team:

Stage 2: The Readiness Gate

Security review is the long pole: start it on day one

Technical connection usually takes a day or two, and most of that work is on the vendor’s side. Security approval takes weeks because it includes SOC 2 reports, penetration test results, data handling documentation, and, in stricter environments, deployment model decisions. Request the vendor’s trust center package on the first call and run the review in parallel with everything else.

Do not start the trial until every box below is checked. If you cannot check them all, you are setting up a black-box POC, the kind that ends in “we didn’t really see the value” instead of a decision.

Why black-box trials fail, in Qodo’s own words

It’s difficult when we run these POCs to have the classic black-box testing, where a customer might just take it and evaluate it as they perceive the tool. Nine out of ten times that doesn’t work – because we built it, we’re the experts. So we’re trying to figure out the best way to engage with the team.

Qodo sales engineer, on what separates an engaged POC from a black-box trial

Pre-POC checklist

• The gap is written down in one sentence, and success criteria are drafted from it
  
  Example: “PRs in our platform repositories wait an average of two days for first review, and agent-generated PRs have grown past what our senior reviewers can cover.” The full worked version (with the five criteria drafted from it)  is in the box below.
• Security review started: trust center documents requested and handed to infosec
  
  Example: on the first vendor call: “Please send your SOC 2 Type II, latest pen test summary, and data handling documentation” – then forward the package to infosec the same day.
• Deployment model confirmed with infosec (SaaS / self-hosted / hybrid)
  
  Example: SaaS for most teams: it is the fastest path to value. A healthcare, finance, or public-sector team may require self-hosted, or a hybrid where only metadata leaves the environment. Get infosec’s answer in writing before kickoff, not during week one.
• A named internal owner assigned: one person accountable for engagement and feedback
  
  Example: a staff engineer or engineering manager who runs a 15-minute weekly trial standup, chases feedback in the team channel, and is the vendor’s single point of contact. A title is not required; accountability is.
• 10–15 developers identified for the soft launch, and told what is coming
  
  Example: a two-paragraph message before day one: what the tool does (comments on PRs, does not block merges), why the team is trialing it, and where to drop feedback.
• Trial repository selected: active, but not release-critical
  
  Example: an internal services repo with roughly 15–30 PRs per week that is not in the current release train. Not your busiest production repo, not a dead one.
• Existing-tool plan agreed: which tool owns which feedback while both run
  Example: “Copilot keeps its security autofix comments; the trial tool owns logic, cross-repo, and standards feedback. Both run for the full three weeks; we compare per-PR at the end.”
• Git integration permissions scoped; Jira linking requirements listed
  Example: read access plus PR comment permissions on the trial repo only,  not organization-wide. For Jira: base URL configured, then verified with one test PR carrying a ticket link in its description.
• Communication channel decided (Slack Connect, Teams, or scheduled check-ins) and approved if needed
  Example: Slack Connect keeps each company out of the other’s workspace, which satisfies most policies. Where any external channel needs its own security approval, book a fixed weekly 30-minute check-in instead and don’t let the channel question delay kickoff.
• Vendor commercial questions answered: pricing model, admin seats, marketplace purchasing, cost at scale
  Example: confirmed in writing: per-seat or usage-based, whether admins cost extra, marketplace availability, plus a cost estimate extrapolated to your full developer count, so stage 5 has a number to score against.

The deal structure to insist on: the POC is free, success criteria are agreed upfront, and the purchase conversation happens only after those criteria are met. No commitment during the trial.

Must-have vs. varies by organization

Not every item on the list carries the same weight. These are non-negotiable for any POC:

The vendor questions to settle at scoping

Stage 3 – Week 1: Soft Launch

One active repo. 10–15 developers. A kickoff session, not an announcement email. Here is what the first week actually looks like and what to do at each point.

Week 1, day by day

Day 1–2: connect and configure

Git integration goes live; the vendor does most of this. Three settings to confirm before the first PRs flow:

1. Automatic re-analysis on every push is enabled (this is a group- or project-level setting, not a default everywhere)
2. Rules files (best-practices and compliance definitions) are in place at the right level
3. Jira linking works.

Test it by putting a ticket URL in a PR description and confirming the connection.

Example: in Qodo, all three are day-one items: re-review on every push is a configuration option at the group or project level, rules live in the rules portal and in best-practices files alongside your code, and Jira linking is verified with a ticket URL in the PR description.

Day 2–5: the first comments arrive – and so do the first false positives

Expect them. Every real POC hits this in week one. What separates a productive trial from a stalled one is the response:

• The wrong response: “the tool is noisy,” raised in a check-in with no examples attached. That is a sentiment, and nothing can be tuned from it
• The right response: collect the specific false positives, then categorize them: is the finding wrong, or is it correct but missing context your codebase would provide? The two categories have different fixes: the first is model feedback for the vendor, the second is solved with rules files, documentation ingestion, or ticket context

Day 5: agree on metric definitions

Before anyone quotes a number, align on what it measures. In one real evaluation, the customer read a ~20% implementation rate off the dashboard while the vendor measured 50% – because one was counting a subset of findings and the other counted all findings implemented. Neither number was wrong; the definitions were different. Settle the definition in week one so the decision in week three is about results, not measurement.

Example: a workable definition to agree in writing: “Implemented = a finding whose suggested change appears in a subsequent commit on the same PR, whether applied via the tool or written by hand. Acknowledged-but-deferred findings count separately.”

What you’ll see in week 1 – and what to do about it

Stage 4 – Weeks 2–3: Rollout and Tuning

Expand deliberately

Add repos and developers based on what week one surfaced, not everything at once. Configuration should now reflect real feedback: custom rules created from your team’s actual conventions, documentation ingested for context, noisy categories tuned down.

Run a rules workshop

A recurring pattern in real trials: developers give positive feedback but admit they “haven’t played with the rules enough.” Rules are where the tool stops being generic and starts enforcing your standards. Teams consistently under-invest in them during trials. A dedicated working session with the vendor on rule creation, scoped to your actual conventions, is the single highest-value hour of week two.

Example: rules worth writing in that hour: “Every new API endpoint validates input through our shared validator – flag raw request access in handlers.” / “Database access goes through the repository layer; flag direct queries in service code.” / “PR titles start with the Jira project key (e.g. PLAT-1234).” Specific, checkable, and yours – not generic best practices the tool already knows.

Watch the engagement number, not just the findings number

A trial with forty connected repositories and three participating developers produces no decision. If attendance at feedback sessions is thin, that is the internal owner’s problem to fix this week, through standups, surveys, or direct follow-up, not a data point to note in week three.

By week 3 the questions change – that is a good sign

Mature trials shift from “does it work” to “how does it fit our workflow”: can developers see issues in the IDE before opening a PR, does it understand dependencies across repositories, can findings feed back into coding agents, can it learn from production incidents. When your team starts asking workflow-fit and roadmap questions, you have enough signal to move to the decision.

Stage 5: The Decision

Score the trial against the criteria from stages 1–2. The ones that hold up under scrutiny are specific and comparative:

If your AI infrastructure is metered: make cost-at-scale a stage-one criterion

The situation (from a real Qodo POC): an enterprise routing all AI traffic through a metered internal gateway, with model access behind approvals.

The move: cost-at-scale went into the stage-one success criteria: token budgets, model costs, and gateway capacity sized for PR-scale traffic before kickoff.

What it protected them from: a scaling surprise arriving as the trial’s verdict. Qodo’s model mix is engineered for cost (efficient models on high-volume paths, premium models on review reasoning), so the trial produced a credible full-rollout extrapolation.

Apply it to your own evaluation: if model access is metered or approval-gated, score cost-at-scale as part of the decision, not as a footnote.

The differentiation criterion carries the most weight

Nearly every enterprise evaluating AI code review already has an AI assistant in the workflow. The decision question is never “does the new tool find issues”. It is “does it find the issues the existing tool misses, without duplicating what’s already flagged.”