Does our source code get used to train external models?

No. We work within enterprise tiers and self-hosted gateways where prompts and code are excluded from training, and we can run entirely inside your tenancy or VPC when policy requires it.

How do you stop AI-generated code from degrading quality?

Every generated change passes the same gates as human code — peer review, static analysis, security scanning, and tests. We also track defect-escape rate before and after adoption so the impact is evidenced, not assumed.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

AI-Augmented Product Engineering

Ship more software, faster, with AI inside the delivery process itself.

Book Free Consultation All Services

35–50%

Faster feature cycle time

Test coverage on legacy modules

100%

Changes under human review

Overview

What AI-Augmented Product Engineering means at NeonAITech

We run engineering teams where AI is part of the toolchain, not a side experiment — assisted coding, automated test generation, AI code review, and documentation that writes itself. Governance, IP protection, and human review stay firmly in place, so you gain the throughput without inheriting the risk.

Tools & Platforms

Claude
GitHub Copilot
SonarQube
Playwright
GitHub Actions
Snyk

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

AI-Assisted Delivery Pods

Cross-functional squads equipped with coding assistants, generated test scaffolding, and automated review gates that measurably shorten cycle time.

Engineering Toolchain Enablement

We roll out and secure AI developer tooling across your organisation — model routing, prompt libraries, IP guardrails, and usage analytics.

Legacy Comprehension & Documentation

AI-driven analysis of undocumented codebases that reconstructs architecture maps, data flows, and specs your team can actually work from.

Capabilities

Inside the Engagement

AI pair-programming with human review gates

Automated unit, contract, and regression test generation

Code-quality, security, and licence scanning in CI

Source-code IP and data-residency controls

DORA metrics baselining and uplift tracking

How We Work

A delivery rhythm you can see into

Every AI-Augmented Product Engineering engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.