EdTech company, PE-financed
From Tech Due Diligence to AI Transformation in EdTech
A PE-financed EdTech company first needed a reliable technical picture, then someone to rebuild it.
| Industry | Education |
|---|---|
| Services | Fractional CTO, Technical Due Diligence, AI Transformation, Agentic Engineering |
The investor needed a solid technical picture before investing
An investor wanted to know, before investing in an EdTech company, how viable the technology really was: architecture, team, risks. After the investment, the next question came up: how does this company make more of AI than a few experiments, without putting the running business at risk.
From due diligence to target architecture and AI strategy
Bytes & Pixels first came in as an independent assessor for the tech due diligence, then as fractional CTO. The mandate: design a target architecture for the next growth phase, set up an AI transformation strategy, and change the engineering organization so it works AI-assisted, not just experiments with AI.
From due diligence to AI agents in production
Technical due diligence
Independent assessment of architecture, team, and risks for the investor.
Greenfield target architecture
New architectural framework for the next growth phase.
AI transformation strategy
Prioritized roadmap for where AI goes into day-to-day operations first.
AI agents in production
Including a support agent with access to the knowledge base.
Automated workflows
n8n- and LLM-based pipelines for recurring content work.
AI-assisted engineering organization
Toolchain, process, and quality assurance for agentic software development.
Fact base from the DD, prioritization by impact
The due diligence delivered the factual basis everything else was built on: which risks were real, where the architecture hit its limits, how the team worked. As fractional CTO, we worked directly with leadership, on a fixed rhythm, with clear decisions instead of long strategy papers.
Priority was set by impact: first the areas where AI had the biggest lever on cost or lead time, such as customer service and content production. The new architecture was built alongside ongoing operations, not as a big-bang replacement. The engineering team was trained on the new tools and ways of working so it could carry the systems forward itself after the mandate ended.
Solid technical picture, AI runs in daily operations
The investor had a reliable, independent picture of the technical risk before investing. The company now runs AI agents and automated workflows in day-to-day operations, and the engineering organization works AI-assisted as the standard, not the exception.
Carry the DD into delivery, training carries the practice
DD and execution belong together
Whoever makes the diagnosis should also be able to lead the execution. Otherwise knowledge gets lost.
Sequence beats breadth
AI transformation works when it starts where cost or lead time is under the most pressure.
Architecture runs alongside operations, not against them
A greenfield architecture must not stop day-to-day business.
A mandate without training fades out
An organization only adopts AI-assisted ways of working if it is trained for it.
A first conversation takes 30 minutes.
We talk about your situation, before or after an investment.