LMS decisions, AI integration, compliance architecture, engineering hires. $5,499/mo Advisory, $9,499/mo Fractional CTO.
- Audit
- Architect
- Scale
monthly retainer
Who this is for
You're an edtech founder, school operator, or corporate L&D head carrying platform decisions alone. Product strategy, vendor contracts, and engineering quality all land on your desk, usually without a senior technical partner who understands how education products actually get built and used.
The pain today
- No clear owner for platform and architecture decisions
- LMS vendor lock-in risk with no technical vetting
- Compliance gaps: FERPA, COPPA, or GDPR exposure nobody has mapped
- AI features requested by investors or customers but no plan to build them
- Engineering team or freelancer shipping code that will not scale to 10,000 concurrent learners
The outcome you get
- Fractional edtech CTO at $5,499 to $9,499/mo with no full-time overhead
- LMS decision made with confidence in 30 days: white-label, standard, or custom
- Compliance posture mapped and vendor contracts tightened
- AI integration roadmap: personalization, assessment automation, or admin AI
- First engineering hires levelled, interviewed, and onboarded
Why edtech founders hire a fractional CTO
Three situations where fractional CTO for education makes the most sense.
First: the edtech founder who raised seed without a technical co-founder. Product is being built by contractors, no one owns architecture, and investors are starting to ask about the tech plan. Second: an established school or training provider launching a digital product for the first time, where nobody in the org has shipped software at the scale they're targeting. Third: a corporate L&D team building an internal learning platform, trying to choose between custom development and an off-the-shelf LMS with no senior technical voice in the room.
In each case, fractional CTO at $5,499 to $9,499/mo delivers the leadership without the cost or timeline of a full-time hire.
Fractional edtech CTO at $5,499 to $9,499/mo with no full-time overhead
LMS strategy: build, buy, or white-label
This is the decision that shapes the next three to five years of an education product. Get it wrong early and the cost of correction compounds fast.
White-label LMS (Teachable, Thinkific, Kajabi) works when content is the product and the platform is commodity infrastructure. For most consumer edtech founders and course creators, white-label handles 80 percent of needs at a fraction of the build cost. Standard LMS (Canvas, Moodle, Docebo, LearnUpon) fits schools, universities, and corporate L&D where pedagogical features and institutional integrations matter more than differentiation. Custom LMS is right when the platform mechanics themselves are the competitive advantage: novel assessment logic, proprietary cohort structures, adaptive learning paths that off-the-shelf tools cannot model.
The default answer is: do not build custom. Custom LMS adds 6 to 12 months to your timeline and requires ongoing engineering investment most pre-Series A edtech companies are not staffed to support. I help you make this call with full visibility into your learner scale, your content model, and your three-year product roadmap.
3 weeks: From kickoff to investor demo.
AI integration for education platforms
Investors and customers are asking about AI. The question is which AI features actually improve learning outcomes versus which ones are cosmetic.
The integrations that deliver real value in education: adaptive difficulty engines that adjust content sequencing based on assessment performance, AI-powered assessment grading for short-answer and essay formats, admin automation for scheduling, reporting, and learner communication, and retrieval-augmented content search so learners find the right material without instructor intervention. I have built AI-powered systems with OpenAI and Claude that process large structured datasets and surface insights at speed. That experience translates directly to edtech AI work.
For most edtech founders in 2026, AI integration is a 60 to 90 day project layered onto an existing platform, not a full rebuild. The right architecture decision made now avoids an expensive AI retrofit later.
Compliance architecture: FERPA, COPPA, and GDPR
Student data is some of the most regulated data in any industry. Getting the architecture right from the start is faster and cheaper than retrofitting compliance after a school procurement team hands back your vendor questionnaire.
FERPA applies to US schools and their vendors handling student education records. COPPA covers any product collecting data from users under 13 in the US. GDPR applies to learners in the EU. These three often overlap for international edtech products. I scope the compliance posture that fits your actual learner population: which regulations apply, which vendor contracts need data-processing addenda, and what architecture decisions protect you if you scale into a new market.
I also evaluate tooling vendors for data-handling posture. LMS, video, analytics, and community tools each carry data risk that rarely shows up in the sales conversation.
Vendor management and your first engineering hires
Education operators collect vendors fast. LMS, video hosting, payment processing, community platform, analytics, support tooling. By month three of most engagements, the stack audit reveals 30 to 50 percent overlap or outright redundancy.
Consolidation reduces monthly cost and reduces the surface area for integration failures. For founders building on custom infrastructure, the hiring plan matters as much as the architecture plan. I scope the first 3 to 5 engineers with clear role definitions, levelling benchmarks, and compensation guidance calibrated to your market and stage. At W2O I led 15 developers across 30-plus customers; at GigEasy I was the first engineer hired, shipping an investor-ready MVP in 3 weeks for Barclays and Bain Capital-backed founders. Both contexts inform how I approach building a team from scratch in edtech.
Pricing and when a full-time CTO makes more sense
CTO Advisory at $5,499/mo covers 1 to 2 days per week: platform strategy, vendor decisions, compliance framing, and hiring input. For operators who already have technical capacity and need a senior strategic voice. Fractional CTO at $9,499/mo covers 3 days per week: team leadership, architecture decisions, hands-on execution alongside your engineers. For founders without a senior engineer on staff.
Both tiers include a 14-day money-back guarantee. Cancel anytime. Nonprofit discount available for registered 501(c)(3) education organizations.
For edtech companies post-Series A with engineering teams of 10 or more, a full-time CTO handles day-to-day leadership better than a fractional engagement. Fractional bridges the gap from pre-seed through the first meaningful engineering hires. When the time comes, I help with the full-time CTO search: interview loops, levelling, and onboarding support.
Recent proof
A comparable engagement, delivered and documented.
Built and shipped an investor-ready MVP from scratch
Built the entire technological base and delivered MVP in just 3 weeks, enabling a successful rapid launch and investor demo.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Default to existing LMS unless the platform mechanics themselves are the competitive product. Build custom when cohort structures cannot be modeled in available tools, assessment logic is novel and proprietary, or the platform itself is what you are selling. Skip custom when content is the value and the platform is just delivery. For most edtech founders, white-label LMS plus a custom front-end handles 80 percent of needs. Custom adds 6 to 12 months and ongoing engineering cost that compounds quickly.
FERPA applies to US schools and their vendors who handle student education records. As a vendor, you are typically a school official under FERPA and subject to the same data-handling obligations. Practically, that means documented data retention policies, limited data sharing, and contract language covering data processing. For products targeting under-13 users in the US, COPPA adds a consent layer. I map the applicable regulations against your actual product and learner population rather than applying a one-size checklist.
I look at five factors: pedagogical fit with your delivery model, scalability against your projected concurrent user load, API quality and data export flexibility, pricing structure at your target learner volume, and the vendor's data-handling posture for student records. For higher education, Canvas and Blackboard dominate. For corporate L&D, Docebo, LearnUpon, and Absorb. For consumer edtech, Teachable, Thinkific, and Kajabi. The right answer depends on your scale, compliance requirements, and how much platform control matters to your product strategy.
Yes. The AI integrations that tend to deliver measurable outcomes in education: adaptive content sequencing, automated assessment grading for open-format questions, admin and reporting automation, and AI-powered content search. I scope which of these fits your current platform and learner data quality, then define the architecture and integration approach. Most AI additions to an existing edtech platform are a 60 to 90 day project, not a rebuild, if the data layer is clean.
Yes. For edtech founders raising, I attend technical due diligence calls, architecture reviews, and pitch meetings where technology is on the agenda. I prepare on expected questions beforehand. My experience at GigEasy, where the MVP shipped in 3 weeks for Barclays and Bain Capital-backed founders, gives investors a concrete reference point for technical execution. Typically 2 to 4 meetings across a raise cycle.
Monthly cash at the published rates is my standard arrangement. For pre-seed edtech founders with constrained cash, reduced cash plus a small equity component can be discussed case by case. Equity-only is not something I take. A clean cash arrangement keeps the engagement professional, easy to adjust, and simple to exit if the business changes direction. For founders raising in the next 6 months, a straightforward cash structure typically signals maturity to investors reviewing the cap table.