For EdTech companies and institutions that need it to actually ship.
We build the software learning runs on — LMS, student information systems, adaptive-learning engines, and admin automation that gives teachers time back. Engineered for real enrollment loads and the systems schools already run.
Where AI helps, it assists the educator, not replaces them. Fixed scope, one accountable lead, full IP to you, production in 4–8 weeks.
Because the constraints are unforgiving and unlike most B2B software. Usage spikes on a calendar everyone shares — the morning a term opens, the night an assignment is due — and a platform that buckles takes a whole cohort down with it.
The data is sensitive and regulated: student records sit under FERPA in the US, COPPA for under-13s, and GDPR for any European learner, so a sloppy integration is a compliance incident, not just a bug.
And education software never works in isolation — a new tool has to talk to the student information system, the single sign-on, the rostering feed, and the gradebook an institution already depends on, or the people who administer it reject it.
The buyers are split, too. The institution wants outcomes, security, and something its team can actually run. Education software development that ignores either buyer ends up shelved — most EdTech fails to land for want of engineering and integration discipline, not ambition.
Education software development isn’t one product — it’s a set of systems that each earn their place by fixing a specific, repeated burden. For each: what it does, the benefit, and how it plays out.
Course delivery, assignments, grading, discussion, and progress tracking in one place — built to your pedagogy instead of bent around an off-the-shelf tool. Benefit — higher engagement and less time lost to clunky workflows. Content, submission, and feedback live in one flow rather than three.
For example, an instructor posts an assignment, collects submissions, and returns graded feedback without exporting to a spreadsheet and re-importing — reclaiming the evenings that workaround used to cost.
The system of record for enrollment, scheduling, attendance, transcripts, and reporting — wired to the feeds and compliance reports an institution lives on. Benefit — administrative hours collapse and records stay accurate.
For example, an enrollment change updates the roster, the gradebook, and the attendance record at once, so the registrar isn’t fixing three systems by hand at term start.
Content that adjusts to each learner’s pace and gaps — sequencing the next lesson from how they actually performed on the last one, with the teacher seeing exactly where each student stands. Benefit — students spend time where they need it, and teachers get a clear signal of who’s stuck.
For example, a student missing one prerequisite concept is routed back to it before moving on, while the dashboard flags the five learners a teacher should check on first — so intervention happens before a unit test, not after.
Automated rostering, attendance, routine grading, progress reports, and parent communication — the repetitive paperwork that crowds out teaching. Benefit — time returned to instruction.
For example, objective-question grading and a first-draft progress comment are generated for the teacher to review and approve in minutes, instead of a weekend of manual marking — with the educator always the final sign-off.
Testing, item banks, proctoring workflows, and dashboards that turn raw scores into something an educator or administrator can act on. Benefit — earlier, better-grounded decisions about who needs help and what’s working.
For example, a cohort dashboard surfaces a concept the whole class missed days after the quiz, so the next lesson can re-teach it rather than the gap compounding silently.
A grounded assistant that helps with practice, explanation, and feedback — built to support the tutor or teacher, not to grade unsupervised or stand in for them. Benefit — more support reaches more learners without removing the human. We keep these deployments conservative and human-in-the-loop by design.
For example, an AI assistant suggests a guiding question for a live tutor to ask a struggling student — the approach in Stanford’s randomized trial below — rather than handing the student an answer.
The scope below is the difference between an education platform that survives a term and one that gets uninstalled after the first enrollment spike.
Custom LMS, SIS, course-authoring, and learner-facing web and mobile apps, built full-stack in-house to your pedagogy and brand — not assembled from a template you’ll outgrow.
We connect to the systems institutions already run — SIS feeds, single sign-on (SAML/OAuth), rostering and data standards (LTI, OneRoster, QTI), payment and gradebook systems — so adoption clears the administrators who would otherwise block it.
FERPA, COPPA, and GDPR considerations are designed into data handling, access controls, and audit logging from the start — not retrofitted after a review. Every data path is documented so your team verifies rather than trusts.
Recommendation and sequencing logic, item banks, and the reporting dashboards that turn raw activity into decisions an educator can act on — with the teacher’s view first-class, per the evidence above.
Where AI genuinely helps — drafting feedback, summarizing progress, powering study support or an education assistant — we build it human-in-the-loop, grounded in your own content, educator as the reviewer of record. We’ll also say where AI doesn’t belong.
Load engineering for term-start and deadline spikes, monitoring, and the runbooks and training that let your team operate the platform after we step back.
What you get when you hire us — all assigned to you under full work-for-hire IP transfer
The same delivery model behind all our software and AI work, tuned for education — one accountable lead, fixed scope, no handoffs.
Scope the use case, the integrations and compliance requirements, and the academic outcomes you’ll judge it on — run as our AI/software readiness assessment, with the honest “don’t build this” call included.
Output: a ranked plan & the success metrics
Architecture, data model, and integration map; where AI is involved, the evaluation set and the human-in-the-loop checkpoints.
Output: a build plan & the interoperability/compliance design
Develop in your own cloud tenant, wired to your SIS, SSO, and rostering through governed integrations, with access controls and audit logging in place.
Output: a working platform behind your controls
Pilot with a real classroom or cohort, then widen; adoption and outcomes measured against the kickoff targets, your team trained to operate it.
Output: a production platform & a team that owns it
Most engagements reach production in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff.
We’ll be direct about where our deepest first-party proof sits: our flagship engagements are in restaurants, sports tech, and marketplaces — not a named EdTech client we can put on this page. What carries over is the engineering discipline education software depends on, and we won’t dress adjacent work up as something it isn’t.
The reliability problem education faces at term start — a multi-site, software-critical operation that cannot go down — is one we’ve solved for years. We hold a 200+ location restaurant business (BJ’s Restaurants, an adjacent multi-site reliability engagement, not an education client) at twice-a-week releases with zero critical defects across four years, using the same evals-before-launch, staged-rollout, monitor-after discipline an institution needs through enrollment season.
And the longevity problem — a platform that has to keep working and modernizing for years without going offline — is one we’ve lived: we carried Bridge Athletic (an adjacent long-lived platform, now used by USC, the LA Rams, and MLB and MLS teams) from a 2012 startup build through twelve-plus years of modernization, never offline.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. The same person scopes your work and answers for it.
Four reasons buyers choose us for education software development over a generalist shop or an off-the-shelf product.
The human-led stance matches the evidence. The data above says education technology works best when it supports the educator. That’s our founding charter, not a feature we bolted on — which is why our AI in classrooms assists and escalates rather than replacing the teacher.
Interoperability and compliance are treated as the product. SIS/SSO/rostering integration and FERPA/COPPA/GDPR-aware data handling are designed in from day one — the parts that decide whether administrators adopt or reject a tool.
Built to survive the calendar. We engineer for the enrollment and deadline spikes that take other platforms down, with the same reliability discipline that holds a 200-location operation steady.
Founder-led, one accountable lead, built to transfer. No account managers, no handoffs; code, evals, and IP are assigned to you, and your team is trained to run and extend the platform when we step back.
What teams want to know before they commission education software.
All three, with the same engineering discipline and different constraints. K-12 leans on COPPA, parent communication, and district SIS integration; higher ed on enrollment systems, LTI/SSO, and scale; EdTech companies on a product that differentiates and a platform that survives growth. We scope which of those constraints actually bind your project before we build.
By designing for it from the start, not retrofitting. Access controls, data minimization, audit logging, and documented data paths are built into the architecture; the platform runs in your own cloud tenant under your controls; and every engagement begins with an NDA and a security review. Education data is sensitive and regulated, and we treat it as such rather than treating compliance as a checkbox at the end.
That’s a core part of the work, not an afterthought. We integrate through the standards institutions run on — SAML/OAuth single sign-on, LTI, OneRoster, QTI, and direct SIS/gradebook feeds — because a tool that doesn’t fit the existing stack gets rejected by the people who have to administer it. We map the required integrations in the discovery phase.
Only in a human-in-the-loop way, and we’re conservative about it. The independent evidence is clear — Stanford’s randomized trial found the gains came from AI assisting human tutors, and McKinsey’s PISA analysis found technology in teachers’ hands outperforms student-only use. So we build AI that drafts feedback, suggests guiding questions, and summarizes progress for an educator to review and approve — never an unsupervised grader or a replacement for the teacher.
Because we’d rather be straight than overclaim. We don’t have a named education case study, and we won’t dress our restaurant or sports-tech work up as one. What we do bring is the exact engineering education depends on: multi-site reliability that holds a 200+ location operation at zero critical defects, and twelve-plus years keeping a platform modernizing in continuous production. Founder Kelvin Tran is personally accountable for the result.
You do — completely. Code, data models, evaluation suites, and documentation transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most engagements reach production in 4–8 weeks under a fixed-scope model with one accountable lead and payment tied to ROI. Build cost depends on scope — our development cost guide gives real ranges — and we model ongoing run cost (hosting, any AI token economics) before building, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the platform, the integration list, and the outcome you’re after — we’ll tell you honestly what it takes to build, where AI belongs and where it doesn’t, and what it costs to run.