BYB: platformă white-label, multi-tenant, pentru aplicații de startup și evaluare asistată de AI

The venture

BYB is not a client project — it's a product Milannial Software co-owns (49%) and built end-to-end: architecture, backend, AI pipeline, admin and judge portals, infrastructure. It's the clearest possible demonstration of what "end-to-end" means here, because there was no one else to hand anything to.

The product: organizations that receive startup applications — accelerators, competitions, investment programs — run their entire evaluation pipeline through BYB. Intake, deduplication, AI pre-analysis, judge assignment, criteria-based scoring, structured feedback back to the applicants, and 1:1 session booking. White-label, multi-tenant, one codebase.

It is currently in pilot with its first users.

The problem

Anyone who has organized a startup competition knows the spreadsheet phase: hundreds of applications, a dozen volunteer judges, criteria that mean something different to each of them, conflicts of interest nobody tracked, and applicants who never hear anything back beyond "thanks for applying."

Each of those is a solvable engineering problem. Together they're a product:

• Intake at scale — a public application form with draft autosave, anti-spam, and GDPR consent, that doesn't fall over the night before the deadline.
• Fair evaluation — judges score against a versioned criteria set, not vibes; conflict-of-interest exclusions are enforced by the assignment system, not by memory.
• Feedback as a first-class output — applicants get a published feedback packet on a tokenized page, and can request a 1:1 session, instead of silence.

How the pipeline works

Inside the product

No screenshots below — what follows is the product's actual UI, rebuilt in code for this page. Scroll through it.

The architecture decisions that matter

Tenant isolation, enforced twice

Every organization's data lives in the same Postgres. That only works if isolation is unbreakable, so BYB enforces it at two independent layers: every server-side query goes through a repository that injects the organization id from a tenant context — that's the rule — and Postgres row-level security is the safety net underneath it. Cross-tenant isolation tests run in CI and block the deploy if either layer regresses.

AI is async, multi-provider, and cost-capped

AI analysis never runs inside a request. Jobs are queued on Postgres (pg-boss — deliberately no Redis to operate) and processed by a separate worker service. The AI layer is provider-agnostic: OpenRouter, OpenAI or Anthropic, switchable at runtime per organization, with cost caps so a misconfigured edition can't burn a budget overnight.

Human-in-the-loop by design

The AI produces a structured pre-analysis against the edition's criteria — it never decides. Judges see the AI's assessment next to their own scoring workflow. This is the pattern we recommend to every client shipping AI into a real decision process: the model accelerates the humans; it doesn't replace their accountability.

Trust & compliance

Startup applications contain confidential material — decks, financials, founder data. BYB treats that accordingly:

• Private deck storage with time-limited signed URLs; nothing is publicly addressable
• GDPR cascade delete — an applicant's erasure request removes their data across every table, not just the visible row
• Audit log on admin actions
• A public sub-processor list, because GDPR transparency is a feature, not a legal chore
• EU hosting (Frankfurt) end to end

Where it's going

The pilot is running with first users, and the roadmap follows the white-label promise: deeper org-level branding, richer analytics on evaluation quality, and self-serve program setup. Because criteria, prompts and forms are all versioned data, new program types are configuration — not forks.

Want a platform like this for your own intake, evaluation or review process — with AI that assists instead of decides? That's exactly the kind of system we build.