We created this project to streamline viral-vector manufacturing by connecting the entire lifecycle—from client intake to regulatory document delivery—within a single AI-powered workflow. The platform unifies business development, scientific, manufacturing, quality control, and QA teams, ensuring every stage is connected through a traceable digital process. The system evaluates submitted documents, identifies missing information, summarizes project requirements, assesses manufacturing feasibility, predicts production yields, supports process simulation, automates QC and batch documentation, and streamlines review and release approvals. With workflow gates, portfolio-level dashboards, and end-to-end traceability, the platform helps teams improve collaboration, reduce manual effort, accelerate project delivery, and maintain regulatory readiness.
Batch Record Generator
The Writer Finder module answers a question every documentation PM faces once a project is approved: who is the right person to do the work, and are they actually available? The interface surfaces a live talent pool with status breakdowns—idle, partially available, busy, and on leave—so staffing decisions start from reality, not guesswork. Writers are ranked using AI match scores that weigh experience, quality ratings, weekly availability, on-time delivery, and fit with the project’s industry and document type. Filters for language, service line, document type, and availability status help narrow a long list quickly, while “Top 5 only” and shortlist views support focused decision-making. Each recommendation card explains why a writer was suggested—for example, deep fintech experience or seniority aligned with project complexity—and offers clear actions: shortlist, compare side by side, view full profile, or assign directly to the project. This turns writer selection from a manual review exercise into a transparent, data-informed staffing workflow.
AI Machine Log Analysis
The Quote Generator module transforms a completed client intake into a structured commercial proposal using a visible four-step generation pipeline. First, the system retrieves similar accepted quotes from the knowledge base to ground pricing and scope in past work. Next, it loads service-specific rate cards and project size tiers. It then synthesizes commercial terms—payment schedules, validity windows, and scope assumptions—before Open AI composes the full quote draft with sections, line items, and deliverables. For a project like Blue River Fintech with intake marked complete and a strong audit score, the PM sees delivery targets, language requirements, and readiness signals at a glance before clicking Generate quote. The simulation-style pipeline makes the AI process legible to clients and internal stakeholders, not a black box. Typical generation takes 30–60 seconds, producing an editable draft that can be refined, sent for client approval, and linked forward to writer assignment—closing the loop from sales to delivery.
Technologies Used
- Platform: React 19 +Next js
- AI Models: Google Gemini + Claude Opus
- Database: Firebase — Auth, Firestore, Cloud Storage
- Infrastructure: Vercel Edge Network
- Industries: Biotechnology
