AI systems built for complex, real-world ops
Deployments across enterprise AI, analytics, healthcare, creator commerce, and high-precision navigation. Each project is structured for secure access, scalability, and measurable impact.
What the platform solved
Here are examples of systems where automation, orchestration, and scalable architecture created measurable impact. These case studies show how orchestration, RBAC, and modular system design unlock reliable outcomes for complex workflows. Each build is engineered to turn data into action, not just analysis.
Fello
AI-Powered Project Planning & Management Workspace
Fello is a premium, AI-assisted project management workspace that translates natural language project descriptions into structured, interactive Scrum/Kanban boards in real time.
Teams waste hours translating high-level requirements into structured Jira-style backlogs. Generative AI tools typically output raw text or malformed JSON that leaks into user interfaces, leaving tasks disconnected from real execution environments.
Avernus engineered a custom streaming parser that filters JSON plan definitions from live chat streams and upserts them to MongoDB. The frontend displays an interactive Kanban board with drag-and-drop support, a persistent Plan Lock state, and an on-demand AI Task Drawer that generates custom specs, QA test plans, and technical steps per task card.
- Zero-dependency streaming Markdown bubble renderer
- Live stream parser to intercept and suppress raw JSON payloads
- Interactive Kanban board with independent column scrolling
- AI Project Companion drawer (Technical Spec, Test Cases, Tech Steps)
- Encrypted LLM API credentials & dynamic model listing badges
- Workspace-wide Lock Plan state persisted to localStorage
- Transitioned from experimental internal R&D to a production-ready application
- Reduced initial project setup time from hours of manual entry to seconds
- Prevents raw JSON payload leaks in streaming UI via custom buffer parsers
- Provides developers with instant technical context directly inside task cards
Cerebro
Multi-Agent AI Orchestration Platform with RBAC
Cerebro is an AI orchestration system built to classify intent, delegate tasks across specialized agents, and generate structured outputs securely within a role-controlled environment.
Organizations needed AI systems that can understand multi-intent queries, route to domain agents, enforce strict access control, and output structured data beyond plain text.
Avernus engineered a LangGraph-based pipeline that routes requests through intent classification, module delegation, and synthesis, enforced with role-based access control.
- Intent classification engine
- Multi-agent routing
- Automated chart generation
- Secure RBAC
- Context-aware caching
- Modular AI task delegation
- Enterprise-grade control over AI access
- Reduced latency via caching
- Structured outputs for reports and charts
IR Hub
Institutional Research & Analytics System
A centralized internal analytics suite aggregating dashboards, course evaluations, and institutional research metrics under secure role-based entry points.
The institution needed centralized dashboards, role-separated access, automated evaluation reporting, and reduced manual report generation.
Avernus built a secure analytics platform that aggregates institutional data, automates reporting, and presents dashboards with strict role separation.
- Role-based dashboard access
- Automated evaluation reporting
- Centralized academic analytics
- Secure internal infrastructure
- Eliminated manual reporting workflows
- Improved administrative decision-making
- Centralized institutional data systems
Psychiatric Hospital Management System
Healthcare Workflow Automation
A full-stack hospital management platform automating patient lifecycle management, pharmacy operations, and accounts tracking under strict access controls.
The hospital required unified patient records, real-time pharmacy inventory tracking, IPD/OPD separation, and role-restricted access to sensitive data.
Avernus delivered a modular system covering IPD, OPD, pharmacy, and accounts, all synchronized with centralized data and access hierarchy.
- Real-time patient intake tracking
- Automated inventory updates
- Secure access control
- Cross-module synchronization
- Reduced administrative overhead
- Increased operational visibility
- Improved patient data security
Creteva
Creator Economy Marketplace & Live Commerce Platform
Creteva is a high-performance creator ecosystem bridging artists and fans through real-time streaming, exclusive content feeds, duplex chat channels, and dynamic digital/physical product storefronts.
Creators are forced to stitch together separate platforms for live streaming, direct messaging, paywalled feeds, and commerce storefronts. This results in fragmented user experiences, high transaction overheads, complex API integration maintenance, and a lack of automated real-time safety classification for user-generated media and text.
Avernus engineered a unified dark-mode web client using Next.js 16, React 19, and Tailwind CSS v4, backed by a high-efficiency Fastify 5 API gateway. The platform integrates Mux for low-latency live streaming with Socket.io duplex presence updates, utilizes Stripe Connect for direct creator-fan subscriptions, and features real-time text/image safety moderation powered by the Google Gemini AI SDK.
- JSON schema-validated Fastify REST routes & middleware hook cycles
- Low-latency HLS livestream playback lobby and interactive viewer rooms
- Duplex WebSocket direct messaging & typing indicator presence stores
- Multistep custom onboarding flows and creator profile configuration
- Gemini-powered real-time base64 image and text NSFW moderation gateways
- Dynamic analytics metrics dashboards displaying revenue using Recharts
- Consolidated streaming, commerce, and community into a single platform
- Sub-second content safety classification checks via Gemini AI integration
- Enabled direct creator-to-fan monetization with secure payouts via Stripe Connect
- Enhanced viewer engagement with real-time sockets and concurrent React 19 rendering
Orientation Feedback Chatbot
RAG-Based Insight Extraction System
A retrieval-augmented generation chatbot designed to extract qualitative insights from large-scale university feedback datasets.
The university had extensive feedback data but lacked structured analysis, insight extraction, and conversational access.
Avernus built a RAG pipeline that indexes feedback documents, retrieves relevant context, and synthesizes AI responses with structured summaries.
- Context-aware feedback analysis
- Insight extraction via natural language
- Structured AI summaries
- Faster qualitative analysis
- Reduced manual review workload
- Improved administrative insight access
AccuNav
High-Precision Localization System
A geolocation system leveraging SUPARCO's PakRehber PPK service to achieve sub-meter positional accuracy.
Standard GPS solutions lacked sufficient precision for engineering-grade applications.
Avernus developed a configuration management system that integrates PPK correction data to enhance accuracy and visualize coordinates in real time.
- Sub-meter positioning
- Correction data processing
- Real-time visualization
- Improved engineering-grade navigation
- Reliable correction-based tracking