Sentinal Pro Evolutionary AI
Budget: ₹12,500 – ₹37,500 INR
I already have a working prototype of Sentinal Pro, a self-evolving intelligent system. For the first milestone, I want to go deep on AI logic refinement—specifically, stronger automation and decision-making powered by reinforcement learning. Once that core is rock-solid we will turn to performance tuning, a futuristic UI/UX refresh, and a more modular architecture, but your immediate focus will be teaching the system to learn from its own actions and adapt in real time.
You will start by reviewing the current codebase and data pipelines, then design and implement a reinforcement-learning loop that can operate safely in production. Smooth interaction with streaming APIs and other real-time data sources is essential, so any new agent logic has to expose clean interfaces and remain non-blocking under load. I expect model and feature experimentation to be trackable, with automatic rollback or exploration controls to prevent runaway behaviour.
Deliverables
• A refactored AI module centred on reinforcement learning for closed-loop automation
• Unit and integration tests that cover decision paths and edge cases
• Documentation of the reward framework, hyper-parameter strategy, and APIs exposed
• A short demo or notebook that proves the improved decision-making on live or simulated data
Acceptance criteria
1. The new logic outperforms the current rule-based baseline on predefined business KPIs.
2. Latency for critical actions remains within existing SLA after integration.
3. All code runs inside our Docker stack and passes CI tests.
If you are comfortable pairing advanced RL techniques with robust engineering practices—and can collaborate on future UI, performance, and scalability phases—let’s talk.
You will start by reviewing the current codebase and data pipelines, then design and implement a reinforcement-learning loop that can operate safely in production. Smooth interaction with streaming APIs and other real-time data sources is essential, so any new agent logic has to expose clean interfaces and remain non-blocking under load. I expect model and feature experimentation to be trackable, with automatic rollback or exploration controls to prevent runaway behaviour.
Deliverables
• A refactored AI module centred on reinforcement learning for closed-loop automation
• Unit and integration tests that cover decision paths and edge cases
• Documentation of the reward framework, hyper-parameter strategy, and APIs exposed
• A short demo or notebook that proves the improved decision-making on live or simulated data
Acceptance criteria
1. The new logic outperforms the current rule-based baseline on predefined business KPIs.
2. Latency for critical actions remains within existing SLA after integration.
3. All code runs inside our Docker stack and passes CI tests.
If you are comfortable pairing advanced RL techniques with robust engineering practices—and can collaborate on future UI, performance, and scalability phases—let’s talk.