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Smart Farming Advisor — Agentic AI
The Smart Farming Advisor is an end-to-end agentic AI system that empowers the agriculture sector with data-driven crop recommendations and accurate plant disease detection. Utilizing an intelligent agentic routing system for autonomous intent classification, the platform seamlessly integrates computer vision models (ResNet50) and machine learning classifiers (Random Forest). It features a comprehensive RAG pipeline with semantic search for farming Q&A, bridging a Next.js 14 frontend with a Flask REST API backend. This project emphasizes my ability to orchestrate multi-tool AI assistants and deliver high-impact predictive modeling solutions.
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Heron AI is an agent that reviews architectural models and flags building code problems. The hardest part of the brief was making that legible in the first few seconds.
We built the hero as a small interaction instead of a statement. The visitor drags across a black and white building sketch and violations surface where they drag, annotated with real IBC clauses. It puts the person in the agent's position for a moment.
Everything after that section can then talk in normal product language, because the core idea has already landed without needing to be described.
Really smart way to make a technical AI capability tangible—the drag interaction lets the user discover the value instead of reading a claim. The architectural visual language supports that clarity beautifully.
One of our clients needed 70 ad video variants in a week, so we built an AI agent to do it in seven hours.
The build itself was a project. It required:
- Assembling a Claude agent connected to Remotion
- Building a knowledge base of the client's brand: brand guide, movement language, hook creation guidelines, past deliverables, briefing structure
- Teaching the agent to read the brief the way our team would
- Composing hook variants adapted for each aspect ratio (1x1, 9x16, 16x9)
- Assembling endcards and outputting files named and organized for QA
The first batch still needed heavy manual editing. The second, the agent assembled alone.
The third came out fully stitched (hook, body, CTA, disclaimer) needing only a review pass before shipping.
Each iteration meant testing what the agent got wrong, adjusting the skills, refining the knowledge base, and rerunning until the output matched the standard our team would ship.
When we sent the batch, the client feedback was: "The revised videos look great. We'll start using them immediately."
To show what this workflow can do, the video in this post was produced by this exact system.
If your business needs to ship ads at this volume, let's talk 👉 apta.agency/contact
70 ad variants in seven hours is wild. remotion?
FarmiAi logo design
FarmiAi is an AI powered agricultural platform designed to make farming smarter and more accessible. I led the visual design from concept to execution, creating a clear, modern identity and user experience that makes the platform easy to understand and engaging to use.
Making an AI farming platform approachable is a useful design goal. Did you test the identity within the actual interface while developing the mark?