Case Studies

Real-time wildlife detection and video tagging using computer vision. Outcome: Improved animal classification accuracy by 94%, enabling faster tagging for their live streams.

NLP-powered Healthcare intelligence assistant. Outcome: Reduced manual research time by 60%, improved their team productivity.

Generative AI-powered smart assistance for property tracking. Outcome: Enabled real-time real estate summaries using LLMs and multimodal inputs.

A mid-sized hotel chain wanted to improve customer experience by making room booking and in-room service requests faster, more personalized, and available 24/7. They implemented an AI-powered virtual assistant integrated into their website, mobile app, and in-room devices.

The AI HR Interview Chatbot is a GPT-4–powered agent that simulates an HR representative during initial candidate screening. It dynamically adapts its questions and tone to reflect a company’s culture (corporate, startup, or neutral), stores full transcripts for later review, and evaluates candidate responses against a scoring rubric derived from the job description. Integrated via API or embeddable widget, it enables seamless volume hiring and pre-screening on career pages or within ATS platforms.

The project focused on building an AI system that generates lip-synced animations of animal images based on audio inputs. Traditional lip-syncing models such as **MuseTalk** are trained primarily on human facial data, limiting their ability to generalize across non-human subjects. To overcome this limitation, the MuseTalk model was fine-tuned on curated datasets of animal faces, enabling natural and expressive lip-sync animations of animals speaking in synchronization with audio recordings.

We built an AI-powered system that allows users to generate unique, visually appealing stickers based on simple text prompts. Instead of requiring full design specifications or manual effort, users only need to describe their desired sticker in a few words, and the AI handles the rest—resulting in faster, cost-effective design workflows without sacrificing quality or creativity.

The project focused on building a **multi-intent AI chatbot** for an automobile client with a large knowledge base (~100 GB of car manuals). The chatbot was designed not only to answer user questions through **Retrieval-Augmented Generation (RAG)** but also to handle **service scheduling** and **service offers discovery**. The solution combined **custom query routing, RAG with Gemini 2.0 Flash, web scraping from authorized dealers, and Firestore-based session management** to deliver a complete conversational assistant for car owners and service seekers.