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Jared Criteria

pro

• Apr 16

Cognigy.AI is a conversational AI platform that automates customer support and internal business workflows via chatbots and voice assistants.

IT Support Node.js Angular Python

Marta Wasilewska

• May 2

Every business has a breaking point — the moment when manual processes, repetitive tasks, and scattered data start costing more than they're worth. NeuraCloud was built for that moment. An AI-powered platform that steps in as the quiet guide — automating the complex, predicting the next move, and giving teams the clarity to focus on what actually matters.

The brand was designed to reflect that same philosophy. No noise. No corporate jargon. Just a bold, honest identity that says: we see the problem, and we know how to solve it. Because the businesses that scale aren't the ones that work harder — they're the ones that work smarter.

Completed work

Brand Design Graphic Design Marketing Strategy Adobe Photoshop Adobe After Effects Adobe Illustrator

Oleksandra Marchenko

pro

• May 4

Great job, awesome

Dipangi Agarwal

• May 3

I do have worked on various Machine Learning projects such as Customer Churn Prediction, Spam Email Detetction, Imge Classification, Sentiment Ananlyis, Customer Segmentation, Car Price Prediction, Anomaly Detection and many more. With the increating experience i am getting more interested in workign with AI Tools and Technologies. Looking for working with more interesting projects!

Machine Learning Engineering AI Development Python

Hmad Afzal

• May 3

The objective is straightforward: given a raw dialogue, the model produces a structured JSON object with topic and summary keys. This transforms unstructured conversational data into a machine-readable format suitable for downstream pipelines, databases, and APIs. Instruction-tuned models like Qwen2.5-3B-Instruct are capable of following JSON formatting instructions zero-shot, but they are inconsistent, output structure varies across prompts, edge cases produce malformed JSON, and there is no guarantee the keys will match a fixed schema. Fine-tuning on task-specific data solves this by teaching the model to reliably produce valid, schema-conformant JSON for any dialogue input. Demo: https://huggingface.co/spaces/llhax/dialog-to-json Code: https://github.com/HmadAfzal/qwen2.5-3B-Instruct-Dialog-to-json/