fxis.ai
visit site- $1,000+
- 50 - 249 employees
- Ahmedabad, India
Client Insights
Industry Expertise
10%
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10%
10%
10%
10%
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Client Size Distribution
Small Business (<$10M) 15%
Midmarket ($10M - $1B) 50%
Enterprise (>$1B) 35%
Common Project Size
$10K-$49K 17 projects
<$10K 16 projects
$50K-$199K 8 projects
Clients
- Assam Government
Highlights from Recent Projects
Fxis.ai completed a project for an online fashion retailer. The company was hired to explore the potential of AI in supporting product content workflows. Fxis.ai developed a model to analyze product images and descriptions, suggesting simple attribute tags like color, fit, and material. This automated approach aimed to reduce manual effort and understand the long-term value of AI. The team delivered a prototype, an API layer for testing, and a lightweight dashboard for reviewers to validate and calibrate model outputs. The project was a learning-focused engagement that was well-received.
For a staffing solutions company based in Dubai, fxis.ai developed a chatbot for their website. The chatbot provides information about the company and its services, fetches data from users, and offers solutions. It supports English, Arabic, and Hindi. Fxis.ai developed a model based on the company's dataset, creating a chatbot for the HR domain and staff augmentation. The chatbot provides real-time AI solutions. The company has also begun a new project with fxis.ai for web development and a customizer solution. The engagement, which began in August 2024, is ongoing and the investment is between $5,000 and $10,000.
Fxis.ai was hired by a global professional services organization to develop an AI engine. The engine was designed to support audit and compliance teams by automating the extraction of key information from large volumes of documents. The goal was to reduce manual review time and improve consistency in data capture across engagements. The scope involved creating an AI engine capable of reading financial statements, policies, contracts, and compliance documents, and extracting predefined fields required for audit and risk assessments. The team delivered a custom extraction model, a dashboard for reviewing extracted data, and an API to integrate the engine into the company's internal workflow. The project was well-received and considered a success.