Large Language Models (LLMs) for automation and innovation

Automate enterprise processes, such as customer experience, contact centers, report generation, and others, and infrastructure with LLMs
Scale generative AI applications with LLMOps
Solve business problems in different domains with LLMs using frameworks such as LangChain
Custom train and fine-tune LLMs for domain-specific needs using Reinforcement Learning with Human Feedback (RLHF)
Design custom prompts to generate creative and informative text
Ensure the safety, quality, and structure of LLM responses using GuardRails.

Tools and accelerators:

LeapLogic: Automated cloud migration accelerator
Gathr: A cloud-native, zero-code platform for building ML-powered applications
Choice of data vectorization and other tools
Best practices for enterprise adoption of GenAI

Assessing enterprise analytics for Generative AI and Machine Learning

Assess the current state of the customer’s analytics
Modernize rule-based systems
Create roadmaps for the transition to learning-based decision-making with AI/ML
Enable ML lifecycle best practices
Recommendations to upgrade to modern technology stacks on-premise or on the cloud

Tools and accelerators:

Business alignment on use cases
Exploratory data analysis and feature engineering
Performance metrics and model validation
Solution: Cloud Cost Optimization
Solution: Schema matching for data integration

Data-powered decision-making for business transformation

Data discovery for business value
Recommend novel use cases based on data
Generate and visualize insights with user-friendly dashboards

Tools and accelerators:

Python, Power BI, and Tableau
Proactive business decisions based on generated insights
Business alignment on feature store for modeling

Machine Learning Development

Implementing algorithms for tasks represented by classic ML approach such as classification, regression, clustering, anomaly detection, time series. Developing recommender systems for personalized content recommendations. But also proficient with state of the art models both released by open sources providers and hyperscalers. . Covering both areas : NLP, Computer vision.

Natural Language Processing (NLP)

Building solutions for understanding and processing human language, including chatbots, sentiment analysis, and language translation, multilingual content moderation, topic extractions, recommender systems, complex document flow optimization and conducting operations related to diverse documentations.
Experienced in providing projects for Banking, arbitration center, insurance and retail companies.

Computer Vision

Designing and implementing Computer Vision solutions, including object detection, license plate recognition, and movement detection. Creating computer vision applications for tasks like image and video analysis, object detection, facial recognition, and image segmentation and image creation.

Developing solutions for autonomous vehicles, surveillance, and medical imaging.