Custom AI Solutions - Development
Comprehensive Tech Stack
Large Language Models (LLMs)
Description: Develop AI solutions powered by
state-of-the-art LLMs for natural language
understanding, generation, and reasoning.
LangChain
LangChain is a framework for developing
applications powered by large language models (LLMs).
It integrates LLMs with tools, APIs,
and workflows for enhanced functionality.
LangGraph
LangGraph is a framework for building
AI-driven applications, similar to LangChain,
but designed to manage complex workflows and
graph-based interactions with large
language models (LLMs).
Retrieval-Augmented
Generation (RAG)
Retrieval-Augmented Generation (RAG)
combines LLMs with retrieval systems to access
relevant information. This enhances accuracy and
keeps responses context-aware and up-to-date.
Conversational AI
Conversational AI refers to technologies that
enable machines to engage in human-like
conversations. It powers chatbots, virtual
assistants, and customer support systems
Generative AI
Generative AI creates new content
(text, images, video, audio) by learning patterns from
data. It powers tools like GPT, DALL·E, and deepfakes.
Knowledge Graphs &
Semantic Search
Knowledge graphs connect data through M
relationships, enabling deeper insights.
Semantic search enhances search by understanding
the meaning behind queries.
Custom Workflows with
LangChain and LangGraph
Custom workflows with LangChain and LangGraph
integrate LLMs with tools and data,
enabling tailored applications.
Speech AI
Speech AI enables machines to understand
and generate human speech. It powers
voice assistants, transcription services,
and speech-to-text systems.
NLP and Text Analytics
It involve using AI to understand,
interpret, and extract insights from text data.
They power applications like sentiment analysis,
chatbots, and document classification.
Machine Learning &
Deep Learning
It is a subset of AI that
enables systems to learn from data and
improve over time. Deep learning, a type of ML,
uses neural networks with many layers to
model complex patterns in large datasets.
Data Engineering & Processing
Data engineering and processing involve
building systems to collect, store, and
transform data for analysis. It ensures
data is clean, structured, and ready
for machine learning or analytics.
