Contextual AI

In August 2024, Contextual AI secured an $80 million fundraising round, increasing its worth to over $600 million. This AI technology, which is intended for corporate usage, focuses on developing systems that are extremely accurate, auditable, and controlled.  It sets itself apart by providing customized access to data and applications, guaranteeing that AI solutions satisfy certain business objectives and legal standards.  Among the main target groups are businesses looking for dependable and adaptable AI applications for their business needs.

What is contextual AI?

Contextual AI is the term for AI technologies that comprehend and interpret data to provide more precise and pertinent answers. Examples of this type of AI include user intent, the environment, past encounters, and real-time data.

Important Contextual AI Features:-

Context Awareness:- Enhances decision-making by utilizing situational variables, such as time, location, and user history.

Personalization:- Modifies answers according to each user’s choices and behavior.

Dynamic learning is the process of continuously improving comprehension through continuing encounters.

Multimodal Understanding:- Provides greater context by processing text, audio, pictures, and sensor data.

Chatbots and virtual assistants (like ChatGPT and Google Bard) that retain conversation history are instances of contextual AI in action.

• Netflix and Spotify are examples of recommendation systems that provide content recommendations based on user activity.
• Autonomous cars that modify their driving according to road conditions, traffic, and weather.
• AI for healthcare that uses patient history to recommend a course of therapy.

 Why It Is Important

 Contextual AI allows for more intelligent, human-like interactions by going beyond strict rule-based systems.  It’s a significant advancement in the development of AI’s intuitiveness and practicality.

Real-time adaptation modifies answers in response to real-time inputs (e.g., chatbots that rectify miscommunications in the middle of a discussion).

Chatbots & Virtual Assistants: the role of contextual AI

1.0 What Is Unique About Contextual AI in Chatbots?

 Due to their lack of memory and reliance on preset scripts, traditional chatbots frequently need users to repeat data.  Conversely, chatbots driven by contextual AI make use of:

Memory Mechanisms:- Customize answers by recalling previous exchanges (such as order history and preferences). 

Real-Time Adaptation:- Modify answers in reaction to real-time information (such as weather or airline cancellations).

Multimodal Understanding:- Interpret text, audio, and visuals to provide more detailed context (e.g., by examining a user’s voice or documents they have submitted).

 For instance, the chatbot Erica from Bank of America examines consumer spending patterns to provide personalized financial guidance.

2.0 Key Contextual AI Chatbot Personalization Features

 Customize answers based on user information (e.g., surfing history, previous purchases).

Sentiment analysis: Identify feelings (such as annoyance) to modify the tone or consult human agents.

Maintaining the context of conversations across platforms, for as when switching between web chat to phone, is known as cross-channel consistency. 

Anticipate requirements and provide proactive assistance by reminding users of chores based on routines. 

 3.0 Applications in Industry

• E-commerce:- Make product recommendations based on past browsing activity and outside variables like the weather (for example, H&M’s chatbot suggests ensembles). 
• Healthcare: SGH AI and other virtual assistants utilize patient histories to recommend treatments or set up appointments.
• Finance: Chatbots such as Erica offer budget advice or real-time fraud alerts.
• Customer service: Use historical interactions (like Kore.ai’s IVA) to resolve problems more quickly.

4.0 Problems and Solutions.

Data privacy: Adhere to GDPR/CCPA  and anonymize user data.

To mitigate bias, train models on a variety of datasets to prevent biased results.  

Integration Complexity: For smooth adoption, use API-based solutions (such as AWS’s generative AI tools) 

5.0 Upcoming Patterns

Emotion Recognition: Look for subtle indicators of empathy, such as hesitancy.  

Autonomous Agents: Manage intricate duties such as creating advertising briefs or evaluating sales data 6.

Voice Integration: For cohesive experiences, integrate with voice assistants (like Alexa). 

The Impact of Contextual AI 

According to Gartner, by 2030, AI will drive 85% of consumer contacts, with contextual chatbots cutting operating expenses by 30%.

Contextual AI offers businesses AI platforms that RAG-Retrieval Augmented Generation enables.  This technique, which addresses the data freshness problems with LLMs, is the foundation of the startup’s technology.

LLMs can utilize RAG to acquire pertinent real-time data from the internet and internal sources inside an organization, rather than depending just on training data.  As a result, these models are more likely to provide users with the precise responses they want and are less prone to experience hallucinations.

Contextual AI creates what they refer to as “Contextual Language Models” using RAG.  In essence, this can provide the LLM with context about the user’s question and the information that is accessible.

According to business statistics, Contextual AI’s fully integrated system provides accurate responses with “state-of-the-art” accuracy. Over the last 24 months, search interest in “retrieval augmented generation” has skyrocketed. The RAG tools market is currently worth over $1 billion and is expected to grow at a CAGR-(Compound Annual Growth Rate) of around 45% until 2030.

According to some industry insiders, hallucinated replies can be fixed in the next 12 months, even if AI-powered chatbots can have hallucinations up to 27% of the time. And a crucial component of that solution is RAG.

Trending RAG startups are; 

Voyage AI 

Voyage AI is an advanced artificial intelligence business that specializes in embedding models and rerankers to improve retrieval-augmented generation (RAG) systems. These systems are essential for increasing the relevance and accuracy of replies produced by AI.  Because it uses sophisticated semantic comprehension and domain-specific optimizations to handle important issues like hallucinations along with retrieval precision, its technology is very important for contextual AI.

In RAG systems, Voyage AI creates embedding models, which are mathematical representations of data.  Since its 2023 debut, the firm has amassed over 250 clients.  They have raised $28 million.

The ability of Voyage AI to bridge the gap between unprocessed data and trustworthy, domain-aware AI solutions is revolutionizing contextual AI.  Partnerships and MongoDB’s acquisition to incorporate these features directly into databases 9 demonstrate how important its embedding & reranking technologies are for businesses creating reliable RAG systems.  This results in quicker recollection, better contextualized outputs, and fewer hallucinations for developers.

1.0 Essential Technology and Products

 Modern embedding models and rerankers created by Voyage AI convert unstructured data, such as text, code, and legal or financial documents, into condensed numerical representations (vectors) for effective retrieval. Why is this important?

• High Accuracy:- In terms of retrieval quality, models such as voyage-3-large and domain-specific variations voyage-code-3 and voyage-law-2) perform better than rivals. 
• Word context is captured via contextual embeddings, which also capture semantic meaning (e.g., distinguishing “bank” in financial vs. river settings). 
• Low Latency/Cost:- Performance is maintained while computational overhead is decreased with optimized models; voyage-3.5-lite. 
• Long-Context Support:- Perfect for complicated documents, it can handle up to 32K tokens.

2.0 Importance of Contextual AI  

Contextual AI systems depend on AI technology in several ways.

Reducing Hallucinations:- It guarantees that AI replies are based on pertinent material by increasing retrieval accuracy, which reduces false outputs 

Domain-Specific Optimization:- Performance in specialized settings is improved by models specifically designed for the legal, financial, and coding domains (e.g., voyage-law-2 leads legal retrieval benchmarks).  

Integration with RAG:- For increased accuracy, its embeddings & rerankers (such as rerank-2) improve retrieval pipelines by fusing semantic search with precise keyword matching (using methods as Contextual BM25).

Employed by businesses like as Anthropic, Replit, & Harvey, enterprise adoption allows AI-native retrieval to be directly integrated into databases. MongoDB just purchased this technology.

3.0 Real-World Applications Code Search: 

voyage-code-3 helps developers locate pertinent snippets even with a variety of syntax by comprehending cross-language code semantics. 

Domain-specific models enhance accuracy and compliance in delicate areas in legal and financial AI.

Multimodal Retrieval: Expands contextual AI to a variety of data kinds by supporting text and pictures (e.g., via voyage-multimodal-3).

4.0 A Competitive Advantage

 Voyage AI is unique because,

 Research-backed models were created by Stanford and MIT graduates, and benchmarks indicate that they outperform alternatives by 5–15%.

Modularity: Compatible with any vector database (OpenSearch, MongoDB, etc.) and LLM (Claude 3, for example) 

Cost-effectiveness: reduced prices for heavy usage and a free tier (200M tokens)

Former Google workers founded the RAG-as-a-service company Vectara in 2022.  Last summer, the business raised $25 million, increasing its total capital to almost $73.5 million.

 A rerank model and embedding are used by Cohere, a $5 billion Canadian business, to improve the latency and maximize the accuracy of their AI models.

Summary

Contextual AI is a significant trend in technology. There is a possibility to change the shape of the business purpose.

Hope this content helps!

Read more on related topics here: AI privacy concern, Plaud AI