Smart Content Processing: Think of this as having a super-smart assistant that can understand not just text, but also images, PDFs, and even recorded meetings. For example, when you upload a product brochure, the system can understand the images, text layout, and even extract information from tables. This means when a customer asks about a product feature, the system can find information from any part of your materials.
Smart Tagging: This is like having an organized filing system that automatically labels everything correctly. The system tags documents with relevant topics, important names, dates, and relationships. For example, if you upload sales documents, it can automatically tag them by product line, customer segment, or sales region, making it much easier to find specific information later.
Information Cleanup: Think of this as your digital organizing service. It removes duplicate content, makes sure everything is in a consistent format, and connects related information. For example, if you have multiple versions of a sales pitch, it keeps the most current one while preserving any unique examples or case studies from older versions.
Smart Information Chunking: Instead of breaking documents into random pieces, the system creates meaningful segments. For example, when processing a case study, it keeps related information together – the challenge, solution, and results stay connected. This means when someone asks about customer success stories, they get complete, coherent examples.
Knowledge Graph Construction: Imagine creating a web of connections between all your business information. The system understands that Product A is related to Feature B and Customer Segment C. When someone asks about solutions for a specific industry, the system can connect the dots between products, features, and relevant case studies to provide comprehensive answers.
Advanced Information Matching: The system uses sophisticated methods to understand the true meaning of content, not just keywords. For example, if someone asks about “increasing revenue,” it can match this with documents talking about “growing sales” or “expanding business.” This means more accurate and helpful responses to questions.
Smart Search Methods: The system combines different search techniques to find the most relevant information. It is like having multiple expert researchers working together – one looks for exact matches, another understands context, and a third double-checks everything. This means more accurate answers to customer questions.
Adaptive Search: The system breaks down complex questions into smaller, manageable parts. For example, if someone asks about “successful implementations of Product A in the healthcare sector last year,” it can separately find information about Product A, healthcare cases, and recent implementations, then combine them into a complete answer.
Results Improvement: The system personalizes search results based on what is most relevant for different audiences. For example, when a sales rep asks about a product, they get revenue and customer success information, while a technical person gets implementation details. This ensures everyone gets the most useful information for their needs.
Smart Response Building: The system thinks through answers step-by-step, just like an expert would. For example, when asked about product recommendations, it considers the customer’s industry, size, needs, and past success stories before making suggestions. This leads to more thoughtful and accurate responses.
Advanced Reasoning: The system can connect information from multiple sources to answer complex questions. For example, it can combine product specifications, pricing information, and customer success stories to build a compelling case for why a solution fits a customer’s needs.
Quality Checking: Before providing any answer, the system verifies information against trusted sources and includes references. For example, when discussing product capabilities, it links directly to official documentation or case studies. This ensures responses are accurate and trustworthy.
User-Friendly Communication: The system maintains conversation context like a good sales representative would. It remembers previous questions, understands follow-ups, and can even suggest related information you might find useful. This creates a more natural and helpful interaction.
Personal Touch: The system learns from interactions to provide better responses. For example, it can adjust its language to match your company’s terminology and adapt its responses based on whether it is talking to a CEO or a technical manager.
Easy Integration: The system can work seamlessly with your existing tools like CRM or email. For example, it can pull customer information from Salesforce while answering questions or help draft follow-up emails based on conversation history.
The Amplifi platform consists of Workspaces, Source Connectors, Datasets, Destinations and Workflows.
Workspaces: Workspaces in Amplifi provide a logical separation for teams to have specific access to data. For example, in an org, Finance can have a separate workspace which has access to financial data and is provided only to certain users.
Source Connectors: Unstructured data are present in various storage systems in the enterprise. Amplifi can connect to these source systems using source connectors and extract the files present in that source.
Dataset: A dataset is a collection of files that are ingested with a specific configuration of chunking and embedding models. This dataset is available on the platform as a set of chunks and vectors which can then be loaded on to any vector database.
Destination: A destination is a connection to a vector database that is present external to the platform. This destination could be used for other applications downstream.
Workflow: A workflow is a pipeline that can load vectors from the dataset in Amplifi to a destination at a scheduled frequency. This makes sure any new data from the source is automatically ingested and then loaded to the destination vector database
Amplifi is a RAG platform that is modular and customizable to fit to a customer’s use case. It can use local embedding models and LLMs to provide an additional layer of security and data protection. Some key differentiators are as follows:
Amplifi would continuously keep improving by adding more features to enable a wider range of use cases and improve retrieval accuracy. Some key developments would be:
Knowledge Graph Integration: Creating a knowledge graph based on the contents ingested would help improve the overall accuracy of factual question answering. A hybrid mode would get facts from the knowledge graph and context from the text to provide more context rich responses to input queries
Agentic RAG: With agentic RAG, multiple AI agents would work in tandem to retrieve, enrich and check data before the final LLM responds to the question. This would improve reasoning, enable complex workflows, and help with automating specific tasks
Connector Eco-system: Amplifi would support more connectors which can ingest unstructured data from various systems and enable contextualization for powering more use cases in the enterprise
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