API Integrations

Last year marked a transformative phase for our technology, as we introduced a feature that allowed custom analysis of Call Log transcriptions. This breakthrough enabled our CRM users to unlock new potentials, pushing the boundaries of what we imagined was possible. From evaluating the quality of customer service calls against rigorous ISO standards in mere seconds to generating insightful funding notes for Housing Counseling and enhanced summary notes for Customer Service interactions, the impact was profound. Users leveraged our technology to not only analyze the text but to delve deeper, extracting meta-data to spotlight outliers and streamline business processes. The capability to pose complex, multi-layered questions and receive high-quality insights from the GPT-4 model was both surprising and incredibly empowering.

Building on this momentum, we’re thrilled to introduce a groundbreaking enhancement: the deep integration of Large Language Models directly into the workflow process through Tasks. This new development is designed to not just augment, but revolutionize how tasks are analyzed, automated, and acted upon within your organization. Imagine having the ability to dynamically generate AI-driven insights and actions directly from routine tasks, all tailored to enhance efficiency and decision-making.

We are eager to see the innovative applications you’ll dream up with this new capability. Your creativity and feedback are what drive us forward, and we’re committed to evolving this technology together.

New Feature Overview: AI Task Analysis Engine

The AI Task Analysis Engine is designed to seamlessly integrate AI capabilities into organizational workflows by allowing tasks to trigger dynamic AI prompts. These prompts, defined in DocumentTemplates, are then evaluated by a Large Language Model. The results of this evaluation are stored in the ClientMessages table, providing a rich dataset for analysis and further action. This feature includes several key components:

• AI_PROMPT_TASK DocumentTemplate: A new type of document template that allows the creation of dynamic AI prompts.
• Dynamic AI Prompt Evaluation: Upon task creation or completion, an AI_PROMPT_TASK document is sent to a Large Language Model for evaluation.
• Enhanced ClientMessage Table: Modifications to the ClientMessage table enable the storage of AI Task Analysis results, adding a new dimension to client communications.
• Expanded Smarty Variables: Inclusion of ClientMessage and CallLog records in Smarty variables, enabling the creation of AI prompts based on client interactions.
• ClientMessageType Field: A new field in the ClientMessage table to distinguish between different types of messages, including AI-generated messages.

Benefits and Applications

The AI Task Analysis Engine is a versatile tool that can be leveraged in various ways to benefit an organization. Here are some examples:

Enhanced Reporting

Organizations can use AI to analyze client interactions and communications, generating comprehensive reports that highlight trends, preferences, and areas for improvement. For instance, AI can identify common issues raised by clients in secure messages or emails, allowing businesses to address these concerns proactively.

Automated Decision Making

By evaluating AI_PROMPT_TASK documents, the AI Task Analysis Engine can assist in automating decision-making processes. For example, it could analyze financial data to recommend Debt Management Plan strategies or assess client satisfaction levels to suggest customer service improvements. This automation speeds up decision-making and ensures it is backed by comprehensive data analysis.

Custom Client Communications

With the integration of ClientMessage and CallLog records into Smarty variables, organizations can automate the creation of customized client emails based on the results of prior analysis results. These emails can provide focused recommendations based on an individual’s financial situation and geographic location, enhancing the personalization of client communications. For example, this feature could highlight available housing assistance programs in the county of the client by performing realtime web searches to identify programs that best fit the financial circumstances of the client.

Streamlined Operations

The ability to store AI Task Analysis results in the ClientMessage table ensures that all client-related information is easily accessible. This centralization of data streamlines operations and improves the efficiency of client communication management.

Conclusion

The AI Task Analysis Engine represents a significant step forward in integrating AI into business processes. By enabling dynamic AI prompt evaluation and the automated creation of personalized client communications, this feature not only streamlines operations but also enhances the quality of client interaction and decision-making. As organizations continue to seek ways to leverage technology for competitive advantage, the AI Task Analysis Engine stands out as a tool that can transform data into actionable insights and tailored communications, paving the way for a more efficient and responsive business model. As AI models continue to advance and we move beyond multi modal generative models, we will continue to update the available tools so that you can leverage cutting edge technologies. All these changes are motivated by the intention to help you expand your capability to help our community.

We are happy to announce an enhancement to our Client Support Software. Our CRM now supports the integration of Peregrin’s API AccountStatus refresh, a feature that promises to streamline your financial data management and improve the accuracy of your account status information.

How to Activate This Feature

To take advantage of this new capability, you will need Peregrin API keys. We understand the importance of a seamless setup process. Therefore, we encourage you to reach out to us. Our team will handle the request for these keys on behalf of your organization, ensuring a smooth and efficient integration. There is no additional cost for utilizing this functionality either from Client Support Software or Peregrin.

Utilizing the AccountStatus Refresh

Once this feature is activated, you can easily update Creditor records that are linked through Peregrin. You simply need to input the Peregrin Creditor ID or enter ‘1’ to enable the functionality.

Upon activation, a new Peregrin Refresh link will automatically appear next to eligible ClientCredit records within the DMA/Creditors tab of our CRM. This link is your gateway to up-to-date financial data.

The Peregrin Refresh Link – Keeping Your Data Current

By clicking on the “Peregrin Refresh” link, you can initiate a balance update for your ClientCredit records. This feature is particularly valuable because if the information provided by Peregrin is more current than the data already in the system, the ClientCredit record is automatically updated with the latest balance information.

We are committed to continuously improving our services and offerings to meet your evolving needs. We look forward to seeing how this update will positively impact your organization’s data management processes.

For any questions or assistance in activating this feature, please do not hesitate to contact us.

We are excited to announce a new feature for our CRM customers: Custom AI API Endpoints. This feature allows you to use your own AI models or open source alternatives to the ones provided by OpenAI, depending on your needs and preferences.

Custom Endpoints can help you save money on AI costs while enhancing your data privacy. You can run your own AI models on your own hardware, without sending any data to third-party services. For example, you can use the open source Whisper model to transcribe call recordings using the same API as OpenAI. Whisper is a large language model that can run on a consumer GPU with 11GB of RAM.

Large language models are powerful AI tools that can generate natural language texts for various purposes. They are trained on massive amounts of data and learn to capture the patterns and nuances of human language. As the size of these models increases, they can exhibit emergent capabilities that go beyond their original training objectives. For example, some large language models can answer questions, write summaries, generate code, and even create music or art.

However, running large language models requires a lot of computational resources, especially video RAM (VRAM). VRAM is the memory that is used by the GPU to process graphics and other tasks. The more VRAM a GPU has, the larger and more complex models it can run. For example, a 30 billion parameter model requires 24GB of VRAM, which is available in some high-end consumer GPUs. As of June 2023, recent models of this size are comparable to GPT-3.5, one of the most advanced models from OpenAI. By combining multiple GPUs, it is possible to run even larger models with more emergent capabilities.

Open source large language models are advancing at a rapid rate, thanks to the efforts of researchers and developers around the world. As our customer, you have the option of using OpenAI’s models or hosting your own open source models. As hardware capability inevitably improves and prices come down, you will be able to run more powerful and privately hosted models that can easily integrate with your existing customer data.

The future is bright and full of possibilities as we leverage these technologies in service of society. If you’re interested in exploring this option, please contact us for more details. We hope you enjoy this new feature and we look forward to hearing your feedback.

We are excited to announce a new feature for our Client Support Software: Gmail integration within the Client record. This feature allows you to view all the emails related to a client from your Google Workspace account without leaving the CMA. You can also filter out irrelevant or sensitive email addresses by using exclusion patterns. Here’s how it works:

• To enable this feature, you need to have a Google Workspace Service level account with Domain Wide delegation. This allows our software to access your emails on behalf of your users. Please contact us so that we can set it up on your behalf.
• After enabling the feature, you can go to any Client record in the CMA and the relevant Gmail messages associated with the Client email will be displayed in the Info tab, under the Email Activity section. You can click on any email summary to see the full text message in the CMA.
• The Email Activity section is updated asynchronously, so there is no performance penalty when loading the Client record.

We hope this feature will help you improve your communication with your clients and streamline your workflow. If you have any questions or feedback, please contact us at support@clientsupportsoftware.com.

Following up on the addition of Call Log Sentiment Analysis, we’ve added the ability perform transcription analysis within Client Support Software’s CRM and OpenAI’s language models. This feature allows you to create and use AI prompts for Call Log Transcription Analysis, which can help you gain insights into your customer conversations and optimize your service delivery.

How can AI help you with Call Log transcription analysis?

Call Log transcription analysis is a process of extracting insights from the transcripts of phone calls. It can help you understand the needs, preferences, and feedback of your customers, as well as identify areas for improvement in your products or services. AI can help you with Call Log transcription analysis by automating the transcription of audio files, applying natural language processing techniques to analyze the content and sentiment of the conversations, and generating reports that summarize the key findings and trends. AI can also help you with Call Log transcription analysis by providing recommendations and suggestions based on the data, such as how to improve customer satisfaction, retention, and loyalty, or how to optimize your sales and marketing strategies.

AI prompts are questions or commands that you can use to interact with a large language model (LLM), such as OpenAI’s GPT and get relevant and useful responses. For example, you can use an AI prompt to ask an LLM to summarize the main points of a Call Log transcription, or to suggest actions or solutions based on the customer’s feedback.

By using Document Templates, you can create AI prompts that are tailored to your specific Call Log transcriptions and your desired outcomes. You can also use variables and placeholders to make your AI prompts dynamic and adaptable to different situations. For instance, you can use a variable to insert the customer’s name or previous communication history into your AI prompt, or you can use a placeholder to indicate where you want the LLM to fill in some information.

What are some examples of AI prompts for Call Log transcription analysis?

Here are some examples of AI prompts that you can use or modify for your Call Log transcription analysis:

• In one sentence, describe what the customer wanted and how the agent resolved the issue.
• Write a brief summary of the call, including the customer’s problem, the agent’s solution, and the customer’s satisfaction level.
• How would you summarize this transcription for a manager who wants to know the main outcome of the call?
• Analyze the sentiment of the customer and the agent during the call:
• Using a scale from 1 (very negative) to 5 (very positive), rate the sentiment of the customer and the agent at different stages of the call.

• Identify the emotions that the customer and the agent expressed or implied during the call, and explain how they affected the communication.
• What was the overall tone of the call? Was it friendly, professional, angry, frustrated, etc.? How did it change over time?
• Suggest improvements or best practices for the agent based on the Call Log transcription:

• Based on this transcription, what are some things that the agent did well and what are some areas that they could improve on?
• Give three specific feedback points for the agent, along with examples from the call and suggestions on how to implement them.
• How could the agent have handled this call more efficiently or effectively? Provide some concrete tips or recommendations.

How can diarization be performed based on the conversational context?

Diarization is the process of identifying who is speaking when in a multi-speaker audio recording. Diarization can help you segment your Call Log transcriptions into speaker turns and assign labels or names to each speaker. This can make your transcriptions more readable and easier to analyze.

One way to perform diarization based on the conversational context is to use an LLM that can recognize speech patterns, cues, and features that indicate speaker changes. For example, an LLM can use contextual clues such as pronouns, names, roles, topics, etc., to identify speakers based on their content. A prompt can be used to ask the LLM to perform the diarization of the transcription. If there are more than two speakers, a prompt like “Perform diarization on the following phone conversation that may include speakers such as [IVR, Voicemail, Agent, Client]”.

Adding new Prompts

Under System Management/Document Templates, add a New Record, select Type = AI_PROMPT_CALLLOG

Enter the prompt using plain text and optional dynamic Smarty variables available within the CMA

Analyzing Call Log Transcriptions with the Prompts

A Call Log record can be viewed from within the Client record or from within the Call Log Detail report. In both cases, when viewing the individual transcription, the predefined prompts are available for selection. They will populate the prompt text area which can be further customized for a personalized analysis.

Once the desired prompt is written, submit the record, and analysis is performed. The resulting. analysis is now available to view either as a popup when hovering over the Call Log notes or within the Call Log record. The prompt that is used, is stored with the response so that the response can be understood within its context. For example:

We hope that this help your organization provide constructive feedback to assist counselors who in turn assist clients. Please contact us for implementation questions.

We are happy to announce a new feature for Client Support Software’s CMA: Call Log Sentiment Analysis. This feature builds on yesterday’s addition of Call Log Transcription to provide sentiment analysis using OpenAI’s text-davinci-003 model. In this post, we will explain what sentiment analysis is and how it is useful to have in a CRM.

Sentiment analysis is the process of identifying and extracting the emotional tone and attitude of a speaker or writer from their words. It can help you understand how your customers feel about your products, services, or interactions with your agents. Sentiment analysis can also help measure customer satisfaction, loyalty, and retention.

With Call Log Sentiment Analysis, you can now automatically analyze the sentiment of your call transcripts using OpenAI’s text-davinci-003 model. This model is one of the most advanced natural language processing models available today. It can handle complex and nuanced language expressions and generate accurate and consistent sentiment scores.

Call Log Sentiment Analysis works by assigning a score between 1 and 5 to each call transcript. A score closer to 1 indicates a very negative sentiment, while a score closer to 5 indicates a very positive sentiment. In addition, the top three emotional tones are identified next to the sentiment score.

You can access Call Log Sentiment Analysis from the CMA Call Log Detail Report. You will see an emoji-coded display of your call transcript with the sentiment scores for the entire conversation. You can also filter your call logs by sentiment score using the “Sentiment” filter in your call log list.

Call Log Sentiment Analysis can help you improve your customer service and sales performance in many ways. Here are some examples:

• You can identify unhappy or dissatisfied customers and take proactive actions to resolve their issues or offer them incentives.
• You can identify happy or satisfied customers and ask them for referrals, testimonials, or reviews.
• You can monitor the quality and effectiveness of your agents’ communication skills and provide them with feedback or training.
• You can discover insights into your customers’ needs, preferences, pain points, or objections and use them to improve your products or services.
• You can track trends and patterns in customer sentiment over time and across different segments or regions.

We hope you enjoy this new feature and find it useful for your business. We are always working on improving the CMA to provide you with the best tools for customer support and sales. If you have any questions or feedback about Call Log Sentiment Analysis or any other feature, please contact us.

Client Support Software has integrated OpenAI’s text-to-speech API known as Whisper in order to optionally transcribe call recordings for customers that use our PBX hosting product. This feature will increase productivity and benefit CMA users by allowing them to quickly review the transcription of a conversation and extract key insights.

Whisper is a state-of-the-art speech-to-text model that can transcribe audio files in any of the supported languages into text . It can also translate and transcribe audio files into English. Whisper has received immense praise from the developer community for its accuracy and speed.

With Whisper integration, CMA users can easily access the transcription of any call recording using the Call Log reports. They can search for keywords, filter by date, duration, caller ID, or extension, and export the transcripts as CSV files. They can also listen to the original audio file while reading the transcript.

This feature will help CMA users to:

• Improve customer service by identifying pain points, feedback, and satisfaction levels from call recordings.
• Enhance training and coaching by reviewing call transcripts and providing feedback to agents.
• Save time and resources by avoiding manual transcription.

In order to enable this feature, your company should open an account with OpenAI to obtain an API key. Existing customers may contact us to enable this functionality. We hope you enjoy this new feature and we look forward to hearing your feedback.

We are excited to announce that Client Support Software’s CMS for housing counseling agencies, has been approved by the U.S. Department of Housing and Urban Development (HUD) as a HUD certified Housing Counseling CMS, compatible with the Agency Reporting Module version 6 (ARMv6) specification.

This means that the CMS meets HUD’s standards for collecting, storing, and reporting data on housing counseling services provided by HUD-approved agencies. Agencies using Client Support Software’s CMS can easily transmit their data to HUD’s Housing Counseling System (HCS) using XML format, without any manual entry or conversion.

CMA is designed to help housing counselors deliver high-quality and efficient services to their clients. With Client Support Software, you can:

• Manage client intake, assessment, action plan, follow-up, and outcome tracking
• Generate customized reports for internal and external use
• Automate reminders, notifications, and referrals
• Integrate with third-party applications such as credit reports, messaging services, and online education platforms
• Access it from any device with an internet connection

Client Support Software’s CMS is also affordable and flexible. You can choose from different pricing plans based on your agency size and needs. You can also customize the CMS to fit your agency’s workflow and preferences.

If you are interested in learning more about how our CMS can help your Housing Agency, please schedule a demo by clicking on the Schedule a Demo button on our website.

We look forward to helping you achieve your housing counseling goals with Client Support Software!

The CMA is now integrated with the USPS API to perform city, state or zip lookup.

To lookup City and State by Zip, the Client’s City field must be blank. Enter a zip code and then change focus to the City field by either tabbing to it or clicking on it. The city will automatically be populated.

 

To lookup Zip, the Client’s Address, City, and State fields must have a value. Give focus to the Zip field. If a value is found, the 5+4 zip code is populated.