agentryz
agentryz™ builds custom solo-AI agent and multi-AI agent workflows with cloud computing services and agentic AI to automate marketing activities. AI agents respond to inquiries and complete routine tasks that free you to focus on higher work.
How it Works
1
The implementation process begins with defining the goals of the custom AI workflow by examining the specific process you need to automate.
2
We continue with the execution of a requirements exercise to identify all relevant roles, tasks, data and tools needed for AI to complete the workflow.
3
AI agents are provisioned and configured for sequential or parallel execution. The AI workflow is tested, then made ready for production.
4
To deploy, a familiar chatbot or full screen frontend is tailored and activated to accept user input and display useful output from the AI workflow.
Resolve routine customer inquiries with chatz, our AI chatbot
agentryz™ chatz uses a chatbot interface to facilitate two-way conversations about topics of interest. Assist, engage and resolve inquiries regarding your products and services.
- Product inquiry handling
- Customer service and support
- Appointment scheduling and reminders
- Guarantee and warranty information
- Proprietary research and insights
A Few Use Cases
Customer Support
Use a conversational agent to provide 24/7 assistance to address inquiries, effectively resolve issues, and ensure a positive experience for each customer by creating a helpful environment.
Lead Generation
Employ a multi-AI agent workflow to intake and automatically qualify lead opportunities. Filter leads by ideal customer profile criteria. Engage via digital campaigns and hand off MQLs to the sales team.
Internal Operations
Use a conversational agent to accelerate work by automating routine tasks and providing instant access to information. Free employees to focus on more strategic and creative work assignments.
MLR Review
Employ a multi-AI agent workflow to perform a medical legal regulatory review of promotional and medical materials to make sure content is accurate, balanced and in compliance with regulations. (read more →)
Full Screen Web UI
When agentryz™ is tasked with producing larger deliverables using solo-AI or multi-AI agent workflows, a full screen Web user interface is deployed to accept user input and display the final output of your digital workers.
Frequently Asked Questions
What are AI agent hallucinations and how are these minimized?
AI agent hallucinations occur when AI models generate information that sounds plausible but is actually incorrect, fabricated, or unsupported by training data. These can include made-up facts, citations, statistics, or logical inconsistencies presented with confidence.
AI agents hallucinate because of how the models that power them fundamentally work. They are trained to predict the next most likely token (word or piece of text) based on patterns in their training data, not to retrieve or verify facts. The model is essentially a highly sophisticated pattern-matching and prediction engine that generates plausible-sounding text, but it has no inherent understanding of truth and no ability to distinguish between accurate information and that which is made up. When faced with gaps in knowledge or ambiguous prompts, the model tries to complete the pattern in a way that sounds coherent and confident.
agentryz™ chatz reduces hallucinations with rigorous data preparation combined with the implementation of Retrieval-Augmented Generation (RAG) to ground responses in verified documents.
Why should we choose agentryz chatz as our conversational agent over competitors?
What separates agentryz™ chatz from competitors may come as a surprise: data preparation. It’s the familiar 80/20 rule again. 80% of AI chatbot quality comes from the quality of the data fed to it, while 20% comes from model sophistication and prompt engineering (read more →). We are data experts and bring our expertise to chatz in collecting, cleaning and arranging proprietary data to create AI chatbots. When comparing solutions, remember that the most important criteria to evaluate are the data preparation practices.
What types of data are used to train conversational agents?
agenrtyz™ chatz is trained on your internal datasets that include information from e-books, articles, case studies, white papers, website content, conference presentations, marketing materials, customer support records, internal meeting transcripts, internal helpdesk records, proprietary research, institutional knowledge sources, product specifications, product instruction manuals and more. Data file types supported include .txt, .csv, .doc, .xls, .md, .pdf and .html.
What is a multi-AI agent workflow?
A multi-AI agent workflow is a system where multiple specialized AI agents work together collaboratively, each handling specific roles and tasks, to accomplish complex objectives that would be difficult for a solo-AI agent (simple AI chatbot) to handle alone. Complex workflows are needed to produce outputs such as industry research reports, campaign insights reports and MLR reviews of medical content.
Is our proprietary data kept safe from web crawlers?
Yes, proprietary information and data remain secure within your private knowledge base and are not accessible to web crawlers. This means only your AI chatbot provides the best answers and information about your organization and its areas of expertise.
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Operations
Published on: May 12, 2026