agentryz
agentryz™ builds solo-AI agents and multi-AI agent workflows with cloud computing services and AI frameworks to automate digital marketing tactics and processes. 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 goal of the AI automation and the specific manual workflow you need to transform.
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
Solo or multi-AI agents are provisioned and configured for independent, sequential or parallel execution. The new automation is tested.
4
Finally, the solo-AI agent or multi-AI agent workflow is deployed and activated to accept input prompts and respond with useful output.
Resolve 80% of customer inquiries with chatz, our solo-AI agent
agentryz™ chatz is a domain-specific AI chatbot built to respond to questions for which your organization has the best answers. Assist, engage and resolve inquiries with your data.
- Customer service and support records
- Appointment data and visit instructions
- Product information materials
- Employee handbook and guidelines
- Proprietary research and insights
A Few Use Cases
Customer Support
Use a solo-AI 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.
Business Research Brief
Create a multi-AI agent workflow to produce research briefs for new business opportunities. Quickly produce a brief about the client, its industry, offering and competitors.
Internal Operations
Use a solo-AI agent to accelerate work by automating routine tasks and providing instant access to information. Free employees to focus on more strategic and creative work.
Campaign Insights Report
Create a multi-AI agent workflow to generate campaign insights reports. We configure agents to run SQL queries against data warehouses, analyze returned data and generate insights.
Multi-AI Agent Workflows
With agentryz™ we build multi-AI agent workflows you will use to automate routine marketing tasks. Tasks that would take several days or more to complete in the past are now done in a matter of minutes using these “digital colleagues.”
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 solo-AI 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 solo-AI 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 briefs and campaign insights reports.
What technologies do you use to build solo-AI agents and multi-AI agent workflows?
agentryz™ chatz, our solo-AI agent chatbot, is powered by AWS (Amazon Web Services). AWS is used to build, deploy and maintain enterprise-grade agents which are eligible for SOC 2 Type 2, GDPR, ISO 27001, and HIPAA compliance. SQL and Python scripts are used to manage and prepare data loaded to your chatz knowledge base. Third-party chatbot interfaces are integrated into solutions as business situations require.
We build multi-AI agent workflows using AWS services and Python AI frameworks.
Can the design of agentryz components be customized?
Yes, all solo-AI and multi-AI agent components can be customized to match your organization’s branding guidelines.
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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Published on: December 15, 2025
