How to Optimize Business Processes with Baseten and Infini-AI-Lab

Introduction: Why business optimization is faster with modern AI tools

The leading AI optimization tools in 2026

joey-1 is an open-weight model that outputs calibrated probabilities rather than raw text. With a latency of just 20 ms P50 and a cost of $0.01 per 1 million tokens, it’s perfect for critical real-time calculations.

Use case:Real-time risk assessment during loan approval.

Hermit trains an outer layer that manages instructions, skills, memory, and tools for modular agents. Licensed under Apache-2.0, it can be seamlessly integrated into existing pipelines without hidden costs.

Use case:Building a support assistant that retrieves documents, performs calculations, and interacts with a CRM.

AssemblyAI-TTS-1 costs $15 per 1 million characters, offers unlimited scalability, and delivers unparalleled vocal naturalness. It’s ideal for IVRs, notifications, and bilingual virtual agents.

Practical implementation: Prompts and code

Use joey-1 for risk scoring

Below is a Python snippet that sends a user profile to Baseten’s decision model and retrieves a probability.

import requests
import json

API_URL = "https://api.baseten.ai/v1/predict"
HEADERS = {"Authorization": "Bearer YOUR_KEY", "Content-Type": "application/json"}

payload = {
    "model": "joey-1",
    "input": {
        "features": {
            "credit_score": 720,
            "income": 55000,
            "employment_length": 3,
            "debt_to_income": 0.22
        }
    }
}

response = requests.post(API_URL, headers=HEADERS, data=json.dumps(payload))
print(json.dumps(response.json(), indent=2))

The model returns aprobabilityobject that you can use to approve or reject a loan application.

Create an agent with Hermit

Hermit integrates with your existing stack via adapters. Here’s an example of how to define a simple agent that reads a document and interacts with a CRM.

# hermit_agent.py
from hermit import Agent, Tool
from my_crm import CRMClient

# Define the CRM tool
@Tool
def search_customer(query: str) -> dict:
    return CRMClient().search(query)

# Initialize the agent with instructions and memory
agent = Agent(
    name="SupportBot",
    instructions="Answer customer questions and retrieve profile data.",
    tools=[search_customer]
)

# Run the prompt
result = agent.run("What is the current status of order #12345?")
print(result)

Add voice synthesis with AssemblyAI-TTS-1

When your agent needs to provide a voice update, you can convert text to audio affordably.

import assemblyai as aai

# Configure the API key (use environment variables in production)
aai.settings.api_key = "YOUR_ASSEMBLYAI_KEY"

transcript = aai.Transcript.get("your_transcript_id")
text = transcript.text

# Synthesize speech
response = aai.SpeechToText.synthesize(
    text=text,
    voice="alloy",
    model="assemblyai-tts-1"
)

with open("alert.mp3", "wb") as f:
    f.write(response.content)

Key takeaways and actionable steps

• Choose the right model for the task
• Start with an MVP
• Monitor costs
• Document prompts
• Experiment with agent iteration

Next steps for your business

1.Sign up for APIsfrom Baseten, Infini-AI-Lab, and AssemblyAI (all offer free developer trials).
2.Create a proof-of-conceptthat uses joey-1 for a simple decision task.
3.Expand the agentwith Hermit by adding internal business tools.
4.Add human voicewith AssemblyAI-TTS-1 for notifications or customer assistance.
5.Implement in productionwith the necessary monitoring and security controls.

Conclusion

The AI tool landscape in 2026 offers a powerful trio for business optimization: probability-based decisions with joey-1, modular agents with Hermit, and realistic voice synthesis with AssemblyAI-TTS-1. By using clear prompts and code snippets, you can integrate these technologies into your existing workflows and achieve advantages in speed, cost, and user experience. Start today with a proof-of-concept and transform your operational processes into a competitive advantage for the future.

Bottom line:Use these steps as a foundation, adapting tools, policies, and controls to your organization’s real-world context.

IMPORTANT: We are in 2026. All time references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (October 2026): Here are some recent trends and news to inspire you: - Prime Intellect Releases Prime Mega MoE: Trainable Fused MoE Kernel, 3.11x Faster Forward Pass: Prime Intellect released Prime Mega MoE, a backward pass for DeepSeek’s fused Mega MoE kernel. It makes the kernel trainable and speeds up Qwen3 MoE tr... [2026-10-29] - Baseten Releases joey-1, a 2B Decision Model at $0.01 per 1M Tokens: Baseten has released joey-1, a 2B open-weight decision model that returns calibrated probabilities instead of text. It runs at 20 ms P50 latency and c... [2026-10-29] - Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents: Infini-AI-Lab released Hermit, an Apache-2.0 harness adapter framework that trains an external layer of instructions, skills, memory and tools from ta... [2026-10-29] Use this current information as inspiration to create an original and relevant prompt for 2026.