Copperpot

Copperpot is a drop-in augmentation to your existing AI SDK. With no human effort or maintenance, Copperpot automatically fine-tunes dedicated models that reduce your inference costs and time to first token. Copperpot lets you effortlessly use custom models that are 21x faster than GPT-5.6-sol, with no loss in accuracy.

The Problem Copperpot Solves

Every time an AI agent makes a decision, every time it calls a subagent, every step of every flow, every time it's always the same. The entire prompt is processed, thousands of tokens spent on classification, categorization, verification, routing...

This is critical work that requires intelligent models, work that has to be repeated for every single request.

A production system processing thousands of requests daily using GPT-5.6 or Claude Opus can easily spend tens of thousands of dollars a month doing the same narrow work. What's worse, costs can suddenly balloon, leaving you footing an outsized bill without any warning.

Is There Nothing to be Done?

Use a smaller model. Lower-tier models do not match the performance of their more expensive cousins. They hallucinate, make mistakes, and your users notice the degradation in quality. In the end you've cost yourself more than you've saved.
Build your own solution. You'll need hardware, dedicated engineers, and months of work before you can even determine if the result is anywhere near what you already have. Even then, you never get back the time and money consumed.
Nothing. Pay per-token rates for the frontier model that delivers the results you need. Write off experimentation costs until you find product-market fit... and then incur rapid scaling costs as users adopt your feature.

You are paying frontier model prices to do the same work, over and over again.

How Copperpot Helps You

Lower cost. Consistent pricing at a fraction of the cost of frontier models.
Faster response times. Milliseconds for inference vs. seconds of token generation.
Intelligence. Match the performance of top-end frontier models, making agentic flows too complex for smaller models tractable.

Copperpot trains an AI model on exactly the task you're performing. Rather than start from a blank slate each time, as the frontier models must, a Copperpot model actively learns from experience, using your data to make the AI powering your product faster and cheaper. With Copperpot, your costs fall as your speed increases, and the quality remains exactly what your users expect.

Copperpot works completely transparently, in the background. You don't need any hardware, you don't need to do any maintenance, and you don't need to hire any new people. It just takes three lines of code.

Copperpot vs. Other Solutions

	Copperpot	Hosted Fine-Tuning Services Together AI, Fireworks.ai, OpenAI, Anthropic	DIY
What it is	Drop-in SDK	Hosted platforms with manual fine-tuning and inference APIs	Build your own ML pipeline from scratch
Integration effort	3 lines of code	For each new task, manually swap model versions, monitor regressions, and evaluate candidate models	Months of engineering to build and integrate
ML knowledge required	None	Moderate: understand model selection, training methods, and evals	Extensive: full ML lifecycle expertise needed
Data management	Completely automatic	Upload curated datasets in standard formats	Fully manual data pipeline
Training and model selection	Done in the background for you	Submit job via API/UI; manage evaluation and lifecycle yourself	Fully custom training loop, evals, and model lifecycle management
Cost and quality	Lower cost, frontier-matched accuracy	Higher per-token costs; quality depends on base model and data	Infrastructure costs plus risk of degraded quality
Ongoing engineering effort	Zero	Ongoing retraining, dataset updates, monitoring, and deployment	Full ownership of everything: training, serving, monitoring

Example

In this example of an application using Copperpot, a property management chatbot classifies tenant maintenance requests and then routes them through a workflow. Urgent issues get flagged to a human immediately; everything else is further handled by category-specific agents.

Traditionally, each of these decision steps would be handled by a full LLM, or perhaps by a painstakingly trained, manually constructed model. With Copperpot, you get the speed and cost benefits of the latter with the ease of use of the former.

In this example, Copperpot achieves a 21x speedup, while matching GPT-5.6-sol's output quality at a fraction of the cost.

Using Copperpot

Wrap your existing LLM client with just three lines of code. Every call gets logged, and Copperpot starts training a dedicated model. Once it's ready, Copperpot routes requests to it automatically, no developer time required.

Original

from enum import StrEnum
from openai import OpenAI
from pydantic import BaseModel

class RequestClassification(BaseModel):
    category: StrEnum("maintenance", "billing", "general", ...)
    is_urgent: bool
    is_recurring: bool

client = OpenAI(...)

result = client.chat.completions.parse(
    model="gpt-5.6-sol",
    messages=messages,
    response_format=RequestClassification,
)

return result.parsed

With Copperpot

+ from copperpot import Copperpot
  from enum import StrEnum
  from openai import OpenAI
  from pydantic import BaseModel

  class RequestClassification(BaseModel):
      category: StrEnum("maintenance", "billing", "general", ...)
      is_urgent: bool
      is_recurring: bool

  client = OpenAI(...)

  pot = Copperpot(client, "my-project", "my-task", "api-key") 

- result = client.chat.completions.parse(
-     model="gpt-5.6-sol",
-     messages=messages,
-     response_format=RequestClassification,
- )

- return result.parsed

+ return pot(messages=messages, response_format=RequestClassification)