The Improvement Loop for Every Workflow

Orchestra turns production work and expert judgment into evaluations, better routes, and proprietary specialist models.

01
Observe
Production traces and expert corrections
02
Evaluate
Each task for its definition of success
03
Improve
Prompts, models, and specialist training
04
Deploy
Approved changes and a retained fallback

Use Calls You Already Make

Use existing OpenAI or Anthropic SDK with an Orchestra API key and the gateway base URL. Configure your provider or managed-model access in the app.

Open the app

OpenAI SDK Python

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ["ORCHESTRA_API_KEY"],
    base_url="https://api.understudylabs.com/v1",
)

Anthropic SDK Python

from anthropic import Anthropic
import os

client = Anthropic(
    api_key=os.environ["ORCHESTRA_API_KEY"],
    base_url="https://api.understudylabs.com",
)

Set Outcomes that Matter

Generic benchmarks don't tell you how models perform on the work that you do. Orchestra creates specialized benchmarks that show if the model updated the right record or followed your policy.

See the published results

Illustrative model comparison3 checks

Resolve an Operations Request

Check	Frontier	Orchestra Model	Generic Model
Correct Information	✓	✓	✓
Valid Structure	✓	✓	✓
Intended Action	✓	✓	×
Result	Pass3/3 passed	Pass3/3 passed	Fail2/3 passed

Implement What Moves the Work Forward

Improved Instructions

Make the task precise before shifting to a different model. Repair prompts, schemas, parsers, and tool contracts.

Head-to-Head Comparison

Compare frontier and open models on the same held-out work. Evaluate quality alongside response time and cost of the complete route.

Proprietary Specialist Weights

For a recurring task, use accepted traces and expert corrections to train a specialist. Test the resulting model against the same benchmarks as frontier or off-the-shelf models.

Keep frontier capabilities where they’re most effective. Review candidates against the baseline, introduce a new one gradually, and hold onto the prior route when evidence calls for it.

Retain the Intelligence Your Work Creates

Task-specific artifacts and trained specialists create a body of knowledge you continually improve.

Prompts: Refined instructions and task context
Evaluators: Examples, rubrics, and checks to meet a definition of success
Routing Rules: Decisions that implement a given model and keep a fallback in place
Specialist Models: Weights trained specifically to the task at hand

The Improvement Loop for Every Workflow

Observe

Evaluate

Improve

Deploy