> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sutro.sh/llms.txt
> Use this file to discover all available pages before exploring further.
# Sutro
> Build AI Functions from expert feedback, then run them at scale.
# Core Capabilities
Sutro turns repeated expert decisions into reliable **AI Functions**. Upload
representative data, review the cases where models disagree, and optimize a
prompt against your feedback.
## Learn More
Create a judge, classifier, or extractor from your data and decisions.
Execute a published Function over a production list, table, or file.
## What you can build
* **Evals and judges** for agent traces, model responses, conversations, and
other quality gates
* **Classifiers and routers** for leads, tickets, documents, policy checks,
ownership, prioritization, and escalation
* **Extractors and enrichment Functions** that turn text, images, and PDFs into
typed fields, labels, and normalized attributes
* **Matchers and resolvers** for companies, people, products, incidents, and
other records
* **LLM-enhanced decisioning** that augments classical ML systems for fraud
detection, KYC review, spam and abuse prevention, risk scoring, and
eligibility decisions with detailed instruction-following and unstructured
context
These can be binary decisions, one-of-many or multi-label classifications,
rubric-based judgments, or structured extractions. A Function works best when
an expert can consistently review its output and when it owns one narrow step
in a larger workflow.
## Run your Function
When the Function is ready, you can efficiently run it offline and at scale
using Sutro Batch. You can also copy its optimized prompt and model
recommendation along with its output schema into an OpenAI-compatible provider.
Sutro is best for repeatable decisions with outputs that can be labeled or
verified. It is not intended for latency-sensitive chat or unconstrained
long-form generation.