For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Build a production LLM data extraction pipeline with AgentControl and Vercel AI Gateway
Published January 9th, 2026
by Scarlett Attensil
Newer features are available with AgentControl
This tutorial was published in January 2026, before LaunchDarkly shipped several features that extend the extraction-pipeline pattern shown below. The walkthrough still works, but for new builds you may also want to use:
Prompt snippets: Reusable prompt fragments so you can share schema definitions across configs without copy-paste drift
Offline evaluations and Datasets: Validate every prompt or schema change against a saved reference set of transcripts before promoting
Custom judges: Score the structured outputs against domain-specific quality criteria, such as field completeness or schema adherence
Every conversation contains signals your ML models need. Customer calls reveal buying intent. Support tickets expose product friction. Interview transcripts capture technical depth. The problem? Those signals are buried in thousands of words of unstructured text.
This tutorial shows you how to build a data extraction pipeline that turns messy transcripts into structured JSON - using AgentControl to control everything (models, prompts, schemas) without redeploying.
What you’ll build: A pipeline that extracts 40-60+ structured fields from any transcript - sentiment scores, engagement metrics, binary signals, text statistics - all instantly configurable through LaunchDarkly’s UI.
The key insight: When you discover that customer_question_count predicts engagement better than talk time, or that Opus 4.5 handles technical jargon better than GPT-5.2, you can update your extraction logic immediately. No PR, no deploy, no waiting.
Ready to build? Clone the complete example repository to start extracting structured data from your transcripts in minutes.
The problem with unstructured text
Your organization has valuable signals buried in text such as customer conversations, support tickets, interview transcripts, product reviews. Tools like Gong, Chorus, and conversation intelligence platforms are excellent for their designed purpose, but you need something different: extracting specific features for your ML models, with a schema you control completely.
What you typically have:
"Yeah, so we've been looking at different solutions. The other vendor's
pricing was reasonable but their timeline was concerning. We need this
│ Your Text │ ---> │ Vercel AI Gateway│ ---> │ LLM │
└─────────────┘ │ (unified API) │ │ (GPT/Claude)│
└──────────────────┘ └─────────────┘
↑ │
│ ↓
┌──────────────────┐ ┌─────────────┐
│ LaunchDarkly │ │ Structured │
│ config │ │ JSON │
│ (model, prompts, │ └─────────────┘
│ 6 tool schemas) │
└──────────────────┘
The AI model automatically selects the most appropriate extraction schema (prospecting, discovery, demo, proposal, technical, or customer success) based on the transcript content.
What stays in LaunchDarkly:
Model selection (GPT-5.2, Opus 4.5, Gemini 3)
System and user prompts
All 6 extraction tool schemas (40-60+ fields each)
Temperature and other parameters
Intelligent tool selection logic
Targeting rules for different use cases
What stays in your code:
Reading input files
Passing transcript text
Writing output CSV/JSON
Why this approach
Change schemas in 2 minutes, not 2 sprints
This separation matters when you discover issues in production. For example, if the AI model is selecting the wrong tool for certain transcript types, you can instantly adjust the prompt or model using LaunchDarkly - takes 2 minutes in the UI instead of a hotfix deploy.
Key benefits:
Instant schema updates: Add fields to any of the 6 tools when you discover new predictive signals
A/B testing models: Test Google Gemini 3 vs Anthropic Claude Opus 4.5 to see which selects tools more accurately
Smart tool selection: AI model automatically chooses between prospecting, discovery, demo, proposal, technical, or customer success schemas
Privacy compliance: Different configurations for different regions using targeting rules - GDPR-compliant schemas for European customers
One gateway, all the models
Vercel brings specific advantages for data extraction workloads:
Server-sent events for real-time progress: Processing 1,000 transcripts? Stream progress updates to your UI as each completes using the Vercel AI SDK
Automatic scaling: From 1 transcript to 10,000 - Vercel functions scale without configuration
Built-in reliability: Automatic retries and failover when LLM providers have issues
OIDC tokens on deployment: No API keys in production - Vercel handles auth automatically
Unified LLM access: One endpoint for OpenAI GPT-5.2, Anthropic Claude Opus 4.5, and Google Gemini 3 models through Vercel AI Gateway
Real-time progress showing extraction status for batch processing
Deploy once, then tune everything through LaunchDarkly while Vercel handles the infrastructure.
Where it’s defined: LAUNCHDARKLY_TOOLS.json → variation_f_customer_success
Use case: QBRs, renewal discussions, expansion opportunities
Example fields: account_health_score, renewal_likelihood, churn_risk
ML Preview: These extracted fields become features for predictive models - Part 2 will show you how to train models that predict deal outcomes, customer churn, and more using the data you extract here.
How to customize the schemas
You have two options for customizing your extraction schemas:
Option 1: Edit directly in LaunchDarkly UI
Navigate to LaunchDarkly → AgentControl → transcript-extraction-unified
Click on any tool (e.g., “Extract Prospecting Features”)
Edit the JSON schema directly in the UI - add/remove fields instantly
Save changes - they apply immediately, no deployment needed!
This is perfect for quick iterations - discover a new predictive signal? Add it in 30 seconds.
Option 2: Update via code
Edit LAUNCHDARKLY_TOOLS.json:
1
// Add a new field to prospecting tool:
2
"variation_a_prospecting": {
3
"function": {
4
"parameters": {
5
"properties": {
6
// Add your custom field here:
7
"competitor_switching_intent": {
8
"type": "boolean",
9
"description": "Intent to switch from competitor"
10
}
11
}
12
}
13
}
14
}
Re-run bootstrap to update LaunchDarkly:
$
python bootstrap/create_unified_config.py
Your extraction automatically uses the new schema - no code changes needed!
Start with 100 transcripts to validate your tools. You’ll likely learn more from the first 100 extractions than weeks of planning. Watch which tools the AI model selects and refine the prompts if it’s choosing incorrectly.
ML applications: Predict stock price movements, identify leadership confidence levels, detect financial health indicators, compare guidance to historical accuracy
Privacy and sensitive data
Add PII detection step before extraction - scan for emails, phone numbers, SSNs, names
Either redact ([REDACTED]) or skip extraction based on compliance requirements
ML applications: Route by geography using LaunchDarkly targeting - EU transcripts use privacy-safe schemas, other regions get full extraction
When to use this approach
Use this when:
Processing 100-10,000 documents monthly
Schema needs frequent iteration
Different document types need different treatment
You’re bootstrapping training data
Skip this when:
Processing millions of documents (use traditional NLP)
Schema is fixed and proven
You need sub-second latency
Documents follow strict templates
What’s next
In Part 2, I’ll show how to use these extracted features for ML models. The challenge: sparse outcomes (most deals don’t close, most candidates aren’t hired). I’ll demonstrate a zero-inflated regression approach that actually works with real-world data.
Ready to build? Start with your messiest transcripts - that’s where you’ll learn what features really matter.
