Laminate › Use Cases

Real problems Laminate solves

From AI pipelines to healthcare data — see Laminate in action

AI / LLM

AI/LLM Response Handling

The Problem

LLM APIs return messy, inconsistent JSON. Anthropic stringifies tool arguments. OpenAI streams fragments across dozens of SSE events. Schema changes arrive without warning. And serde crashes on the first surprise.

The Solution

use laminate::provider::anthropic::parse_anthropic_response;

let response = parse_anthropic_response(&raw_body)?;
let text = response.text();
for tool_call in response.tool_uses() {
    let (id, name, input) = tool_call.as_tool_use().unwrap();
    let query: String = input.extract("query")?;
}

Provider Adapters documentation →

DATA INGESTION

CSV/Config File Parsing

The Problem

CSV columns are always strings. Environment variables are always strings. Config files mix types freely. Every pipeline builds its own string-to-number conversion, and every one has different edge cases.

The Solution

use laminate::FlexValue;
use laminate::value::SourceHint;

// Tell laminate the data came from CSV — enables string coercion by default
let row = FlexValue::from_json(csv_row_json)?
    .with_source_hint(SourceHint::Csv);

let price: f64 = row.extract("price")?;       // "29.99" → 29.99
let active: bool = row.extract("active")?;     // "true" → true
let count: i64 = row.extract("quantity")?;     // "42" → 42

Source Hints documentation →

API INTEGRATION

REST API Consumption

The Problem

External APIs change schemas without notice. Optional fields appear and disappear. Types drift — an ID that was always a number suddenly arrives as a string. Your pipeline needs to handle what it gets, not what the docs promise.

The Solution

use laminate::Laminate;

#[derive(Laminate, Debug)]
struct ApiUser {
    #[laminate(coerce)]
    id: i64,                              // handles "123" or 123
    name: String,
    #[laminate(default)]
    email: Option<String>,                // missing → None
    #[laminate(overflow)]
    extra: HashMap<String, Value>,        // unknown fields captured, not dropped
}

let user = ApiUser::shape_lenient(&api_response)?;
// user.value has your struct; user.diagnostics tells you what was coerced

Derive Macro documentation →

DATA QUALITY

Data Quality Auditing

The Problem

You need to know what's IN your data before you can trust it. How many nulls? What types does each column actually contain? Does the real data match the documented schema?

The Solution

use laminate::schema::InferredSchema;

let schema = InferredSchema::from_values(&sample_rows);
println!("{}", schema.summary());
// Columns: id(Integer, 100% fill), name(String, 98% fill),
//          score(Float/String mixed, 95% fill)

let report = schema.audit(&new_data);
for v in &report.violations {
    println!("{}: {:?} at row {}", v.field, v.kind, v.row_index);
}

Schema Inference documentation →

HEALTHCARE

Healthcare / Lab Data

The Problem

Medical lab values use different units across countries (mg/dL vs mmol/L). Date formats vary wildly (ISO, HL7, FHIR). Identifier validation (NHS numbers, NPIs) is scattered across niche libraries.

The Solution

use laminate::packs::medical::{convert_lab_value, classify_lab_value};

let glucose_si = convert_lab_value(126.0, "glucose", "mg/dL", "mmol/L");
// Some(6.99) — US conventional to SI units

let classification = classify_lab_value(126.0, "glucose", "mg/dL");
// Some(High) — above normal fasting range

Medical Pack documentation →

DATA PROFILING

Type Detection & Data Profiling

The Problem

You have a column of strings. Some are dates, some are numbers, some are UUIDs, some are credit card numbers. You need to know what they are before you can process them.

The Solution

use laminate::detect::guess_type;

let guesses = guess_type("4111111111111111");
// [CreditCard(0.90), Integer(0.50)] — credit card wins

let guesses = guess_type("2026-04-06T14:30:00Z");
// [Date(Iso8601, 0.85)] — ISO 8601 datetime

let guesses = guess_type("$1,234.56");
// [Currency(0.90)] — US dollar format

Type Detection documentation →

Ready to get started?

Getting Started Guide → All Features →