Disco

Disco

Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.

Made by Leap Laboratories.

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What it actually does

Most data analysis starts with a question. Disco starts with the data.

Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL *and* high CRP have 3× the readmission rate" — without you needing to hypothesise that interaction first.

Each pattern is:

• Validated on a hold-out set — increases the chance of generalisation
• FDR-corrected — p-values included, adjusted for multiple testing
• Checked against academic literature — to help you understand what you've found, and identify if it is novel.

The output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.

Use it when: "which variables are most important with respect to X", "are there patterns we're missing?", "I don't know where to start with this data", "I need to understand how A and B affect C".

Not for: summary statistics, visualisation, filtering, SQL queries — use pandas for those

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Quickstart

pip install discovery-engine-api

Get an API key:

# Step 1: request verification code (no password, no card)

curl -X POST https://disco.leap-labs.com/api/signup \ -H "Content-Type: application/json" \ -d '{"email": "you@example.com"}'

Step 2: submit code from email → get key

curl -X POST https://disco.leap-labs.com/api/signup/verify \ -H "Content-Type: application/json" \ -d '{"email": "you@example.com", "code": "123456"}'

→ {"key": "disco_...", "credits": 10, "tier": "free_tier"}

Or create a key at disco.leap-labs.com/developers.

Run your first analysis:

from discovery import Engine

engine = Engine(api_key="disco_...") result = await engine.discover( file="data.csv", target_column="outcome", )

for pattern in result.patterns: if pattern.p_value < 0.05 and pattern.novelty_type == "novel": print(f"{pattern.description} (p={pattern.p_value:.4f})")

print(f"Explore: {result.report_url}")

Runs take a few minutes. discover() polls automatically and logs progress — queue position, estimated wait, current pipeline step, and ETA. For background runs, see Running asynchronously.

→ Full Python SDK reference · Example notebook

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What you get back

Each Pattern in result.patterns looks like this (real output from a crop yield dataset):

Pattern(

description="When humidity is between 72–89% AND wind speed is below 12 km/h, " "crop yield increases by 34% above the dataset average", conditions=[ {"type": "continuous", "feature": "humidity_pct", "min_value": 72.0, "max_value": 89.0}, {"type": "continuous", "feature": "wind_speed_kmh", "min_value": 0.0, "max_value": 12.0}, ], p_value=0.003, # FDR-corrected novelty_type="novel", novelty_explanation="Published studies examine humidity and wind speed as independent " "predictors, but this interaction effect — where low wind amplifies " "the benefit of high humidity within a specific range — has not been " "reported in the literature.", citations=[ {"title": "Effects of relative humidity on cereal crop productivity", "authors": ["Zhang, L.", "Wang, H."], "year": "2021", "journal": "Journal of Agricultural Science"}, ], target_change_direction="max", abs_target_change=0.34, # 34% increase support_count=847, # rows matching this pattern support_percentage=16.9, )

Key things to notice:

• Patterns are combinations of conditions — humidity AND wind speed together, not just "more humidity is better"
• Specific thresholds — 72–89%, not a vague correlation
• Novel vs confirmatory — every pattern is classified; confirmatory ones validate known science, novel ones are what you came for
• Citations — shows what IS known, so you can see what's genuinely new
• `report_url` links to an interactive web report with all patterns visualised

The result.summary gives an LLM-generated narrative overview:

result.summary.overview

"Disco identified 14 statistically significant patterns. 5 are novel.

The strongest driver is a previously unreported interaction between humidity

and wind speed at specific thresholds."

result.summary.key_insights

["Humidity × low wind speed at 72–89% humidity produces a 34% yield increase — novel.",

"Soil nitrogen above 45 mg/kg shows diminishing returns when phosphorus is below 12 mg/kg.",

...]

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How it works

Disco is a pipeline, not prompt engineering over data. It:

1. Trains machine learning models on a subset of your data
2. Uses interpretability techniques to extract learned patterns
3. Validates every pattern on the held-out data with FDR correction (Benjamini-Hochberg)
4. Checks surviving patterns against academic literature via semantic search

You cannot replicate this by writing pandas code or asking an LLM to look at a CSV. It finds structure that hypothesis-driven analysis misses because it doesn't start with hypotheses.

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Preparing your data

Before running, exclude columns that would produce meaningless findings. Disco finds statistically real patterns — but if the input includes columns that are definitionally related to the target, the patterns will be tautological.

Exclude:

1. Identifiers — row IDs, UUIDs, patient IDs, sample codes
2. Data leakage — the target renamed or reformatted (e.g., diagnosis_text when the target is diagnosis_code)
3. Tautological columns — alternative encodings of the same construct as the target. If target is serious, then serious_outcome, not_serious, death are all part of the same classification. If target is profit, then revenue and cost together compose it. If target is a survey index, the sub-items are tautological.

Full guidance with examples: SKILL.md

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Parameters

await engine.discover(

file="data.csv", # path, Path, or pd.DataFrame target_column="outcome", # column to predict/explain analysis_depth=2, # 2=default, higher=deeper analysis, lower = faster and cheaper visibility="public", # "public" (always free, data and report is published) or "private" (costs credits) column_descriptions={ # improves pattern explanations and literature context "bmi": "Body mass index", "hdl": "HDL cholesterol in mg/dL", }, excluded_columns=["id", "timestamp"], # see "Preparing your data" above use_llms=False, # Defaults to False. If True, runs are slower and more expensive, but you get smarter pre-processing, summary page, literature context and novelty assessment. Public runs always use LLMs. title="My dataset", description="...", # improves pattern explanations and literature context )

Public runs are free but results are published. Set visibility="private" for private data — this costs credits.

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Running asynchronously

Runs take a few minutes. For agent workflows or scripts that do other work in parallel:

# Submit without waiting

run = await engine.run_async(file="data.csv", target_column="outcome", wait=False) print(f"Submitted {run.run_id}, continuing...")

... do other things ...

result = await engine.wait_for_completion(run.run_id, timeout=1800)

For synchronous scripts and Jupyter notebooks:

result = engine.run(file="data.csv", target_column="outcome", wait=True)

or: pip install discovery-engine-api[jupyter] for notebook compatibility

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MCP server

Disco is available as an MCP server — no local install required.

{

"mcpServers": { "discovery-engine": { "url": "https://disco.leap-labs.com/mcp", "env": { "DISCOVERY_API_KEY": "disco_..." } } } }

Tools: discovery_list_plans, discovery_estimate, discovery_upload, discovery_analyze, discovery_status, discovery_get_results, discovery_account, discovery_signup, discovery_signup_verify, discovery_login, discovery_login_verify, discovery_add_payment_method, discovery_subscribe, discovery_purchase_credits.

→ Full agent skill file

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Pricing

Estimate before running:

estimate = await engine.estimate(file_size_mb=10.5, num_columns=25, analysis_depth=2, visibility="private")

estimate"cost" → 55

estimate"account" → True/False

Account management is fully programmatic — attach payment methods, subscribe to plans, and purchase credits via the SDK or REST API. See Python SDK reference or SKILL.md.

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Expected data format

Disco expects a flat table — columns for features, rows for samples.

| patient_id | age | bmi  | smoker | outcome |

|------------|-----|------|--------|---------| | 001 | 52 | 28.3 | yes | 1 | | 002 | 34 | 22.1 | no | 0 | | ... | ... | ... | ... | ... |

• One row per observation — a patient, a sample, a transaction, a measurement, etc.
• One column per feature — numeric, categorical, datetime, or free text are all fine
• One target column — the outcome you want to understand. Must have at least 2 distinct values.
• Missing values are OK — Disco handles them automatically. Don't drop rows or impute beforehand.
• No pivoting needed — if your data is already in a flat table, it's ready to go

Supported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.

Not supported: images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV)

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Compared to other tools

Disco isn't a replacement for EDA or AutoML — it finds the patterns those tools miss. We tested 18 data analysis tools on a dataset with known ground-truth patterns. Most confidently reported wrong results. Disco was the only one that found every pattern.

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Links

• Dashboard
• API keys
• Python SDK on PyPI
• Python SDK reference
• OpenAPI spec
• Agent / MCP docs
• LLM-friendly reference
• OpenAPI spec
• OpenAPI spec (in-repo)
• [Public reports gallery](https://d