Supported Domains

Misata detects your domain from the story and applies a pre-built schema with realistic FK relationships, domain-calibrated distributions, and semantic column types. Every domain generates data that passes referential integrity checks out of the box.

How detection works: each domain has a set of trigger keywords. The domain whose keywords appear most in the story wins. The literal domain name (e.g. "saas", "fintech") scores +5 points — always enough to override ambiguous keyword matches.

---

SaaS

Trigger keywords: saas, subscription, mrr, arr, churn

Tables: users, subscriptions, invoices

Table	Key columns
users	user_id, name, email, plan, signup_date, country, is_active
subscriptions	subscription_id, user_id, plan, mrr, start_date, status, churned_at
invoices	invoice_id, subscription_id, amount, invoice_date, status

Distributions: MRR is lognormal (median ~$150/mo). Churn rate configurable from story. churned_at is only set for churned subscriptions. Plan distribution: free 55%, pro 30%, enterprise 15%.

Narrative support: MRR outcome curves — monthly anchors, quarterly patterns, multipliers, and named events all work on the subscriptions.mrr column.

import misata

# Basic
tables = misata.generate("A SaaS company with 5k users and 20% monthly churn")

# With narrative curve
tables = misata.generate(
    "SaaS startup — MRR from $50k in Jan to $200k in Dec, Q3 slump, doubled by year end",
    rows=5000, seed=42
)
print(tables["subscriptions"][["mrr", "start_date"]].head())

---

Ecommerce

Trigger keywords: ecommerce, e-commerce, orders, cart, store, retail, shop

Tables: customers, products, orders, order_items

Table	Key columns
customers	customer_id, name, email, city, country, signup_date, lifetime_value
products	product_id, name, category, price, cost, stock_count, rating
orders	order_id, customer_id, amount, status, ordered_at, shipped_at, delivered_at
order_items	item_id, order_id, product_id, quantity, unit_price, discount

Distributions: Order amounts lognormal (median ~$85). shipped_at is always after ordered_at; delivered_at always after shipped_at. ~88% of orders are delivered; ~8% returned.

Narrative support: Revenue and order volume curves. Named events (Black Friday, Christmas) are especially effective here.

tables = misata.generate(
    "Ecommerce store with 10k customers — Black Friday spike, Christmas peak, Q1 slump",
    rows=10_000, seed=42
)

---

Fintech

Trigger keywords: fintech, transactions, payments, wallet, banking, loans, credit, fraud

Tables: customers, accounts, transactions

Table	Key columns
customers	customer_id, name, email, date_of_birth, credit_score, kyc_status, country
accounts	account_id, customer_id, account_type, balance, currency, iban, opened_at
transactions	transaction_id, account_id, amount, type, status, is_fraud, transaction_date, merchant

Distributions: Credit scores lognormal centred on FICO mean (~700, σ=75). Fraud rate ~2% (configurable: "3% fraud rate"). Transaction amounts lognormal. IBAN format follows locale (DE: DE##..., BR: BR##...).

tables = misata.generate(
    "Brazilian fintech with 2k customers, R$ payments, CPF verification, 3% fraud rate",
    rows=2000, seed=42
)
print(tables["customers"][["name", "credit_score", "country"]].head())
print(tables["transactions"][["amount", "is_fraud", "transaction_date"]].head())

---

Healthcare

Trigger keywords: healthcare, health, patients, doctors, hospital, clinic, appointments, medical

Tables: doctors, patients, appointments

Table	Key columns
doctors	doctor_id, name, specialty, department, years_experience, rating
patients	patient_id, name, date_of_birth, blood_type, gender, diagnosis, insurance_provider
appointments	appointment_id, patient_id, doctor_id, scheduled_at, duration_minutes, status, notes

Distributions: Blood types match real ABO/Rh frequencies (O+ 37.4%, A+ 35.7%, B+ 8.5%, …). Patient ages centred on chronic-care population (μ=52, σ=18). No-show rate ~15%. Doctor specialties drawn from realistic distribution (internal medicine, cardiology, orthopedics, …).

tables = misata.generate("A hospital with 500 patients and 50 doctors", rows=500, seed=42)
print(tables["patients"][["blood_type", "diagnosis"]].value_counts().head())

---

Marketplace

Trigger keywords: marketplace, gig, freelance, sellers, buyers, listings

Tables: sellers, buyers, listings, orders

Table	Key columns
sellers	seller_id, name, email, rating, total_sales, joined_at, country
buyers	buyer_id, name, email, total_spent, joined_at
listings	listing_id, seller_id, title, category, price, status, created_at
orders	order_id, buyer_id, listing_id, amount, status, created_at, completed_at

Distributions: Seller ratings beta-distributed (skewed toward 4–5 stars). Listing prices lognormal. Order completion rate ~85%. Seller volume follows power-law (few top sellers, many occasional ones).

tables = misata.generate("A freelance marketplace with 500 sellers and 2000 buyers")

---

Logistics

Trigger keywords: logistics, shipping, delivery, fleet, warehouse, supply chain, routes, drivers

Tables: drivers, vehicles, routes, shipments

Table	Key columns
drivers	driver_id, name, license_type, rating, hire_date, vehicle_id
vehicles	vehicle_id, type, plate, capacity_kg, year, status
routes	route_id, origin, destination, distance_km, duration_hours, cost
shipments	shipment_id, route_id, driver_id, weight_kg, status, shipped_at, delivered_at

Distributions: Delivery times lognormal. On-time rate ~88%. Route distances lognormal. Driver ratings beta-distributed (4.0–5.0 range).

tables = misata.generate("A logistics company with 200 drivers and 50k shipments")

---

HR / Workforce

Trigger keywords: hr, human resources, employees, payroll, workforce, hiring, headcount, salaries, onboarding

Tables: departments, employees, payroll

Table	Key columns
departments	department_id, name, head_count, budget, location
employees	employee_id, department_id, name, email, role, seniority, hire_date, date_of_birth, salary, tenure_years
payroll	payroll_id, employee_id, period_start, gross_pay, tax_withheld, net_pay, pay_type

Coherence rules: hire_date is always after date_of_birth + 18 years and never in the future. tenure_years is derived from hire_date on the same row — no separate random distribution. net_pay = gross_pay × (1 − tax_withheld) is formula-consistent row by row.

Distributions: Salary lognormal by seniority (junior ~$65k, mid ~$95k, senior ~$140k, lead ~$180k). Tax rate Beta(3, 7) clipped to 18–40%.

tables = misata.generate(
    "A tech company with 1000 employees, monthly payroll, and 4 departments",
    rows=1000, seed=42
)
# tenure_years derived from hire_date — no employees have negative tenure
print(tables["employees"][["role", "salary", "tenure_years"]].describe())

---

Social Media

Trigger keywords: social media, instagram, tiktok, twitter, feed, followers, likes, influencer, content creator, reels

Tables: users, posts, follows, reactions, comments

Table	Key columns
users	user_id, username, display_name, bio, follower_count, following_count, is_verified, joined_at
posts	post_id, user_id, caption, media_type, like_count, comment_count, share_count, posted_at
follows	follow_id, follower_id, followee_id, followed_at
reactions	reaction_id, post_id, user_id, type, reacted_at
comments	comment_id, post_id, user_id, text, parent_comment_id, posted_at

Distributions: Follower counts follow a Pareto (power-law) — a small fraction of accounts captures most reach. Engagement rates beta-distributed (~1–5%). Captions are realistic social media text with hashtags and emoji, not lorem ipsum.

tables = misata.generate("A social media app with 10k creators, posts, and viral content")

---

Real Estate

Trigger keywords: real estate, realty, housing, mortgage, homes for sale, property listing

Tables: agents, properties, transactions

Table	Key columns
agents	agent_id, name, email, agency, rating, listings_sold, years_experience
properties	property_id, agent_id, address, city, state, price, bedrooms, bathrooms, sqft, listing_date, status
transactions	transaction_id, property_id, buyer_name, sale_price, close_date, commission_rate, days_on_market

Distributions: Home prices lognormal (US median ~$410k, heavy right tail). Days-on-market lognormal (median ~23 days). Agent ratings beta-distributed (skewed toward 4–5). ~60% of listings close.

tables = misata.generate("A real estate agency with 500 properties and 50 agents")
print(tables["properties"][["price", "bedrooms", "status"]].describe())

---

Pharma / Research

Trigger keywords: pharma, research, clinical, trials, timesheet

Tables: researchers, projects, trials, timesheets

Table	Key columns
researchers	researcher_id, name, email, department, seniority, publications
projects	project_id, name, status, phase, budget, start_date, end_date
trials	trial_id, project_id, phase, participants, success_rate, duration_weeks
timesheets	timesheet_id, researcher_id, project_id, week_start, hours_logged, task_type

tables = misata.generate("A pharma research company with 200 researchers and clinical trials")

---

Food Delivery

Trigger keywords: food delivery, ubereats, doordash, grubhub, restaurant delivery, meal delivery, takeout, restaurants, menu items

Tables: restaurants, customers, couriers, orders, order_items

Table	Key columns
restaurants	restaurant_id, name, cuisine, city, rating, avg_prep_time, is_active
customers	customer_id, name, email, phone, city, joined_at
couriers	courier_id, name, vehicle_type, rating, deliveries_completed
orders	order_id, customer_id, restaurant_id, courier_id, total_amount, delivery_fee, status, placed_at, delivered_at
order_items	item_id, order_id, name, quantity, unit_price

Coherence rules: delivered_at is always after placed_at — no negative delivery times. Cuisines drawn from realistic distribution (pizza, sushi, burgers, indian, chinese, …).

tables = misata.generate(
    "A food delivery app with 500 restaurants, 2k customers, and 1k couriers"
)

---

EdTech

Trigger keywords: edtech, e-learning, lms, courses, students, instructors, lessons, enrollments, quizzes, learning platform

Tables: instructors, courses, students, enrollments, quiz_attempts

Table	Key columns
instructors	instructor_id, name, email, specialty, rating, courses_taught
courses	course_id, instructor_id, title, category, price, difficulty, duration_hours, rating
students	student_id, name, email, country, joined_at, total_courses_enrolled
enrollments	enrollment_id, student_id, course_id, enrolled_at, completion_pct, completed_at, certificate_issued
quiz_attempts	attempt_id, enrollment_id, quiz_name, score, passed, attempted_at

Narrative support: Enrollment curves support back to school surge (August) and New Year spike (January).

tables = misata.generate(
    "An edtech platform with 5k students, 200 courses — back to school surge, New Year spike"
)

---

Gaming

Trigger keywords: gaming, game, players, leaderboard, achievements, quests, guilds, matches, sessions, levels, esports, rpg

Tables: players, matches, sessions, achievements

Table	Key columns
players	player_id, username, level, xp, rank, country, joined_at, last_active
matches	match_id, game_mode, map, duration_seconds, winner_team, started_at
sessions	session_id, player_id, match_id, kills, deaths, assists, score, result
achievements	achievement_id, player_id, name, category, unlocked_at, points

Distributions: Player levels follow a right-skewed lognormal (most players are low-level). XP follows Pareto. K/D ratio beta-distributed around 1.0.

tables = misata.generate("A gaming platform with 10k players, matches, and leaderboards")

---

CRM

Trigger keywords: crm, salesforce, hubspot, contacts, deals, pipeline, leads, opportunities, sales pipeline

Tables: companies, contacts, deals, activities

Table	Key columns
companies	company_id, name, industry, size, country, revenue, website
contacts	contact_id, company_id, name, email, phone, title, lead_source, created_at
deals	deal_id, contact_id, company_id, name, value, stage, probability, close_date, owner
activities	activity_id, deal_id, contact_id, type, subject, outcome, activity_date

Distributions: Deal values lognormal (median ~$25k). Pipeline stages: prospecting 35%, qualification 25%, proposal 20%, negotiation 12%, closed-won 8%. Activity types: email 40%, call 30%, meeting 20%, demo 10%.

tables = misata.generate("A CRM with 500 companies, contacts, deals pipeline, and activities")
print(tables["deals"][["stage", "value", "probability"]].groupby("stage").mean())

---

Crypto / Web3

Trigger keywords: crypto, blockchain, web3, defi, nft, ethereum, bitcoin, solana, smart contract, dex, dao

Tables: wallets, tokens, transactions, token_prices

Table	Key columns
wallets	wallet_id, address, chain, balance_usd, created_at, wallet_type
tokens	token_id, symbol, name, chain, contract_address, market_cap
transactions	tx_id, wallet_id, token_id, tx_hash, type, amount, gas_fee, timestamp, status
token_prices	price_id, token_id, price_usd, volume_24h, market_cap, recorded_at

Distributions: Wallet addresses are hex-format (40 chars, 0x prefix). Gas fees lognormal. Token prices lognormal with high variance. Transaction types: transfer 60%, swap 25%, stake 10%, bridge 5%.

tables = misata.generate(
    "A crypto exchange with wallets, blockchain transactions, and token prices",
    rows=2000, seed=42
)
print(tables["wallets"][["chain", "balance_usd"]].groupby("chain").describe())

---

Insurance

Trigger keywords: insurance, policy, claim, premium, coverage, underwriting, actuary, policyholder

Tables: customers, policies, claims, payments

Table	Key columns
customers	customer_id, name, email, date_of_birth, gender, state, credit_score
policies	policy_id, customer_id, type, premium, coverage_amount, start_date, end_date, status
claims	claim_id, policy_id, incident_date, claim_date, amount, status, description
payments	payment_id, policy_id, amount, payment_date, method, status

Distributions: Premiums lognormal by policy type (auto ~$1.2k/yr, home ~$1.5k/yr, life ~$800/yr). Claim rate ~8% of active policies. Coverage amounts lognormal (heavy right tail).

tables = misata.generate("An insurance company with 2k customers, policies, and claims")
print(tables["claims"][["status", "amount"]].groupby("status").describe())

---

Travel

Trigger keywords: travel, hotel, flights, bookings, airline, airbnb, booking.com, expedia, hospitality, reservations, trips

Tables: users, hotels, flights, bookings, reviews

Table	Key columns
users	user_id, name, email, country, loyalty_tier, joined_at
hotels	hotel_id, name, city, country, stars, price_per_night, total_rooms
flights	flight_id, origin, destination, airline, departure_at, arrival_at, seat_class, price
bookings	booking_id, user_id, hotel_id, flight_id, check_in, check_out, total_price, status, cancellation_reason
reviews	review_id, booking_id, rating, title, body, reviewed_at

Coherence rules: cancellation_reason is null for non-cancelled bookings. Hotel check_out is always after check_in. Flight arrival_at is always after departure_at. Airport codes are realistic IATA format.

tables = misata.generate(
    "A travel booking platform with 5k users, hotels, and international flights",
    rows=5000, seed=42
)
# cancellation_reason is only non-null for cancelled bookings
cancelled = tables["bookings"][tables["bookings"]["status"] == "cancelled"]
print(cancelled["cancellation_reason"].value_counts())

---

Streaming

Trigger keywords: streaming, netflix, spotify, watch history, content library, subscribers, watchlist, episodes, series, vod, ott

Tables: subscribers, content, watch_history, ratings

Table	Key columns
subscribers	subscriber_id, name, email, plan, country, joined_at, is_churned, churned_at
content	content_id, title, type, genre, release_year, duration_minutes, rating, language
watch_history	view_id, subscriber_id, content_id, watched_at, watch_duration_minutes, completed, device
ratings	rating_id, subscriber_id, content_id, score, rated_at

Coherence rules: churned_at is null for active subscribers (is_churned = false). Content types: series 55%, movie 35%, documentary 10%. Watch completion rate ~65% for movies, ~45% per episode for series.

tables = misata.generate(
    "A Netflix-like streaming service with 10k subscribers and a content library"
)
# churned_at is null for all active subscribers
active = tables["subscribers"][~tables["subscribers"]["is_churned"]]
assert active["churned_at"].isna().all()

---

No match → generic table

If no domain keyword is detected, Misata emits a warning and generates a single generic table with inferred columns. A DetectionReport is returned with domain_confidence: "none" and an actionable warning message.

report = misata.preview("quarterly sales data by region")
# ⚠ No domain detected — domain_confidence: "none"
# warnings: ["No domain detected. Falling back to a generic single-table schema..."]

Fix options: 1. Add a domain keyword: "quarterly sales data by region — fintech" or "ecommerce quarterly sales by region" 2. Use LLMSchemaGenerator for fully open-ended stories 3. Author a misata.yaml directly for complete control

---

Combining domains with narrative curves

Every domain supports outcome curves on its primary metric column:

Domain	Curve column	Table
SaaS	mrr	subscriptions
Ecommerce	amount	orders
Fintech	amount	transactions
Healthcare	duration_minutes	appointments
HR	gross_pay	payroll
Marketplace	amount	orders
Logistics	cost	shipments
Real estate	sale_price	transactions
EdTech	revenue signals	enrollments

# SaaS with a full growth narrative
tables = misata.generate(
    "SaaS company with 5k users — MRR from $50k in Jan, strong Q4, doubled by December",
    rows=5000, seed=42
)

Full narrative patterns reference →