Karan Patel
Founder & CEO · TalkingSchemaAgentic orchestration and evaluation-led iteration — turning plain language into production-grade schemas and warehouse designs.
About
Karan Patel is the Founder and CEO of TalkingSchema — an AI-powered ERD copilot for engineers, data teams, and architects. He owns the agentic orchestration: how models route across reasoning and execution steps, how structured outputs are validated against schema constraints, and how reflection loops recover from invalid proposals before anything reaches your canvas.
Karan’s work spans relational and warehouse design — Kimball, Inmon, Data Vault 2.0, SCD patterns, zero-downtime migrations — and the model lifecycle around those domains: evaluation harnesses, prompt and tool contracts, and disciplined iteration when underlying LLMs or providers ship updates. He writes about AI-assisted data modeling and how agentic workflows change the economics of schema design at scale.
Built at TalkingSchema
Schema design intelligence
Domain reasoning behind Plan Mode — constraints, normalization, warehouse patterns, and validation rules the agent must satisfy before changes reach your canvas.
Agentic orchestration
Multi-step agent workflows: routing, structured outputs, reflection and retry loops, and hand-offs between reasoning passes so schema edits stay auditable and reversible.
Model lifecycle & quality loops
Evaluation-driven iteration — benchmarking prompts and tool contracts across model versions, tightening grounding so production outputs stay consistent as models update.
Data warehouse modeling
Kimball star schema, Inmon 3NF, Data Vault 2.0, SCD types 0–6, snowflake and OBT — encoded into product behavior and AI guidance.
Technical writing
Deep dives on data warehouse methodology, schema patterns, and AI-assisted modeling for practitioners.
Expertise
PostgreSQLSQL
PythonMulti-agent orchestrationLLM evaluation & iterationPrompt & tool designData Vault 2.0Kimball dimensional modelingStar & snowflake schemaZero-downtime migrationsOLTP & OLAP patternsPublished Articles
· The case for context-aware modeling systems — capturing business intent when decisions are blocked, not in a separate documentation sprint.
· An opinionated comparison of major ERD tools — organized by the job each one actually does, with honest strengths and limitations.
· Why row-oriented and columnar storage produce different schemas — plus CDC, DuckDB, HTAP trade-offs, and when to split workloads.
· Database-first API design: one schema, then OpenAPI 3.1, GraphQL SDL, SQLAlchemy, TypeORM, and zero-code API options.
· Use your ERD as the single source of truth for Prisma schema, Drizzle tables, Zod validators, and TypeScript types.
· How fan traps and cross-grain aggregation cause double-counting in OBTs — and four scenarios where denormalization holds up.
· Expand-contract migrations, safe column renames, and PostgreSQL-specific tools for production schema changes.
· Normalized schemas still fail downstream without column intent — context capture, modeling inventory, and AI-safe OLTP design.
· Practical star vs snowflake trade-offs for Power BI, Looker, dbt MetricFlow, and layered warehouse architecture.
· The full SCD taxonomy — why typing belongs in the schema before dbt snapshots or warehouse MERGE pipelines.
· One canonical schema exported on demand to SQL DDL, ORM formats, OpenAPI 3.1, GraphQL SDL, DBML, and JSON.
· Co-authored with Mishall Afzal — conversational AI as the primary interface for collaborative schema design.
· Choosing between Kimball, Inmon, and Data Vault 2.0 — trade-offs, ETL complexity, and a five-question decision framework.
Co-founder
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