The AI kit for Data Architects

Runs architecture decisions end to end, weighing trade-offs and writing the ADR. Example: documenting why you chose Postgres over DynamoDB for billing.

Guides schema, index, ORM, and serverless database choices. Example: adding a composite index so your order lookup drops from seconds to milliseconds.

Designs a data layer from scratch and selects technology. Example: planning a sharded customer store that scales past 500 million rows.

Compares microservices against monoliths, draws architecture diagrams, and analyzes dependencies. Example: showing which three services can split first without breaking reporting.

Like "Design a Logical Data Model"

Like "Architecture Decision Record"

Like "Data Classification Policy Development"

Like "Draft A Source-To-Target Mapping"

Pipelines adapt to upstream changes without corrupting data.

Confirm that data flows correctly in both directions before an integration is marked complete.

Fewer needless indexes with every drop tested against real queries

A verified map of where sensitive data lives, with mismatches fixed or accepted

Lets your AI look at your BigQuery tables and run SQL queries for you.

Let your AI read your databases, files and S3 storage, and run read-only queries across all of them at once.

Lets your AI look at your ClickHouse database, run read queries, and make careful changes when you allow it.

Let your AI design database diagrams that follow your team's naming rules, and check them against a real database.

Lume AI automates data mapping between any two schemas in seconds, enabling teams to efficiently ingest, normalize, and build data pipelines from diverse source

nao: an AI data IDE that centralizes schema, docs and data-stack context so teams can safely edit models, reduce context switching, prevent prod-breaking change

Structa lets you describe a database in plain English and AI generates visual ER diagrams, editable schemas, validation warnings and exports to SQL, Prisma, Typ

Codifyer automates database creation by converting your data schema descriptions into optimized, cloud-ready code, streamlining database architecture setup with

Build it to trace why a design was chosen and when that reasoning changed, so new engineers stop re-litigating old decisions.

Build it to connect schema drift to the documented consumers it breaks, before a downstream team finds out in production.

Build it to produce reviewable mappings and preflight reconciliation, so migration writes start only after the numbers match.

Build it to cut pipeline coordination effort while keeping data current, reviewed, and auditable across teams.

Designs scalable, performant data layers from scratch or re-architects existing ones.

Designs schemas, selects databases and ORMs, and optimizes queries based on your context.

Designs and builds scalable data pipelines, warehouses, and streaming architectures for reliable analytics infrastructure.

Designs and maintains scalable data pipelines and infrastructure for production data systems.