The database your agents share.
The data you own.
Give every agent your brand builds one place to read, write, and search across commerce data, company knowledge, and agent-created work. Start with Gentic-hosted storage or connect your own Cloudflare R2 bucket so your structured and vector data files live in storage you control.
Your agents do not have a model problem. They have a data problem.
Brand data is fragmented across storefronts, ad platforms, analytics tools, reviews, documents, asset libraries, and SaaS databases. Every system speaks its own dialect, and nothing holds the whole picture.
Vendor MCP servers expose inconsistent, isolated interfaces; they don't create shared business memory. Agents can answer questions, but they struggle to reliably understand the brand, complete work, and preserve what happened for the next agent.
One shared data layer for every agent your brand builds.
Every agent reads and contributes through the same consistent interface. No separate memory and no fresh data copy for every new agent you stand up — they all work against one store, so what one agent learns is there for the next.
Gentic Computer, in-house agents, apps, Claude, ChatGPT, and other MCP clients all read, write, and search through the shared Gentic MCP surface.
One layer for commerce data, knowledge, and the work your agents create.
Commerce and operational data
Products, customers, orders, reviews, campaigns, analytics events, research results, agent outcomes, and the derived tables your workflows build on top.
Structured data + embeddings
Familiar relational data alongside text and image embeddings — precise SQL and semantic search in one store, instead of a separate vector database bolted on.
Brand knowledge and memory
Guidelines, briefs, documents, research, transcripts, Brand Brain, and a brand wiki your agents keep current as work happens.
Creative and experiences
Make images, video, and landing pages agent-findable through durable records and references. (These bytes are tracked through Gentic — they don't live in your R2 bucket today.)
Own your data in a format that does not need Gentic.
GenticDB stores structured and vector data in open Parquet files. Query them with DuckDB. Export CSV, Parquet, or JSON. Use standard object-storage tools. With bring-your-own-bucket, those files live in your Cloudflare R2 bucket, where you can inspect them and revoke access.
-- query your open Parquet directly
SELECT product, sum(revenue) AS rev
FROM read_parquet('orders/*.parquet')
GROUP BY product ORDER BY rev DESC LIMIT 3;
┌────────────────┬──────────┐
│ product │ rev │
├────────────────┼──────────┤
│ Kitchen Bundle │ 48,210.00│
│ Travel Mug │ 31,884.50│
│ Spice Set │ 22,019.00│
└────────────────┴──────────┘$ aws s3 ls s3://your-bucket/genticdb/ --recursive orders/part-0001.parquet customers/part-0001.parquet embeddings/products.parquet reviews/part-0001.parquet
Hosted or bring your own bucket.
Gentic-hosted
Gentic manages storage and operation so your team can start quickly — no infrastructure to stand up. Live in production today.
Your Cloudflare R2Early access
Connect credentials scoped to your bucket through Gentic. Once your org is provisioned onto it, your structured and vector data files live there in open Parquet — inspect them, export them, or revoke access at any time.
Already hosted with Gentic? Moving your existing historical data into your bucket is coming soon.
Designed so one brand can never become another brand.
Every brand is bound to its own catalog namespace, storage location, and scoped credentials. GenticDB fails closed if a connection does not match the brand it belongs to, and customer-bucket connectivity is verified before going live. This is the technical proof behind the ownership promise.
DuckDB for speed, DuckLake for table state, open Parquet for ownership.
DuckDB gives agents fast SQL and semantic retrieval, DuckLake manages the lakehouse table state, and R2 stores the open data files. The catalog metadata stays Gentic-managed; your structured and vector data files are what live in your bucket.
Every action makes the next agent smarter.
Work doesn't evaporate when an agent finishes. Each outcome lands back in the shared layer, so the next workflow starts from everything that came before.
Three layers, one platform.
Gentic Computer
Keeps agents working until the job is finished.
Gentic MCP
Gives agents the tools to act.
GenticDB
Gives every workflow shared memory.
Gentic Computer thinks and finishes the job. Gentic MCP acts. GenticDB remembers.
Build any agent. Give it the data to succeed.
Stop rebuilding integrations, memory, and storage for every agent your brand creates. Give them one shared data layer built for e-commerce operations.
Questions, answered.
What data can agents access through GenticDB?
Commerce and operational data, brand knowledge and memory, and the outputs agents create — read, written, and searched through the shared Gentic MCP interface. Structured data and embeddings live together, so the same store answers SQL queries and semantic search.
What does “own your data” mean?
GenticDB stores structured and vector data in open Parquet files. You can query them with DuckDB, export CSV / Parquet / JSON, and use standard object-storage tools. With bring-your-own-bucket, those files live in your Cloudflare R2, where you can inspect them and revoke Gentic's access.
What lives in my Cloudflare R2 bucket?
Your structured and vector data files, in open Parquet. Once Gentic provisions your org onto your connected bucket, that's where they live. The Gentic-managed catalog and media/file bytes are not in your bucket.
Does GenticDB replace Shopify or my operational systems?
No. GenticDB is the shared data and memory layer your agents read, write, and search across — not a replacement for Shopify or your source systems of record.
Can I query or export my data without Gentic?
Yes. The data files are open Parquet. Point DuckDB at them, export CSV / Parquet / JSON, or use standard object-storage tooling — no Gentic runtime required to read your files.
Can I move existing Gentic-hosted data into my bucket?Coming soon
Coming soon. Today, connecting a bucket means new structured and vector data is written there going forward. Migrating an existing hosted org's historical data into its bucket is in progress and not yet generally available.