Store operations
Product records, store orders, media and fulfillment coordination.
In useProducts and content
Product records, stock information, images and copy on one thread.
In usePlatform / AI-native business operating system
Connect the work.
Keep the context.
Simbatch is an AI-native business operating system for teams of every size. It brings business information, people and reusable knowledge into a shared working context.
What is Simbatch?
An AI-native business operating system for teams of every size. It helps a team connect the work, keep experience that carries its source, and let AI take part within permissions and human confirmation.
Simbatch · Team workspace
Team workspaceBusiness taskConfirm the product record
Awaiting human confirmation- Owner
- Content and product team
- Current version
- V3 · 2026-09-08
- Source
- Product records and media
- Next step
- Check the specification and where it may be used
The work has a sourceSuggestions can be checkedOutcomes are recorded
Orders today127
Process templates94
Suggestions to confirm39
Figures explain the workflow and do not represent live or historical operating statistics.
Magnetic car phone mount · product photographs4 images · open any of them
Confirmed experience
Checklist before a product goes live
Once the team has confirmed them, facts, decisions, processes and feedback become versioned assets that later work can find and cite.
- Source
- Product record confirmation
- Confirmed by
- Content lead
- Version
- V3 · edit history can be reopened
- Feedback
- This round added the media source and where it may be used
Business channels
Four channels, each with its own focus.
Simbatch carries daily work on Shopify, WordPress stores, TikTok Shop and Amazon, keeping the product, content, order and fulfillment context together for each channel.
Website and content
Page content, product records, inquiries and operating media.
In useContent and store together
Products, short-form video assets, orders and fulfillment work.
In useMarketplace operations
Product information, listing content, order fulfillment and record upkeep.
In useWho it is for
Built for teams of every size.
Wherever work needs several people, continuous operation and experience that gets reused, the business context is worth connecting. Cross-border commerce and multi-store operations are one current example.
What problem does it solve?
Less scattered information, less re-checking, and less experience that lives only in one person’s memory.
How does AI take part?
Within its permissions it cites material that has a source and has been confirmed, and helps with finding, organising and drafting.
Why does it accumulate with use?
Outcomes, human edits and feedback keep updating the knowledge and the processes. Updating business knowledge is not the same as automatically training a model.
Six connected capabilities
Six capabilities around one business context.
These capabilities work on the same context, and are used according to team permissions and the real process.
Stores and orders
Stores, orders and their status connected into one traceable entry point.
In useMedia library
Images, video and copy with a source, a version, and a record of where they have been used.
In usePurchasing and supply chain
Requirements, supplier records and purchasing progress connected, with the handling recorded.
In useLogistics
Fulfillment information read from the order, with exceptions and follow-up recorded.
In useAI assistant
Suggestions drawn from permitted records and confirmed experience, each one checkable.
In useEnterprise memory / team experience
Experience should be useful again.
Enterprise memory is made of material the team can check. It keeps the context and the changes, so the next similar task starts from what has already been reviewed.
Knowledge becomes reusable
Confirmed step by step,
so experience can be used again.
Business records, human feedback and knowledge versions become experience a team can reuse. Updating business knowledge is not the same as automatically training a model.
18-second product video: an order arrives (0:00) → the product and four real photographs (0:03) → AI cites its sources (0:06) → a person edits and confirms (0:10) → the experience is versioned and used next time (0:14). Figures in the picture explain the workflow and do not represent live or historical operating statistics.
Follow an order through product records, AI suggestions and human review.
Facts
Confirmed product, customer and business information
Decisions
Why one way of handling it was chosen
Processes
Repeatable steps, and where they stop
Experience
Reusable judgment from one piece of work
Working relationships
Roles, owners and what comes before and after
Feedback
Human edits and what actually happened
“Learning” here means knowledge, processes and feedback improving over time. It does not mean a model is trained automatically.