Your domain knowledge. LatentSpin’s agent-building expertise.
Turn the work your team knows into an agent you can direct. Describe the objective in plain language, inspect the workflow LatentSpin builds, and teach it through reviewed corrections from real work.
Bring the work you know. LatentSpin turns your instructions into a workflow you can review, run, and improve.
01 / Describe
Explain the goal, the information to use, and what a good result looks like.
02 / Review
Check the steps LatentSpin prepares. Ask for changes in plain language and choose where a person must approve an action.
03 / Run
See actions, results, and supporting evidence. Review exceptions and approve decisions as the workflow runs.
04 / Teach
Correct the work where it happened and explain your reasoning. Review what the agent should learn before an update is applied.
The difference is who lays out the steps, and whether the path is set before the work runs.
Reliable, repeatable runs once someone designs every step, connection, and exception.Reliable once someone builds every step.
Quick to start in plain language; the steps in between are decided while the task runs.Quick to start in plain language.
Describe how the work should be done. LatentSpin engineers a defined workflow from it; you inspect it and ask LatentSpin for changes in plain language.Your know-how defines it; you inspect it and ask for changes.
Your corrections on real runs also teach the agent, without changing the workflow you approved. Connections and permissions are set up with your team during onboarding.
| The job | Workflow builder | AI assistant | LatentSpin |
|---|---|---|---|
| Arrange the steps | A builder connects steps, branches, and dependencies. | The assistant chooses steps as it works. | LatentSpin proposes readable steps; your team reviews and refines them. |
| Connect systems | Someone configures integrations, credentials, and data mappings. | Uses the connectors and permissions it has been given. | LatentSpin sets up the agreed connections with your team during onboarding; your IT approves access. |
| Handle exceptions | A builder defines conditions, retries, and routing. | Handled as the task runs. | Exception paths are part of the proposed plan; you choose which go to a person. |
| Run and maintain | Same path every run; changes need workflow edits. | The path can change between runs. | Same approved path every run; corrections become reviewed lessons. |
Illustrative comparison of approach categories; individual products and configurations vary.
When a reviewer corrects the work, someone has to turn that correction into a change. The difference is who does it, and where the change lands.
Example: a correction in retail operationsA shelf-compliance agent flags an approved seasonal display. The store-operations manager explains that displays with an approved supplier tag meet the requirement.
↺ Another round if the change isn't quite right.
In the agent's instructions or workflow, edited by whoever maintains them.
In the agent's model, as a versioned update. The approved workflow stays as it was.
A one-off exception can stay a one-off; your team decides what should be learned. Every update can be rolled back.
Illustrative comparison of approach categories; tools and team setups vary.
Explore how the workspace, execution runtime, and customer-scoped learning fit together.
| Aspect | Context-based updates | Offline fine-tuning | LatentSpin |
|---|---|---|---|
| Where changes live | Instructions, retrieval sources, or memory. | Model weights updated through a training project. | Customer-scoped model adaptations from reviewed work. |
| How updates are managed | Testing and versioning depend on the implementation. | Training data, evaluation, and release processes govern changes. | Bounded candidate updates are evaluated, reviewed, and versioned before promotion. |
| Ownership | Expertise is captured in the surrounding prompts and systems. | Model and data ownership depend on deployment and agreements. | Customer learning stays scoped to the organization, with ownership and handling defined in engagement terms. |
| Operating cost | Depends on the model, context size, and workload. | Includes training and serving costs. | Focused adapters on open-weight models; actual economics depend on the workflow and deployment. |
Expert work that repeats often, rules that change quickly, and mistakes that cost real money.
The work happens often enough that small improvements compound.
Policies and markets shift faster than static automation.
Senior judgment already lives in approvals, edits and escalations.
Mistakes cost money through delay, rework and compliance exposure.
Knowledge lives in people and prompts, so context is relearned.
Clear outputs and corrections show whether the agent improved.
Examples include healthcare operations, financial services, retail operations, revenue teams, compliance, and professional services.
Bring one repeatable task and the person who knows it best. We will work with your team to define the workflow, connect the agreed systems, and review what the agent can do.
Early access typically starts with one real task over roughly 1–2 weeks, with a few hours of your team’s time. Scope and success criteria are agreed together.
Build with us
Tell us what your team does today and where review or rework slows it down.
