One platform. Specialized by role.
The shared SaaS platform stays the same. The assistant behavior, tools, source bundle, and review logic change by role.
Solutions
Doctor-level assistants.
For every operating team.
assistants
One OpenClaw runtime. Five role-specific assistants.
Torra is an OpenClaw-based SaaS enterprise AI platform built on one common middle layer. It starts as a general enterprise AI layer, then gets customized into role-specific assistants for marketing, sales, support, engineering, and office teams.
Operating packs5 live tracks
One runtime. Five different operating motions.
MarketingCampaign briefs
SalesAccount plans
SupportGrounded replies
EngineeringSpec grounding
OfficePolicy routing
Role packs
Five role packs.
This is not one generic assistant skinned five ways. Each pack changes the context bundle, workflow logic, approvals, and final deliverables for the team that owns the work.
Launch with brand discipline.
Ground launch work in brand rules, approval paths, and market context before anything ships.
- Campaign briefs
- Brand-safe copy
Move accounts with grounded context.
Use CRM state, prior conversations, and pricing logic before every next step.
- Account plans
- Proposal drafts
Reply fast. Escalate cleanly.
Keep answers tied to docs, ticket history, and SLA rules before delivery.
- Grounded replies
- Escalation checks
Keep specs and repo state aligned.
Tie specs, repo state, and release gates together before engineering output is generated.
- Spec grounding
- Repo context
Route office work without policy drift.
Use policy packs and approval chains to keep internal execution consistent and auditable.
- Policy routing
- Approval chains
Platform model
Standard by default.
Customize only where the team needs it.
Standard SaaS platform
Add custom controls only where teams need them.
One governed OpenClaw runtime
Each team gets a specialized assistant on the same base.
Models, agents, and business files
RAG, QA, image, video, audio, PDF, Word, and Excel on one stack.
Customization path
Roll out in stages.
Start with one team. Expand after proof.
Start with the common operating layer.
Bring identity, knowledge, workflow, and review into one base.
Pick the first high-value team.
Choose the team with clear sources, measurable outputs, and an approval path.
Expand after proof.
Once one pack is grounded and auditable, extend the same platform to adjacent teams.
Next step
Map Torra to your teams.
Start with one pack. Expand on the same runtime.