01
Single-business company
Add one reliable helper first
Give lead cleanup, customer follow-up, content publishing, and report summaries to the first AI role.
Hire one less repetitive roleStart with one workflowValidate quickly
For founders, business leaders, and enterprise management teams
Generate a manageable AI workforce for your company
SaleSea turns business goals, role design, workflows, and existing systems into AI employees with responsibilities, permissions, approvals, execution records, and measurable results.
Start with one high-frequency workflow, then expand into department collaboration and enterprise governance.
Start with one roleBuilt from management playbooksConnect existing systemsHuman approval for critical actionsTrack tasks, cost, and outcomes
Enterprise Operating Layer
From objective to execution loop
01
Business map
02
AI roles
03
Execution audit
Today’s missions
18
Sales follow-up, R&D weekly report, support tickets, finance checks
Awaiting human approval
4
Payment, external email, customer promise, system change
AI Organization
AI Sales Lead
CRM lead research, follow-up recommendations, record updates
AI R&D Assistant
Requirement breakdown, test cases, CI failure summaries
AI Operations Analyst
Spreadsheet cleanup, daily reports, anomaly alerts
AI Audit Assistant
Action traces, cost attribution, permission review
How the generation system works
Understand the business first, then generate roles, tasks, and execution paths
SaleSea is not another chat entry point. It organizes objectives, processes, systems, and human owners into an executable work loop.
Business input
Clarify objectives, constraints, metrics, and accountable owners.
Process breakdown
Identify key steps, systems, data sources, and deliverables.
Role generation
Generate AI employee responsibilities, permissions, and boundaries.
Mission orchestration
Break work into traceable missions with acceptance criteria.
Execution channels
Use APIs, browser actions, local clients, and files to complete work.
Result memory
Record outputs, costs, exceptions, and review material.
Management model library
Not role generation from thin air, but built from mature operating methods
SaleSea references public management practices from leading companies and classic management models, turning organization design, role responsibility, process collaboration, approval governance, and KPI logic into AI-executable templates.
Choose templates for sales, R&D, customer support, operations, finance, or content, then adapt them to your industry, permissions, and systems.
Organization design
Convert departments, roles, and reporting lines into AI role structures.
Role responsibility
Define boundaries, outputs, and accountable humans for each AI employee.
Process collaboration
Split cross-role work into nodes, deliverables, and acceptance rules.
Approval governance
Set confirmation rules and permission policies for high-risk actions.
KPI model
Improve speed, quality, cost, and business results through measurable loops.
Scenario fit
Adapt public operating patterns without copying any company’s internal rules.
Company fit
Small companies gain execution. Large organizations gain collaboration and governance.
Different company sizes see different value: small teams hand repetitive work to AI, growing teams stabilize cross-role collaboration, and enterprises unify permissions, cost, and audit.
02
Multi-role team
Keep workflows moving
Sales, operations, R&D, and support share one task rhythm. AI employees advance work by owner, deadline, and acceptance standard.
Cross-role collaborationAuto reportsOwner approval
03
Enterprise multi-business
One method, layered governance
Replicate AI roles by business unit, region, brand, and permission group while controlling cost, audit, and knowledge reuse.
Multi-department permissionsUnified auditEnterprise governance
R&D teams
Sales teams
Media content
Ecommerce and trade
Customer operations
Enterprise multi-business
Connect existing systems
Let AI employees enter the work environment you already use
SaleSea does not replace ERP, CRM, OA, developer tools, or internal systems. Within authorized boundaries, it reads information, writes back results, triggers workflows, and moves AI from suggestions to execution.
Business systems
ERP / Finance / Inventory
SAP, Oracle NetSuite, Microsoft Dynamics 365, QuickBooks, Xero, Odoo
Customer systems
CRM / Sales tools
Salesforce, HubSpot, Zoho CRM, Pipedrive, Outreach
SaleSea System connection hub
01 Permission policy
02 Execution method
03 Audit log
04 Data boundary
Collaboration and R&D
Workplace / Developer tools
Microsoft Teams, Slack, Google Workspace, GitHub, GitLab, Jira, CI/CD
Data and files
Sheets / Databases / Local files
Excel, Google Sheets, Airtable, Notion, databases, APIs, local files
API access
Browser automation
Local client
File and spreadsheet processing
Enterprise governance
Give AI the ability to work, while keeping boundaries manageable
Once AI starts operating real systems, companies need more than efficiency. They need permissions, approvals, traceability, cost control, and data boundaries.
Least privilege
Open only the systems and data required for the task.
Human confirmation
Confirm payments, external sends, promises, and system changes first.
Full audit trail
Record inputs, outputs, system actions, owners, costs, and outcomes.
Data boundary
Support BYOK, local execution policies, and enterprise isolation needs.
What pilots usually reveal
Before discussing concepts, look at what teams actually get stuck on
In pilot conversations, teams rarely ask whether AI is smart. They ask whether it can take real work, avoid disrupting existing workflows, and leave a record when something goes wrong.
Founder / business owner
Do not replace people first. Catch repetitive work first.
Stabilize lead cleanup, customer follow-up, daily reports, weekly summaries, and document collection so people can focus on judgment and decisions.
Business team
Do not add another system.
AI employees should work inside current CRM, spreadsheets, R&D tools, OA, and files to search, organize, write back, and remind.
IT / management
If it can act, it must be traceable.
Every role needs permission boundaries, approval rules, action logs, and cost attribution so the team can review, expand, and audit.
Before we talk
Start with the concerns you really have
SaleSea usually starts with one repetitive workflow that has clear boundaries and measurable results, then expands after the first role runs reliably.
How should an AI employee pilot start? +
Choose one repetitive workflow with clear boundaries and easy acceptance, such as sales follow-up, R&D weekly reports, support tickets, finance checks, or content production. SaleSea first configures role responsibilities, tool permissions, approval rules, and acceptance criteria.
Will it affect existing systems? +
No replacement is required for existing ERP, CRM, OA, R&D tools, or internal systems. Pilots usually start with low-risk actions such as reading, organizing, reminding, and drafting, then gradually add write-back and execution based on authorization.
Is it suitable for R&D companies? +
Yes. R&D teams can start with requirement breakdown, technical documents, test cases, CI failure summaries, release notes, and weekly reports. Actions involving repositories, task systems, or CI/CD can be governed by permissions and approvals.
How are data and permissions controlled? +
Each AI role only gets the data and tools needed for its task. High-risk actions can require human confirmation, and the system records inputs, outputs, tool calls, owners, costs, and results.
Start with one verifiable workflow
Let one AI role produce results before expanding into a department
Pick a repetitive, clear, and measurable workflow. We will configure responsibilities, permissions, system connections, and result standards together.