A customer’s health information is scattered across test reports, questionnaires and previous conversations. Preparing a consultation or follow-up requires staff to organize those materials, compare indicators and decide what to discuss. As the customer base grows, that preparation is repeated again and again.
LAIFE AI connects health information management, AI-assisted analysis and service operations. Customers maintain their own records; professionals use AI assistants within their assigned customer scope; operators manage report review, service plans, permissions and resource consumption through an administration console.
The backend is provided by laife-ai-next, with laife-ai-next-console providing Web workspaces for customers, professionals and administrators.
Turning a report upload into a foundation for ongoing service
From the beginning, we had to answer a product question: if general-purpose AI health assistants can already explain reports, what additional value can LAIFE AI offer customers and service teams?
We chose to organize services around the customer’s health data. When a report is uploaded, the original file remains available for inspection and verification. Document parsing and large-model capabilities extract text, test indicators and related information into structured data that can be searched, compared and extended over time. Sensitive-information handling, human annotation and review are part of the workflow. Staff can compare low-confidence or ambiguous results against the original report and correct them.
The organized data enters the customer’s health record and is linked to previous reports, questionnaires and follow-up notes. When preparing a consultation, staff can reuse existing information, inspect changes in indicators and provide the AI assistant with authorized context, reducing repeated uploads and manual organization.
Report interpretation is therefore the beginning of service. As information accumulates, the same record supports trend inspection, conversation preparation and follow-up, allowing each interaction to build on the previous one.
AI assistance for a specific customer
Professionals can select an assigned customer and enter a personalized mode. The system checks customer ownership and informed-consent status, then passes the permitted reports and materials to the algorithm service as context for organizing abnormal indicators, preparing conversations and planning follow-up.
The platform also offers an assistant mode and a health-record mode for customers themselves. Each role has appropriate entry points: customers access their own information, while professionals work within their assigned customer scope.
Conversations support attachments, speech-to-text, history search, archiving and follow-up notes. Knowledge bases hold team service materials. Professionals can continue preparing from previous records; customers can inspect indicator trends and generated weekly health reports.
These capabilities support information organization and service delivery. Health assessments and service decisions remain the responsibility of the relevant professionals.
From a personal tool to a team workspace
Customer service involves brokers, doctors and health managers. Team management supports applications through invitation codes, owner approval, membership maintenance and leaving a team. Access depends on the current role, team and customer relationships.
A follow-up might begin with reviewing a record: a professional compares historical indicators, uses AI to organize talking points and records the conversation afterward. Team members use the same customer information and tools within their permissions, reducing repeated collection of information.
Available capabilities
| Area | Capabilities |
|---|---|
| Health report processing | Customer or staff uploads; AI parsing and sensitive-information handling; human annotation, indicator correction, review and status notifications. |
| Customer health records | Reports, questionnaires and follow-up notes; latest indicators, historical test details and trends; weekly reports and PDF downloads. |
| AI service assistants | Assistant, personalized and health-record modes; customer selection, attachments, speech-to-text, quick tasks and response cancellation. |
| Conversations and service notes | History search and message location; pinning, renaming and archiving conversations; recording and editing follow-ups. |
| Knowledge bases and tools | Upload service materials, inspect parsing status, pause or delete knowledge files; configure AI tools and starter tasks. |
| Service teams | Customer assignment, team creation, member invitations and approval; customer access restricted by roles and team relationships. |
| Purchases and resources | Service plans, price estimates, orders, payment initiation and status; service points, estimated consumption and wallet transactions. |
| Platform operations | Tenants, personnel, roles, questionnaires and contract templates; service tickets, dashboards and operation logs. |
| Branding and tenant settings | Per-tenant brand names, images, themes and registration policies; business parameters and resource rules. |
Connecting service use with commercial operations
AI services require management of access, purchases and resource consumption. Service plans, orders, payments and points are part of the same product. Customers and professionals can inspect purchases and usage, while operators maintain service configuration and related accounts.
Tenant branding is also configurable. Operators maintain brand copy, images, themes and selected business policies for different partners. Shared capabilities and tenant-specific requirements can therefore evolve separately.
Outcome
LAIFE AI established a product workflow from health information intake and human correction through record access, AI assistance, team collaboration and service purchases.
Customers, professionals and operators each have a dedicated workspace. Customers maintain information and understand changes; professionals use records for conversations and follow-up; operators manage service supply, review tasks and usage costs. The project puts AI into everyday service workflows and supplies the management capabilities needed for continued operation.