Less integration work
Give business systems a unified interface and reduce repeated work connecting AI services.
Internal enterprise AI · Custom development
Build your company's own AI platform
One integration layer. Clear visibility into AI usage and charges.
Bring separate AI integrations together with a unified interface for internal business systems. TalsAI provides platform development, system integration and operational support tailored to your business.
Discuss your enterprise AI platform
Simpler integration. Flexible choices. Clearer management.
More choice for each task
Connect multiple models and choose for your use case. Each model and protocol requires adaptation and validation.
Visibility into usage
View account usage and billing records centrally to support usage management and budget planning.
How enterprise AI connects
Keep your business systems. Add a shared layer for AI access and management.
Business applications
- Employee workspace
- Support / office apps
- ERP / CRM systems
RequestResponse
Enterprise AI platform
Shared access · Central management
- Unified API
- Accounts & API keys
- Usage & cost records
RequestResponse
Model resource pool
- Licensed cloud models
- Self-hosted models (adaptation required)
Custom extensionsStaff permissions / Department budgets / Integrations / Deployment & logging
Illustrative architecture: models require enterprise procurement or authorization. Self-hosted models and extensions require project assessment. A dedicated platform does not guarantee data stays on your network.
An illustrative custom workflow showing each system’s role and where data goes.
- 01
The application sends a request
A support agent selects a question and submits content approved for processing.
- 02
The platform checks and forwards
It validates the API key and forwards the request to the configured model interface.
- 03
The model drafts a reply
An authorized model processes the request and returns the draft through the platform.
- 04
A person reviews the result
The agent reviews the draft. Account usage is recorded for administrators.
Data flow: cloud models receive request content. Data scope, redaction and log retention must be agreed during the project.
Engineering foundations, tailored to your business
Existing engineering foundations
- Unified API entry point and adapted model integrations
- Account, API key and administrator management
- Account usage records and platform billing dashboards
Custom options subject to assessment
- Enterprise-authorized services and self-hosted model integration
- Employee permissions, departmental budgets and cost allocation
- Dedicated deployment, internal integrations and logging policies
A custom development service, not an off-the-shelf enterprise product. Model services must be procured or authorized by the enterprise. Capabilities, deployment requirements and deliverables are defined through project assessment and acceptance. Platform charges are not supplier procurement invoices.
From business needs to delivery
- 01
Define the requirements
Identify users, workflows, existing model authorizations and budgets.
- 02
Design and build
Agree on integrations, permissions, data handling and deployment, then develop and test.
- 03
Validate and support
Validate agreed scenarios, hand over the platform and provide support within the agreed scope.
Common questions
Are we buying model accounts or platform development?
This service covers platform development, deployment and integration, not the sale of model accounts or usage rights. Your enterprise must procure or obtain appropriate authorization for the model services used.
Can we manage employee and departmental AI budgets?
Account usage and billing foundations exist. Employee permissions, shared departmental budgets and cost allocation require custom development around your organization and policies.
Can it run in our own environment?
Dedicated deployment can be assessed against your network, security and operational requirements. Private model integration, intranet and offline operation require separate validation. Local deployment does not mean requests stay local; external model calls and logging policies will be documented in the solution.
Bring clarity to AI integration and spending
Tell us about your workflows, existing systems and management needs to define the right scope together.
Discuss your enterprise AI platform