Solutions
In every large enterprise, there is an urgent and compelling focus to embrace and succeed at “AI Transformation” – a reworking of the entirety of business operations into an AI-native engine of efficiency and growth.
This AI transformation is not easy. While it is clear that staying with legacy approaches and legacy platforms is the wrong answer, the right answer is not obvious. When it comes to choosing AI platforms and solutions, there are four key requirements driving the choices:
In all four of these dimensions, Thunk.AI excels and offers a best-of-breed solution. Thunk.AI is a horizontal AI agentic automation platform, offering differentiated capabilities for high AI reliability, no-code authoring by process owners, easy customization, and rapid time to deployment.
Many AI agentic projects in the enterprise are being built on complex software platforms (like LangChain or ServiceNow), require expensive internal or external developer resources (including forward deployed engineers from the platform vendor), take many months or even years, and still fail to move into production. AI projects are becoming known for quickly getting to a compelling demo but then never getting reliable enough to actually function as a production system.
This makes it very difficult to transform operations rapidly. There are also simpler self-service agentic platforms that do not require developers, but they tend to be limited to basic scenarios and rate poorly on AI reliability.
On the other hand, Thunk.AI combines a rapid-iteration no-code model with high reliability. As a result, any new automation scenario can move from ideation to production in a matter of weeks.
AI Transformation is not about implementing a couple of automation scenarios. Instead, the modern enterprise wants to revisit all of its human-intensive processes across all of its departments and utilize AI to make them more efficient and higher quality.
If AI automation technology requires specialized skills and lengthy training, this will be a problem. If AI automation technology is silo’ed to only work for specific departments or only with specific data (eg: an AI automation platform embedded within a SaaS CRM service and focused on sales process automation), then this becomes a problem for scale across the enterprise.
Thunk.AI is intentionally a horizontal platform. It can connect to many different business systems using the industry-standard MCP protocol, so that the enterprise can have a common AI automation model, whether automating sales processes (using the CRM data), IT processes (using ITSM data), or any other processes in any other department.
Also, as a no-code platform designed for the process owner, it is intentionally friendly to the business user. Training if simple and quick, and the development of automation applications (thunks) can happen at scale across the organization.
However eager an organization is to automate work with AI, it cannot move forward without AI reliability. There is a baseline expectation that the AI automation will work reliably, conforming to the intent of the business process.
Thunk.AI is the technology leader in reliable AI automation. This ensures the investments made in AI automation move into production mode and deliver actual ROI.
The IT department of the enterprise needs to ensure that any new technology platform complies with its standards and integrates safely with its existing systems. In addition to the many existing compliance needs, AI agents introduce new concerns around prompt injection, agent identity, tool call credentials, data leakage to AI models, etc.
The enterprise offering of Thunk.AI addresses many of these needs and concerns.
The entire platform can be deployed as a private instance in any of the major hyper-scalar clouds. It can also be deployed entirely within a customer-operated cloud tenant. This addresses many of the concerns around isolation and unintended access
Authentication/identity management of users can be integrated with the enterprise identity provide using single-sign-on
Users across the enterprise can be grouped into organizations, with controlled access to development, test, and production environments. Each of these environments exposes specific assets with keys, throttles, and various administrative controls.
All AI activity has a persistent “AI Fingerprint” – a thorough, explainable, and auditable trail of decisions made by AI agents leading to changes in data or actions taken.