Intentee provides an intent‑driven development platform where business domains, objectives, and constraints are expressed in natural language and compiled into adaptive workflows powered by self‑hosted open‑source large language models. The solution includes a Kubernetes‑native LLMOps stack for secure on‑premise model serving, an AI‑augmented documentation engine with vector‑search and conversational interfaces, and enterprise‑grade encryption and access controls.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional software development relies on explicit code specifications, producing rigid applications that cannot easily accommodate unforeseen requirements. Additionally, organizations using proprietary AI services face high costs, limited data control, and compliance challenges.
Solution
Intentee provides an intent‑driven development platform where users describe their business domain, objectives, and constraints in natural language. The platform compiles these intents into adaptive workflows powered by self‑hosted open‑source large language models, enabling the system to handle edge cases without manual re‑coding. Paddler offers a Kubernetes‑native LLMOps stack for scalable model serving, versioning, and resource management within the customer’s own infrastructure. Poet transforms static documentation into AI‑searchable, interactive content through vector‑search and conversational interfaces. All components run on‑premise with end‑to‑end encryption and role‑based access control, preserving data sovereignty and reducing reliance on external AI providers. Integration is supported via REST, GraphQL, and event‑driven SDKs, plus connectors for common enterprise data stores. The solution is delivered as a cloud‑native SaaS layer that handles model updates, monitoring, and compliance reporting.
Target Audience
Primary customers are enterprises and mid‑size organizations that need to build internal tools rapidly while maintaining data privacy, including regulated industries, product teams, and domain experts without deep programming expertise.
Features
- Intent‑based modeling language that captures business rules and goals in natural language and compiles them into executable workflows.
- Automatic generation of adaptive code paths using open‑source LLMs, allowing the system to respond to unforeseen inputs without manual re‑coding.
- Paddler: Kubernetes‑native LLMOps stack for hosting, scaling, and versioning large language models behind the enterprise firewall.
- Poet: AI‑augmented documentation engine that adds vector‑search and interactive Q&A to static content.
- End‑to‑end encryption, role‑based access control, and audit logging to meet regulatory and privacy requirements.
- Integration SDKs for REST, GraphQL, and event‑driven architectures, with connectors for SQL, NoSQL, and FHIR data stores.
- Open‑source core components hosted on GitHub, enabling custom extensions and community contributions.