What we do with generative AI
We design GenAI solutions that work with each organization's own context (its documents, processes and technical knowledge) to give answers specific to its business. The goal is for AI to help people do their work better and faster, with control and traceability.
Use cases
- Technical knowledge management: turning experts' experience into organizational memory.
- Contextual assistants that draft, summarize and answer questions about existing knowledge.
- Automatic quality review of documents and deliverables.
- Automation of repetitive search, classification and writing tasks.
Azteria
Azteria is our generative AI platform for technical knowledge management: each researcher creates documents from templates, adds tests, images, audio, datasets and PDFs, develops them with AI assistance grounded in existing knowledge, receives an automatic quality review and submits them to peer review. Once approved, the document is published and classified in a searchable institutional library. Invesa uses it to turn its researchers' experience into organizational memory.
Discover Azteria · See the Invesa case
Insuros
Insuros by Summan is the SaaS platform for insurance agencies and brokers: CRM, quotes, policies, renewals, commissions and real-time analytics, with intelligent automation, all in one place.
Design principles
- One clear flow: create → review → approve → publish.
- The organization's own context: answers based on its knowledge.
- Human control: only what a peer approves gets published.
Frequently asked questions
Does generative AI use my company's information to train public models?
The solutions are designed so the organization's knowledge is used only within its own environment, with the access controls the company defines.
What do we need to get started?
A specific process where knowledge or writing is a bottleneck, and the information sources that support it. From there we design the flow and the solution.
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