Problem
Companies that develop AI applications struggle to store, index, and retrieve large volumes of unstructured data in a fast, efficient, and scalable way. Vektra solves this problem by offering a vector database infrastructure optimized for AI applications, allowing systems to find semantically relevant information and utilize that knowledge more efficiently
Solution
Vektra offers a specialized vector database infrastructure for Artificial Intelligence applications, allowing you to store, index, and retrieve information by semantic similarity in a fast and scalable way.
The platform simplifies the construction of AI applications, especially RAG (Retrieval-Augmented Generation) based solutions, agents, and semantic search systems, providing an optimized data layer for AI models to find and use the most relevant context for each query.
With this, companies can transform large volumes of unstructured data into a knowledge base that can be consulted by AI applications, reducing the development complexity and infrastructure needed to implement these solutions.
Business model
Vektra adopts a B2B SaaS infrastructure model, based on recurring subscription and charging proportional to platform consumption. The revenue consists of infrastructure usage plans, scaled according to the volume of vector data, storage, queries, and computational capacity, allowing the customer to start with a smaller operation and expand according to their use.
The expected average ticket is approximately US$ 300 the US$ 1.000 per month per customer, with the potential to expand to higher-value enterprise contracts as data volume, number of applications, performance requirements, and service levels increase.
Market
Vektra operates in the global Vector Databases market, a data infrastructure category that has become fundamental for Artificial Intelligence applications, including RAG, semantic search, AI agents, recommendation systems, and multimodal applications.
The global Vector Databases market is currently estimated at approximately US$ 3,0 billions, with a projection of reaching around US$ 9 Billions even 2030, representing compound annual growth close to 25%–28%.
In addition to the specific Vector Databases market, Vektra is part of the broader ecosystem of infrastructure for AI, which presents a significantly greater opportunity and is also growing at a rapid pace.
Competitors
Pinecone, Qdrant, Weaviate, Milvus/Zilliz, and pgvector.
Competitive differentiation
Our differential lies in combining Wise's experience in software engineering and corporate environments with an infrastructure dedicated to AI workloads, allowing companies to incorporate semantic search, RAG and knowledge bases into their applications without having to develop and maintain this entire technological layer internally.
Vektra seeks to offer a flexible and scalable alternative to international vector banking solutions, with greater control over the infrastructure, integration with the client's technological ecosystem, and the possibility of adaptation to the specific needs of corporate AI applications.
Entry barrier
Our biggest differential is speed, cost, and the possibility of hosting in Brazil (the user will choose the cloud and the region, self-service) and this brings low latency
Traction
Vektra is currently undergoing validation and commercial development, with the technology already developed and prepared for Artificial Intelligence applications. The current strategy is focused on product validation with our portfolio clients and potential clients and on the evolution of the platform to meet real AI workloads.