Intelligence · AI native platform
Most platforms add AI on top of what already exists. Vishvakta is designed from the ground up so that every transaction, every search, every seller interaction, and every logistics decision feeds a learning system. The platform does not just process commerce. It gets better at commerce with every order.
AI philosophy
The difference between AI as a feature and AI as architecture is where the model sits in the system. A feature sits at the edge: it takes finished data and decorates it with a prediction. Architecture sits at the centre: it shapes the data, informs the decision, and improves the outcome before the user ever sees a result.
At Vishvakta, every AI module is a first class system component. It has its own service boundary, its own data contract, its own deployment lifecycle, and its own performance metrics. It is not a prompt wrapper. It is not a third party widget embedded into a page. It is infrastructure.
Every order, search, and interaction generates a training signal. The platform gets measurably smarter as data volume grows. Day one intelligence is a floor, not a ceiling.
Every AI driven decision that affects a seller’s visibility or a buyer’s experience can be traced to a specific signal. No black box outcomes on consequential decisions.
AI is not applied where human judgment is required. Moderation pipelines have human review gates. Pricing intelligence operates within seller defined constraints. The model assists; it does not override.
Applied intelligence
Click any module to see how it works inside the platform.
AI infrastructure
Every user action (search queries, product views, clicks, add to carts, purchases, returns) is captured as a structured event and streamed to BigQuery via the analytics ingestion pipeline on Cloud Run. This is the raw training signal for every model on the platform.
Models are trained offline in BigQuery ML and custom Python training pipelines. Trained models are packaged as Docker images and deployed as independent microservices on GKE. Each model service exposes a typed REST API consumed by the relevant domain service. Model versions are managed in Artifact Registry; rollback to a previous version takes one deployment.
Every model decision that results in a downstream user action (a search result click, a recommended product purchase, a flagged review confirmed by a human reviewer) is captured as a labelled outcome and fed back into the training pipeline. The models improve continuously as the platform generates more data. This is not a quarterly retraining cycle. It is a continuous feedback architecture.
Boundaries
Performance linked discovery means AI contributes to ranking, but seller visibility always has a transparent, auditable basis. A seller can see exactly what signals affect their position and take specific actions to improve it. There is no unexplained suppression.
The pricing intelligence module makes recommendations. It never changes a seller’s listed price without explicit seller action. Sellers set their prices. The platform gives them better information to make that decision.
Reviews flagged as inauthentic, seller accounts flagged for suspension, and high risk orders flagged for fraud review all have a human review step before any consequential action is taken. The model recommends. A human decides.
Built in active development
The AI modules described on this page are designed as platform infrastructure, not post launch additions. The data pipelines, event ingestion architecture, and model serving infrastructure are being built alongside the commerce platform. When Vishvakta launches in October 2026, the intelligence layer launches with it. Every order from day one is a training signal.