The Platform

Intelligent Infrastructure for
Enterprise AI.

Built for enterprise teams who want to move fast. We've simplified the technical foundations
into one coherent Agentic AI system, supporting rapid prototyping of various use cases,
enabling enterprises to go production quickly.

Architecture

One engine. Every layer.

Prescience.one spans the full AI infrastructure stack as a single, cohesive system —
from data ingestion to production monitoring.

UI Layer

Prescience IDE

Use case definition · data connectors · deployment settings · performance targets

Monitoring Dashboard

Pipeline health · model metrics · cost visibility

API Gateway

REST · gRPC · webhook endpoints

Orchestration

Deployment Orchestrator

Architecture selection · component wiring · pipeline generation · cost optimization

Lifecycle Manager

Deploy · scale · monitor · retire

Compute Fabric

Dynamic resource provisioning · multi-cloud

AI/ML Stack

Data Infrastructure

Warehouses · lakes · streaming

Feature Store

Auto-built · versioned

ML Frameworks

Classical + deep learning

GenAI Services

LLMs · embeddings · RAG

Serving Layer

Real-time + batch inference

Compute

AWS

S3 · SageMaker · Bedrock

Google Cloud

GCS · Vertex AI · BigQuery

Microsoft Azure

ADLS · Azure ML · OpenAI

On-Premises

Private cloud · bare metal

Platform Capabilites

Every Layer of AI
Infrastructure, unified

The core primitives that work together to eliminate the infrastructure gap between your team and production AI.

01

AI Use-Case Builder

An intuitive IDE that lets domain SMEs define AI use cases without engineering overhead. Captures intent, data context, and operational requirements — then hands off to the orchestrator automatically.

  • Natural language use case definition
  • Visual data source connector
  • Performance and cost requirement capture
  • Multi-modal data support
AI Use-Case Builder
02

Deployment Orchestrator

The intelligent engine that determines optimal end-to-end pipeline architecture. Selects and connects data infrastructure, compute layers, ML frameworks, and GenAI services — automatically.

  • Automatic architecture selection
  • Hyperscaler and on-prem compatibility
  • Cost and performance optimization
  • Infrastructure-as-code output
Deployment Orchestrator
03

Lifecycle Manager

End-to-end production management for AI applications. Built-in monitoring, auto-scaling, model versioning, and drift detection — so your team maintains visibility and control without custom observability tooling.

  • Real-time pipeline health monitoring
  • Auto-scaling via Compute Fabric
  • Model drift detection and alerting
  • Compliance audit trails
Lifecycle Manager
04

Compute Fabric

Prescience's Compute Fabric abstracts cloud infrastructure complexity, automatically provisioning and scaling compute capacity based on use case load — without manual configuration or vendor lock-in.

  • Automatic provisioning and teardown
  • Multi-cloud resource optimization
  • GPU / CPU workload routing
  • No vendor lock-in architecture
Compute Fabric

Your next AI use case
deserves to be Live.

Connect with us to discuss how we can built intelligent, scalable solutions for your organisation.
Our team is ready to collaborate.

The unified AI infrastructure platform for enterprise data and AI teams. From prototype to production — automatically.