Case Study – PNID.IO
Project Details
Client Name: PNID.IO
Partner with us to accelerate development
PNID.IO builds an AI-powered, governed multi-tenant SaaS platform on AWS
How F9 Infotech designed and delivered a cloud-native engineering-drawing intelligence platform from scratch, combining GPU-accelerated AI, Amazon Bedrock, automated tenant provisioning and enterprise-grade AWS governance.
Turning engineering drawings into intelligent, connected asset data
PNID.IO transforms Piping and Instrumentation Diagrams (P&IDs), together with PFDs, isometrics and related engineering drawings, into structured, connected and validated intelligent asset data for SmartPlant P&ID and Enterprise Asset Management (EAM) systems.
Its proprietary platform and machine-learning models automate diagram recognition, asset identification and structured-data generation, reducing manual engineering effort from hours to just 5–8 minutes per drawing.
A compute-intensive AI platform had to be built from zero
At inception, PNID.IO had a powerful idea — turning static engineering drawings into intelligent, connected data — but no cloud infrastructure to run it on. Reading a P&ID with AI is compute-hungry work: workloads spike hard when customers upload batches of drawings, then go quiet.
PNID.IO needed a platform that could carry heavy, bursty AI workloads on demand, keep every customer's engineering data strictly separated, and meet enterprise-grade governance requirements from day one.
- High-compute AI processing: Symbol, line and text recognition across complex engineering drawings.
- On-demand execution: Heavy compute should run only when drawings are being processed, keeping cost tied to actual use.
- Strict tenant isolation: Customer plant designs required secure per-tenant data separation.
- Zero-touch onboarding: New enterprise customers needed to be provisioned without manual configuration.
- Automated infrastructure: Networking, security guardrails and deployments had to be repeatable and automated.
- Governance: Multiple environments required consistent security and compliance controls.
- Scalability and resilience: The architecture needed to grow with demand without carrying unnecessary always-on capacity.
An AI-native, multi-tenant SaaS architecture on AWS
F9 Infotech designed and implemented a complete enterprise-grade multi-tenant SaaS architecture on AWS from the ground up, using an AI-native delivery model across the full build.
Amazon Kiro
AWS's spec-driven agentic IDE authored the platform's Terraform infrastructure-as-code layer, including account bootstrapping, networking, security guardrails and per-tenant provisioning logic.
Custom AI Detection Engine
A proprietary computer-vision model, built and trained in-house, runs on GPU-accelerated Amazon EC2 instances to detect symbols, tags and lines directly from drawing images.
Amazon Bedrock
Provides the reasoning layer that maps detected symbols and tags into a connected engineering graph and enables natural-language interaction with digitized drawings.
AWS Organizations
A structured multi-account framework provides a dedicated AWS account per customer tenant, with Service Control Policies enforcing governance, security guardrails and compliance.
AWS Fargate + Lambda
Containerized execution and event-driven orchestration provide scalable, serverless-first processing matched to actual workload demand.
Amazon EC2
GPU-accelerated instances host the custom AI detection engine and support other high-compute processing tasks.
AWS CodePipeline
Automatically builds and deploys the application stack into newly created tenant accounts, eliminating manual reconfiguration during onboarding.
Terraform + CloudFormation
Infrastructure-as-code establishes networking, security guardrails and account baselines in a repeatable, governed manner.
Governance and isolation built into the platform foundation
A centralized core account hosts shared services and application logic, while a dedicated AWS account is automatically provisioned for each customer tenant. Service Control Policies enforce governance and security guardrails across accounts.
When a customer is onboarded, the tenant account is provisioned, governance policies are applied and the application environment is deployed automatically — without manual reconfiguration or delay.
Faster engineering workflows, lower cloud spend and enterprise-grade isolation
Automated P&ID processing now takes 5–8 minutes per drawing, representing an approximate 80% reduction in manual engineering effort. Serverless, on-demand execution reduced infrastructure cost by 70%, while the governed multi-account model delivers 99.9%+ platform availability and 100% tenant-level data isolation.
A future-ready foundation for engineering intelligence
Cloud-native SaaS from scratch
AI diagram intelligence sits at the core of the platform rather than being bolted on afterward, shaping the platform around PNID.IO's engineering workflows.
Enterprise-grade multi-tenancy
Account-level isolation and SCP-enforced guardrails keep each customer's engineering data fully separated.
Automated customer onboarding
Manual provisioning is removed as a bottleneck, enabling new customers to be brought on and processing drawings without slowing the team down.
Optimized cloud spend
Elastic and serverless compute is matched to real usage, avoiding unnecessary always-on infrastructure while drawings are not being processed.
Future-ready AI capabilities
The platform is positioned for natural-language plant queries, automated inspection insights and live simulation on connected drawings.
The technology foundation
| AWS AI Services | Amazon Bedrock, Amazon Kiro |
|---|---|
| Infrastructure & Compute | AWS Organizations, AWS Fargate, AWS Lambda, Amazon EC2 (GPU-accelerated) |
| IaC & CI/CD | Terraform, AWS CloudFormation, AWS CodePipeline |
| Custom Technology | Proprietary AI detection engine — computer vision, GPU-accelerated, self-hosted on Amazon EC2 |
Building an AI-powered engineering platform?
Talk to F9 Infotech about designing a secure, scalable and governed cloud foundation for your AI workloads.