Technology Architecture
PackGPT is a deterministic, cloud-native decision infrastructure deployed on Google Cloud Run under stateless containerized architecture.
In simple terms: PackGPT structures enquiries, validates feasibility, and gives cost range before design begins.
Packaging enquiries are unstructured:
Feasibility validation happens late:
Pricing logic is manually applied:
Decisions are fragmented across teams:
Technical Context
A structured system is required to formalize these workflows into defined operational layers.
Buyer requirements are often described in general terms without standardized structural parameters. These inputs require interpretation before feasibility or costing can begin.
Structural compatibility and production constraints are typically evaluated after initial design discussions, leading to avoidable iteration cycles.
Cost calculations are frequently handled through spreadsheets or isolated systems, requiring internal coordination before reliable outputs can be generated.
Sales, design, and factory planning teams often operate sequentially, resulting in delayed alignment and extended pre-production timelines.
System Architecture Overview
Presentation Layer
Structured capture and normalization of buyer requirements.
Pricing Engine Layer
Structured Output Layer
Decision Gate Layer
Deterministic pricing logic with isolated manufacturer rule control.
Cost range, production baseline, and human validation trigger.
Enforcement of packaging feasibility and structural guardrails.
Pricing logic is never executed inside the language model layer.
Cloud Deployment Model
Containerized Node.js backend
Deployed on Google Cloud Run
Stateless compute architecture
Scalable request handling
Separation between conversational interface and Pricing engine
Designed for scalable containerized deployment on Google Cloud Run with structured observability.
Stateless compute processes each request independently, eliminating dependency on persistent session state and enabling predictable horizontal scalability. Tenant-level logical isolation ensures each manufacturer operates within a fully segregated pricing environment, with no cross-tenant access to proprietary cost logic. This architecture supports scalable deployment while preserving strict operational confidentiality.
Multi-Tenant Isolation
Each manufacturer operates within a fully isolated pricing environment where cost structures, material configurations, and margin logic are independently controlled. Pricing rules are never shared across tenants, and no manufacturer has visibility into another’s configuration. Buyer interactions are limited to structured outputs, with no exposure of internal calculation logic or rule parameters. System-level governance boundaries enforce strict separation between buyer access and manufacturer-controlled decision logic, preserving confidentiality while enabling scalable multi-tenant deployment.
Tenant-level pricing rule control
Logical environment separation
Controlled output exposure
Configurable per production facility
No manufacturer has visibility into another manufacturer’s cost structure.
Data Governance & Control
Structured input normalization:
Tenant-level pricing rule control
Logical environment separation
Controlled output exposure
Configurable per production facility
Independent configuration per manufacturer
Isolated pricing parameter management
No cross-tenant data visibility
Role-based access boundaries
Segregated rule execution environments
Deployment-ready tenant provisioning
Configurable margin and cost structures
Environment-level governance control
Deterministic rule enforcement:
Mandatory human approval before finalization:
Controlled output formatting:
Buyer requirements are converted into defined structural parameters before validation begins. Unstructured descriptions are mapped into controlled decision inputs. This reduces ambiguity and improves consistency across enquiries.
Validation and pricing rules are applied within predefined system boundaries. Rule execution does not depend on conversational variability. Outputs remain consistent for identical structured inputs.
System responses are generated as structured decision artifacts. Outputs include cost ranges and production baselines rather than raw calculations. Internal pricing logic is not exposed in user-facing responses.
System responses are generated as structured decision artifacts. Outputs include cost ranges and production baselines rather than raw calculations. Internal pricing logic is not exposed in user-facing responses.
Security & Operational Governance
Cloud infrastructure deployed on Google Cloud
Role-based access controls
Tenant-level logical isolation
Structured audit logging during pilot
Controlled configuration management
Infrastructure Stack
Node.js containerized backend
Google Cloud Run (serverless compute)
Structured data persistence layer
REST-based API architecture
Observability-enabled deployment
Scalability Roadmap
Phase 1 – Pre-Production Decision Intelligence
Phase 2 – Production Planning Integration
Phase 3 – Operational Intelligence
Structured requirement normalization and feasibility validation before design initiation.
Deterministic pricing logic applied within isolated manufacturer environments.
Human-in-the-loop approval enforced before production confirmation.
Extension of structured decision data into production monitoring environments.
Alignment with machine-level quality validation systems under governed access.
Controlled integration of operational signals into structured decision feedback loops.
Integration of structured decision outputs with material dependency modeling.
Lead-time sequencing and vendor-based input alignment within controlled parameters.
Support for capacity-aware scheduling logic under defined operational guardrails.
support@packgpt.io
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