MTS AI Software Factory Enterprise Studio - Technical Architecture Document

Section 01

Executive Summary

MTS AI Software Factory Enterprise Studio is an AI-powered software engineering platform that accelerates the entire application development lifecycle — from architecture design through code generation, review, testing, documentation, and deployment.

Product

MTS AI Software Factory Enterprise Studio

Version

1.0.0

Release Date

Q1 2025

License

Proprietary — Enterprise

Architecture

Monolithic with modular design, event-driven capabilities

Deployment

On-premise, Cloud (AWS/Azure/GCP), Hybrid

Target Users

Enterprise Development Teams

AI Models

100+ via OpenRouter

MTS AI Software Factory Enterprise Studio provides a unified, AI-augmented workspace for software architects, developers, testers, and DevOps engineers. The platform integrates large language models through an intelligent gateway (OpenRouter), enabling context-aware code generation, architecture design, automated testing, documentation, and infrastructure provisioning across 20+ programming languages and the full spectrum of enterprise technology stacks. Built with a Next.js 16 frontend and FastAPI backend, it delivers real-time streaming responses, role-based access control, and enterprise-grade security suitable for regulated industries including banking, healthcare, and government.

Section 02

System Architecture

Layered architecture with clear separation of concerns. Each layer communicates through well-defined interfaces, enabling independent scaling and technology evolution.

Architecture Layers

Client Layer

Browser · Mobile App · Desktop

↓ HTTP / WSS

Frontend Layer

Next.js 16 · React 19 · TypeScript · Tailwind CSS v4

↓ REST / SSE

API Gateway

FastAPI · CORS · Rate Limiting · Validation

↓

Authentication Layer

JWT · OAuth2 · RBAC · Session Management

↓

Business Logic Layer

AI Modules · Code Engine · Document Engine · Workflow Engine

↓

LLM Integration Layer

OpenRouter Gateway · Model Router · Prompt Engine · Stream Handler

↓

Data Layer

SQLAlchemy ORM · SQLite / PostgreSQL · Redis Cache

↓

External Integrations

GitHub · Jira · Slack · Azure DevOps · Docker · K8s

⚡ Event-Driven Processing

Asynchronous task processing via background workers. SSE streaming delivers real-time LLM output to clients without polling.

🛠 Modular Design

Each AI module operates as an isolated service with its own prompt templates, context management, and output processors. Modules can be composed into multi-step workflows.

🔒 Defense in Depth

Security implemented at every layer: TLS in transit, AES-256 at rest, JWT authentication, RBAC authorization, input validation, and SQL injection prevention via ORM.

Section 03

Technical Stack

Complete technology inventory across all platform layers, selected for enterprise reliability, performance, and ecosystem maturity.

Frontend

Technology	Version	Purpose
Next.js	16.2.10	App Router, SSR, Static Generation, API Routes, Middleware
React	19	UI Components, Hooks, Server Components, Concurrent Features
TypeScript	5.x	Type Safety, IDE Support, Compile-Time Error Detection
Tailwind CSS	4	Utility-First Styling, Design System, Responsive Layouts
shadcn/ui	latest	Accessible Component Library, Radix UI Primitives
Zustand	5.x	State Management, Lightweight Store, DevTools Support
Prism.js	latest	Syntax Highlighting for 20+ Languages
Lucide Icons	latest	Consistent Icon System, Tree-Shakeable

Backend

Technology	Version	Purpose
Python	3.14	Runtime Environment, Async/Await, Type Hints
FastAPI	0.115+	Async Web Framework, OpenAPI Docs, WebSocket Support
SQLAlchemy	2.0	ORM, Database Abstraction, Relationship Mapping
Alembic	latest	Database Migrations, Version Control for Schema
Pydantic	v2	Data Validation, Settings Management, JSON Schema
python-jose	latest	JWT Token Handling, RSA/ECDSA Signing
bcrypt	latest	Password Hashing, 12-Round Salt Generation
uvicorn	latest	ASGI Server, Hot Reload, Production Performance

Database & Cache

Technology	Version	Purpose
SQLite	3.x	Development Database, Zero-Config, File-Based
PostgreSQL	16+	Production Database, ACID, JSONB, Full-Text Search
Redis	7+	Caching, Session Store, Rate Limit Counter, Pub/Sub

AI / ML

Technology	Version	Purpose
OpenRouter	API v1	LLM Gateway, 100+ Models, Unified API, Cost Routing
LangChain	latest	LLM Application Framework, Chains, Agents, RAG
CrewAI	latest	Multi-Agent Orchestration, Task Delegation, Workflows

DevOps & Infrastructure

Technology	Version	Purpose
Docker	24+	Containerization, Multi-Stage Builds, Compose
Kubernetes	1.29+	Container Orchestration, Auto-Scaling, Service Mesh
Terraform	1.7+	Infrastructure as Code, Multi-Cloud Provisioning
GitHub Actions	latest	CI/CD Pipelines, Automated Testing, Deployment

Section 04

AI Modules

Nine specialized AI-powered modules covering the complete software development lifecycle. Each module leverages LLMs with domain-specific prompts, context management, and output validation.

🛠

AI Software Architect

Generates enterprise-grade architecture designs including High-Level Design (HLD), Low-Level Design (LLD), database schemas, API specifications, and microservice decomposition strategies.

HLDLLDDB DesignAPI DesignMicroservices

Languages: Java, C#, Python, Go, TypeScript, Kotlin, Swift, Rust, PHP, Ruby

POST /api/v1/modules/architect/generate

💻

AI Code Generator

Produces production-ready code from natural language specifications. Supports 20+ languages with framework-aware generation, proper error handling, and type safety.

GenerateExplainReview

Languages: Java, C#, Python, TypeScript, Go, Rust, Kotlin, Swift, PHP, Ruby, Dart, Scala, R, MATLAB, SQL, Shell

POST /api/v1/modules/code/generate

🔍

AI Code Reviewer

Performs deep static analysis using LLMs to identify security vulnerabilities, performance bottlenecks, code smells, and design pattern violations with actionable recommendations.

SecurityPerformanceClean CodeDesign Patterns

Languages: All supported languages with framework-specific rules

POST /api/v1/modules/review/analyze

🚀

AI Modernization Engine

Automates enterprise application modernization by migrating legacy codebases to modern frameworks and architectures. Handles monolith-to-microservice decomposition.

Java→Spring Boot.NET→8PHP→LaravelMonolith→Micro

Targets: Spring Boot 3, .NET 8, Laravel 11, Node.js 22, FastAPI, Django

POST /api/v1/modules/modernize/migrate

🗃

AI Database Assistant

Generates optimized stored procedures, queries, and schema designs. Provides query performance analysis, indexing recommendations, and data migration strategies.

Stored ProceduresQuery OptimizationSchema Design

RDBMS: Oracle, SQL Server, PostgreSQL, MySQL, MariaDB · NoSQL: MongoDB, DynamoDB, Cassandra

POST /api/v1/modules/database/generate

📜

AI Documentation Generator

Creates comprehensive documentation artifacts from code and specifications. Generates BRDs, FRDs, user stories, API docs, architecture documents, and runbooks.

BRD/FRDUser StoriesAPI DocsArchitecture

Formats: Markdown, HTML, PDF, Confluence, Notion, Swagger/OpenAPI

POST /api/v1/modules/docs/generate

⚙

AI DevOps Assistant

Generates Dockerfiles, Kubernetes manifests, Helm charts, Terraform modules, and CI/CD pipeline configurations. Includes security scanning and cost optimization.

Docker/K8sCI/CDTerraformHelmAzure Pipelines

Platforms: AWS, Azure, GCP, On-Premise, Hybrid Cloud

POST /api/v1/modules/devops/generate

✅

AI QA Engineer

Generates comprehensive test suites including unit, integration, end-to-end, API, and security tests. Provides coverage analysis and mutation testing strategies.

UnitIntegrationE2EAPISecurity

Frameworks: Jest, Playwright, JUnit 5, Pytest, xUnit, Cypress, Vitest, Mocha

POST /api/v1/modules/qa/generate-tests

📊

AI Business Analyst

Bridges business and technical teams by generating user stories, acceptance criteria, process flow diagrams, gap analyses, and requirements traceability matrices.

User StoriesAcceptance CriteriaProcess FlowsGap Analysis

Methodologies: Agile, Scrum, SAFe, Kanban, Waterfall, Hybrid

POST /api/v1/modules/ba/analyze

Section 05

Enterprise Tech Ecosystem

Full-spectrum technology support across 14 enterprise categories, enabling AI-assisted development for virtually any technology stack.

Category	Technologies
Microsoft	.NET 8, C#, ASP.NET Core, Blazor, MAUI, Azure, Power Platform, SharePoint, Dynamics 365, Entity Framework Core
Java	Java 21, Spring Boot 3, Spring Cloud, Jakarta EE, Hibernate, Gradle, Maven, Quarkus, Micronaut
PHP	PHP 8.3, Laravel 11, Symfony 7, WordPress, Drupal, Composer, PHPUnit, Filament
JavaScript / Node.js	TypeScript 5, Node.js 22, Express, NestJS, React, Next.js, Vue.js, Angular, Svelte, Deno, Bun
Python	Python 3.14, Django 5, FastAPI, Flask, SQLAlchemy, Celery, NumPy, Pandas, scikit-learn, PyTorch
Mobile	React Native, Flutter, Swift, Kotlin, SwiftUI, Jetpack Compose, Xamarin, Ionic, Capacitor
Cloud	AWS (EC2, Lambda, S3, RDS, EKS), Azure (AKS, Functions, Cosmos DB), GCP (GKE, Cloud Run, BigQuery)
Databases	PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, DynamoDB, Cassandra, Redis, Elasticsearch, Neo4j
DevOps	Docker, Kubernetes, Terraform, Ansible, Jenkins, GitHub Actions, GitLab CI, ArgoCD, Helm, Prometheus, Grafana
ERP / CRM	SAP, Salesforce, Dynamics 365, Odoo, Oracle ERP Cloud, ServiceNow, HubSpot
AI / ML	OpenRouter, OpenAI, Anthropic, Google AI, Hugging Face, TensorFlow, PyTorch, LangChain, CrewAI
Testing	Jest, Playwright, Cypress, JUnit 5, Pytest, xUnit, Selenium, k6, JMeter, OWASP ZAP, SonarQube
Security	OAuth2, SAML, OIDC, Vault, Keycloak, Fortify, Checkmarx, Snyk, Trivy, SSL/TLS, WAF
Blockchain / Web3	Solidity, Ethereum, Hyperledger, Web3.js, Ethers.js, IPFS, Chainlink, The Graph

Section 06

LLM Integration

Intelligent multi-model gateway via OpenRouter with automatic model selection, fallback routing, cost optimization, and streaming SSE delivery.

🌐 OpenRouter Gateway

Unified API gateway providing access to 100+ LLMs from multiple providers through a single integration point. Handles authentication, rate limiting, and model lifecycle management.

Single API key for all providers
Automatic provider failover
Usage tracking and cost analytics
Model version pinning and rollback

⚡ Streaming SSE

Server-Sent Events deliver token-by-token LLM output to the frontend for real-time display. Enables responsive UX even for long generation tasks.

HTTP/2 SSE stream per request
Automatic reconnection on disconnect
Back-pressure handling for slow clients
Structured event types (token, metadata, error, done)

🎯 Model Router

Intelligent routing selects optimal models based on task type, context length, quality requirements, and cost constraints. Configurable fallback chains ensure availability.

Task-based model selection
Cost-aware routing policies
Latency-based load balancing
Custom fallback chains per module

Supported Models

Model	Provider	Context Window	Best For
GPT-4o	OpenAI	128K tokens	General reasoning, Code generation, Complex analysis
Claude 3.5 Sonnet	Anthropic	200K tokens	Long-context architecture, Document analysis, Safety
Gemini 2.0 Flash	Google	1M tokens	Fast inference, Multi-modal, Large codebase analysis
DeepSeek V3	DeepSeek	128K tokens	Code generation, Cost-effective batch processing
Llama 3.1 405B	Meta	128K tokens	Open source, Self-hosted, Data sovereignty
Mistral Large	Mistral	128K tokens	European compliance, Multilingual, Low latency

Streaming Implementation

# FastAPI SSE Streaming Endpoint
from fastapi import FastAPI
from sse_starlette.sse import EventSourceResponse

app = FastAPI()

async def stream_llm_response(prompt: str, model: str):
    async for chunk in openrouter.stream(model=model, prompt=prompt):
        yield {
            "event": "token",
            "data": json.dumps({"content": chunk.text})
        }
    yield {"event": "done", "data": "{}"}

@app.post("/api/v1/chat/stream")
async def chat_stream(request: ChatRequest):
    return EventSourceResponse(
        stream_llm_response(request.prompt, request.model)
    )
      

Token Management & Cost Optimization

💰 Cost Controls

Per-user daily token budgets
Automatic model downgrade on budget exhaustion
Real-time cost dashboards
Monthly spend alerts and caps

📈 Optimization Strategies

Prompt caching for repeated patterns
Context window compression for long conversations
Batch processing for non-urgent tasks
Model-specific prompt optimization

Section 07

Authentication & Security

Enterprise-grade security with defense-in-depth strategy. Multi-layer protection for authentication, authorization, data protection, and compliance.

🔑 JWT Token Architecture

Dual-token strategy with short-lived access tokens and long-lived refresh tokens.

Access Token: 25-minute expiry, stateless validation
Refresh Token: 7-day expiry, stored server-side
RS256 signing with rotating keys
Token blacklist for immediate revocation
Claims include user_id, role, permissions, tenant_id

🔒 Password Security

Industry-standard password hashing with adaptive cost factors.

bcrypt with 12 rounds of salt generation
Minimum 8-character policy with complexity rules
Password history enforcement (last 5)
Account lockout after 5 failed attempts
Secure password reset via time-limited tokens

👥 RBAC - Role-Based Access Control

Granular permission system with hierarchical roles.

Roles: Super Admin, Admin, Manager, Developer, Viewer
Permission matrix: 25+ fine-grained permissions
Resource-level access control
API endpoint protection per role
Audit logging for all access decisions

🛡 CORS Configuration

Strict cross-origin resource sharing policies.

Whitelist-based allowed origins
Allowed methods: GET, POST, PUT, DELETE
Credentials support with explicit origins
Preflight caching: 600 seconds max-age
Development vs. production origin policies

🚨 Rate Limiting

Tiered rate limiting to prevent abuse and ensure fair usage.

Global: 1000 requests/minute per IP
Auth endpoints: 10 requests/minute per IP
AI endpoints: 30 requests/minute per user
Streaming: 5 concurrent streams per user
Redis-backed distributed rate limiting

📊 Compliance Patterns

Built-in patterns for regulated industry compliance.

HIPAA: Encryption at rest, access audit trails, BAA support
SOC2: Security monitoring, incident response, change management
GDPR: Data export, right to deletion, consent management
PCI DSS: Tokenized data handling, network segmentation

Additional Security Measures

Layer	Protection	Implementation
Input Validation	Malicious payload rejection	Pydantic v2 schema validation on all inputs; strict type checking; max-length enforcement
SQL Injection	Parameterized queries	SQLAlchemy ORM with parameterized bindings; raw query prohibition; query logging
XSS	Output sanitization	React DOM auto-escaping; Content Security Policy headers; HTML purifier for user content
CSRF	Token-based protection	SameSite cookies; CSRF token validation; Referer header verification
Transport	Encryption in transit	TLS 1.3 mandatory; HSTS headers; Certificate pinning for mobile
Storage	Encryption at rest	AES-256 for sensitive fields; encrypted backups; key rotation every 90 days

Section 08

Database Architecture

Relational data model with SQLAlchemy ORM. Supports SQLite for development and PostgreSQL for production with full migration lifecycle via Alembic.

Entity Relationship Diagram

User

idUUID PK

emailVARCHAR(255) UQ

usernameVARCHAR(100) UQ

hashed_passwordVARCHAR(255)

roleENUM

is_activeBOOLEAN

created_atTIMESTAMP

updated_atTIMESTAMP

Project

idUUID PK

owner_idUUID FK→User

nameVARCHAR(200)

descriptionTEXT

tech_stackJSON

statusENUM

created_atTIMESTAMP

updated_atTIMESTAMP

Conversation

idUUID PK

user_idUUID FK→User

project_idUUID FK→Project

moduleVARCHAR(50)

titleVARCHAR(200)

model_usedVARCHAR(100)

token_countINTEGER

created_atTIMESTAMP

Message

idUUID PK

conversation_idUUID FK→Conv

roleENUM(user/ai)

contentTEXT

metadataJSON

token_countINTEGER

created_atTIMESTAMP

Migration Strategy

📂 Alembic Migrations

Auto-generated migration scripts from model changes
Forward and rollback support
Data migration alongside schema changes
Environment-specific migration paths
CI/CD integration for automated deployment

🔌 Connection Pooling

SQLAlchemy QueuePool: 5 base, 20 overflow
Connection timeout: 30 seconds
Pool recycling: 3600 seconds
Pre-ping health checks on checkout
Read replicas for query distribution

💾 Backup & Recovery

Daily automated PostgreSQL pg_dump
Point-in-time recovery with WAL archiving
Redis RDB snapshots every 15 minutes
Cross-region backup replication
RTO: 1 hour, RPO: 5 minutes

Section 09

API Design

RESTful API with versioned endpoints, consistent error handling, and streaming SSE for real-time AI interactions.

API Endpoints

Endpoint	Method	Description	Auth
`/api/v1/auth/register`	POST	User registration with email verification	No
`/api/v1/auth/login`	POST	Authenticate user, return JWT tokens	No
`/api/v1/auth/refresh`	POST	Refresh access token using refresh token	No
`/api/v1/auth/me`	GET	Get current user profile	Yes
`/api/v1/auth/logout`	POST	Revoke refresh token, invalidate session	Yes
`/api/v1/projects`	GET	List all user projects with pagination	Yes
`/api/v1/projects`	POST	Create new project	Yes
`/api/v1/projects/{id}`	GET	Get project details with stats	Yes
`/api/v1/projects/{id}`	PUT	Update project metadata	Yes
`/api/v1/projects/{id}`	DELETE	Delete project and all conversations	Yes
`/api/v1/conversations`	GET	List conversations with filtering	Yes
`/api/v1/conversations/{id}`	GET	Get conversation with message history	Yes
`/api/v1/modules/{module}/generate`	POST	Generate AI response (streaming SSE)	Yes
`/api/v1/modules/{module}/stream`	POST	Stream AI response via SSE	Yes
`/api/v1/chat/history`	GET	Get chat history for current session	Yes
`/api/v1/admin/users`	GET	List all users (admin only)	Admin
`/api/v1/admin/stats`	GET	Platform usage statistics	Admin

Error Handling

Status	Meaning	When Returned
400	Bad Request	Malformed JSON, missing required fields, invalid parameters
401	Unauthorized	Missing, expired, or invalid JWT token
403	Forbidden	Valid token but insufficient permissions for the resource
404	Not Found	Resource does not exist or user lacks access
422	Unprocessable Entity	Validation error - data format correct but content invalid
429	Too Many Requests	Rate limit exceeded, retry after header provided
500	Internal Server Error	Unexpected server failure, logged and alerted
503	Service Unavailable	LLM provider outage, fallback model attempted

Response Format

// Standard success response
{
  "success": true,
  "data": { ... },
  "meta": {
    "page": 1,
    "per_page": 20,
    "total": 142
  }
}

// Standard error response
{
  "success": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Input validation failed",
    "details": [
      {"field": "email", "message": "Invalid email format"}
    ]
  }
}
      

Section 10

Deployment Architecture

Container-first deployment strategy with Kubernetes orchestration, automated CI/CD pipelines, and multi-environment support.

CI/CD Pipeline

Code Push

→

Lint & Type Check

→

Unit Tests

→

Build Docker

→

Integration Tests

→

Security Scan

→

Deploy Staging

→

E2E Tests

→

Deploy Prod

Docker Configuration

# Multi-stage build for production
FROM node:22-alpine AS frontend-builder
WORKDIR /app/frontend
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM python:3.14-slim AS backend-builder
WORKDIR /app/backend
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

FROM python:3.14-slim AS production
COPY --from=frontend-builder /app/frontend/.next/standalone ./frontend
COPY --from=backend-builder /app/backend ./backend
EXPOSE 3000 8000
CMD ["sh", "-c", "uvicorn backend.main:app && node frontend/server.js"]
      

Environment Variables

Variable	Required	Description
`DATABASE_URL`	Yes	PostgreSQL connection string (production)
`OPENROUTER_API_KEY`	Yes	OpenRouter API key for LLM access
`JWT_SECRET_KEY`	Yes	Secret key for JWT token signing
`JWT_ALGORITHM`	No	JWT signing algorithm (default: HS256)
`ACCESS_TOKEN_EXPIRE_MINUTES`	No	Access token TTL in minutes (default: 25)
`REFRESH_TOKEN_EXPIRE_DAYS`	No	Refresh token TTL in days (default: 7)
`REDIS_URL`	Prod	Redis connection for caching and sessions
`CORS_ORIGINS`	No	Comma-separated allowed origins
`NODE_ENV`	No	Runtime environment (development/staging/production)
`LOG_LEVEL`	No	Logging level (debug/info/warning/error)

📈 Monitoring & Logging

Prometheus metrics collection
Grafana dashboards for real-time monitoring
Structured JSON logging with correlation IDs
Distributed tracing with OpenTelemetry
Alert thresholds for latency, errors, and resource usage
Centralized log aggregation (ELK/Loki)

🏠 Kubernetes Deployment

Namespace isolation per environment
Horizontal Pod Autoscaler (2-16 replicas)
Resource limits: 512Mi-2Gi memory, 250m-1000m CPU
Ingress controller with TLS termination
ConfigMaps and Secrets for configuration
Rolling updates with zero-downtime deployment

Section 11

Configuration

Hierarchical configuration system with environment-specific overrides, feature flags, and runtime-adjustable settings.

Environment Configuration

Category	Key	Default	Description
App	`APP_NAME`	MTS AI Software Factory Enterprise Studio	Application display name
App	`APP_VERSION`	1.0.0	Current application version
App	`DEBUG`	false	Enable debug mode and verbose logging
Database	`DATABASE_URL`	sqlite:///./mtsai.db	Database connection string
Database	`DB_POOL_SIZE`	5	Base connection pool size
Database	`DB_MAX_OVERFLOW`	20	Maximum overflow connections
LLM	`DEFAULT_MODEL`	gpt-4o	Default LLM model for generation
LLM	`FALLBACK_MODEL`	deepseek/deepseek-chat	Fallback model on primary failure
LLM	`MAX_TOKENS`	4096	Maximum tokens per generation
LLM	`TEMPERATURE`	0.7	Model temperature for generation
Rate Limit	`RATE_LIMIT_PER_MINUTE`	60	Global rate limit per user per minute
Rate Limit	`AI_RATE_LIMIT_PER_MINUTE`	30	AI endpoint rate limit per user
CORS	`CORS_ORIGINS`	http://localhost:3000	Allowed CORS origins (comma-separated)
CORS	`CORS_ALLOW_CREDENTIALS`	true	Allow cookies in cross-origin requests

Feature Flags

Flag	Default	Description
`ENABLE_STREAMING`	true	Enable SSE streaming for AI responses
`ENABLE_MULTI_MODEL`	true	Allow users to select different LLM models
`ENABLE_CODE_EXECUTION`	false	Allow generated code execution in sandbox
`ENABLE_AUDIT_LOGGING`	true	Log all user actions for compliance
`ENABLE_TELEMETRY`	false	Anonymous usage telemetry for improvement
`ENABLE_BETA_MODULES`	false	Expose experimental AI modules
`ENABLE_COST_TRACKING`	true	Track and display per-user token costs

Model Configuration

Parameter	Range	Default	Notes
`temperature`	0.0 – 2.0	0.7	Higher = more creative, lower = more deterministic
`top_p`	0.0 – 1.0	0.9	Nucleus sampling threshold
`max_tokens`	256 – 128000	4096	Maximum response length
`frequency_penalty`	-2.0 – 2.0	0.0	Penalize frequent token repetition
`presence_penalty`	-2.0 – 2.0	0.0	Penalize topic repetition
`stop_sequences`	Array	[]	Custom stop sequences per module

Section 12

Domain Support

Pre-configured industry templates, compliance patterns, and domain-specific AI prompts for seven major enterprise verticals.

🏦 Banking & Financial Services

Core banking system development, payment gateway integration, regulatory compliance (Basel III, MiFID II), real-time fraud detection, and open banking APIs.

Core BankingPaymentsPCI DSSSOXReal-time ProcessingAnti-Money Laundering

🩹 Healthcare

HIPAA-compliant system development, EHR/EMR integration (HL7 FHIR), clinical workflow automation, telemedicine platforms, and medical device IoT.

HIPAAEHR IntegrationHL7 FHIRClinical WorkflowsTelemedicinePHI Protection

🛒 E-Commerce

Scalable microservices architecture, payment gateway integration (Stripe, PayPal), inventory management, recommendation engines, and multi-tenant platforms.

MicroservicesPayment GatewaysInventoryCDNSearch & FiltersMulti-Tenant

🛑 Insurance

Claims processing automation, policy management systems, underwriting engines, actuarial modeling platforms, and regulatory compliance reporting.

Claims ProcessingPolicy ManagementUnderwritingActuarialRegulatoryDocument Processing

🏛 Government

Secure citizen service platforms, FedRAMP compliance, data sovereignty requirements, accessibility (Section 508), and inter-agency data exchange.

FedRAMPCitizen ServicesAccessibilityData SovereigntyAudit TrailsEncryption

🏭 Manufacturing

ERP integration (SAP, Oracle), supply chain optimization, IoT data pipelines, predictive maintenance, and digital twin platforms.

ERP IntegrationSupply ChainIoTPredictive MaintenanceDigital Twin MES

🛍 Retail

POS system modernization, loyalty program platforms, omnichannel experience, demand forecasting, and customer 360-degree view systems.

POS SystemsLoyalty ProgramsOmnichannelDemand ForecastingCRMPersonalization

💡 Domain-Specific AI Prompts

Each domain module includes pre-engineered prompt templates that understand industry terminology, compliance requirements, and best practices. Prompts are continuously refined based on domain expert feedback.

🛠 Industry Templates

Starter project templates for each vertical with pre-configured architecture patterns, database schemas, API structures, and security policies. Reduces initial setup time from weeks to hours.

13. Pricing & Licensing

Complete pricing structure for MTS AI Software Factory Enterprise Studio products and services

Product Editions

Community Edition

Free forever

Individual developers, Students, Open source

1 User
Local LLM (Ollama)
Prompt Library
AI Code Generator
Doc Generator
Limited AI Requests

Professional Edition

₹4,999 /month per user

OR ₹49,999/year · Freelancers, Small Companies, Teams

Everything in Community
GPT-5, Claude, Gemini
AI Architect & QA
AI Documentation & DevOps
Unlimited Projects
Team Workspace

Business Edition

₹2,50,000 /year

10 Users Included · Extra User ₹15,000/year · SMEs, Startups

Multi-LLM & RAG
Knowledge Base & AI Agents
Jira, Azure DevOps, GitHub
Docker & Kubernetes
Role Management & Audit Logs

Enterprise Edition

₹10–50 Lakhs (Custom)

Banks, Government, Healthcare, Manufacturing, Telecom

Unlimited Users
Private / On-Premise Deployment
Azure / AWS / Oracle Cloud
SAP, Oracle ERP, Salesforce
SSO, Active Directory, SLA

AI Modernization Package

Service	Starting Price
Java → Spring Boot	₹5 Lakhs
.NET Framework → .NET 8	₹4 Lakhs
Oracle Forms → Web	₹8 Lakhs
PHP → Laravel	₹3 Lakhs
COBOL → Java	₹15 Lakhs
Legacy to Microservices	₹20 Lakhs

AI Consulting Services

Service	Price
AI Assessment Workshop	₹50,000
Enterprise AI Roadmap	₹2 Lakhs
Architecture Review	₹1 Lakh
AI Migration Planning	₹3 Lakhs
Proof of Concept	₹5 Lakhs

Implementation Charges

Company Size	Price Range
Startup	₹2–5 Lakhs
SME	₹5–15 Lakhs
Enterprise	₹20–75 Lakhs

Support Plans

Silver

₹1 Lakh /year

Email Support
Business Hours
Regular Updates

Gold

₹3 Lakhs /year

Priority Support
Dedicated Engineer
Health Check

Platinum

₹10 Lakhs /year

24×7 Support
Dedicated Team
Monthly Architecture Review
AI Optimization

MTS AI Software Factory Enterprise Studio — Technical Architecture Document v1.0.0