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Go AI Framework

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Service Overview

Beyond a Chatbot That Answers Questions — An Enterprise AI Agent That Works on Its Own

Go AI Framework fully understands complex internal data and goes beyond simple answers — autonomously completing entire work processes.

80%↑

Productivity
Reduced time for gathering information and drafting

Zero Leakage

Security
Data leaks blocked at the source

Vendor Free

Sustainability
Free from dependency on any single AI vendor
Why an "AI Framework"? — Background and Pain Points

The Limits of a Single AI Model — One AI Isn't Enough

Relying on a single LLM can't handle the complexity of enterprise work — which is why multiple agents are necessary.
01

Preventing AI Hallucination

AI relying on a single prompt can generate answers that don't match the facts. Go AI Framework only produces reliable answers, through an evidence-based pipeline (FS-Context).
CORE
FS-Context Evidence Mapping·RAG Grounding·Hyperlinked Sources
02

Ensuring Security and Data Sovereignty

Sending sensitive company data to an external AI service carries a leak risk. An agent runtime sandbox ensures critical data is processed only on your internal network.
CORE
Execution Sandbox·Processed Within Internal VNet·RBAC Permission Matrix
03

A Scalable Agent Ecosystem

As your business grows, you'll need new AI capabilities. A low-code Builder Studio lets you add and deploy new agents anytime.
CORE
Builder Studio·Plugin-Based·Agent Marketplace
system architecture

How Multiple Agents Work Together

Each agent handles its own role, while a central orchestrator coordinates the whole system.
User / External System
Enters natural-language requests, receives results
Request / Response
↓
LAYER 01 · Command Layer
C# / .NET Orchestrator
The "manager" layer that directs and controls every AI agent
API Gateway & BFF
Receives requests, authenticates, routes
Distributes user requests to agents
Workflow Planner
Distributes tasks to agents, tracks status
Automatically recovers from workflow errors
Execution Sandbox
RBAC access control, audit trail
Real-time security policy monitoring
Tech Stack MS Agent Framework · API Gateway · Workflow Planner
Task Directives / Result Reports
↓
COMMUNICATION BUS
Redis Intelligence Bus
A real-time communication channel between the Command Layer (C#) and Intelligence Layer (Python)
Ultra-fast message delivery between systems written in different languages
Performance optimized through event-streaming-based asynchronous communication
Tech Stack Redis Pub/Sub · Event Streaming · Memory Sharing
Event Streaming / Data Calls
↓
LAYER 02 · Intelligence Layer
Python AI Agents
A collection of "expert" agents handling documents, search, and decision-making
Natural Language Router
Identifies user intent
→ routes to the right agent
Document Intelligence
Unstructured documents such as PDFs and reports
Layout analysis, knowledge extraction
Search / GraphRAG
Internal knowledge base
Hybrid search (vector + keyword)
Decision Support AI
Policy analysis, risk scoring
Assists expert judgment
Tech Stack LangGraph · Doc Intelligence · Vector + Graph RAG
＋ 14 modules total, including semantic mapping, drafting, vision, and speech
LAYER 03 · Data Layer
Knowledge Hub
The "knowledge warehouse" that stores and manages all knowledge and data
Vector DB (pgvector)
Meaning-based similar-document search
Knowledge Graph (Neo4j)
Reasoning over relationships between entities
Tech Stack pgvector · Neo4j · Hybrid RAG
Hybrid RAG combines both search methods → maximizing accuracy
The Framework's 14 Core Modules

An AI Architecture That Plugs
Into Any Business Environment Instantly

Build a high-performance AI system as fast as possible, using proven engine blocks for orchestration, RAG (Retrieval-Augmented Generation), multimodal capabilities, and more.
01Natural Language IntelligenceNLI
  • Identifying user intent and interpreting it based on domain knowledge
02Document IntelligenceDI
  • Analyzing the layout of unstructured documents and extracting knowledge
03Semantic Mapping EngineSME
  • Mapping company-wide knowledge systems and ontologies
04Intelligent Drafting EngineIDE
  • Automatically generating documents based on company standard templates
05Decision Support AIDS AI
  • Analyzing complex regulations and assisting expert judgment
06Knowledge Retrieval EngineKRE
  • Hybrid vector/keyword search with supporting evidence
07Workflow OrchestratorWO
  • Controlling collaboration between agents and integrating tools
08Agent Runtime EngineARE
  • Managing the security sandbox and execution status
09Builder StudioBS
  • Low-code agent definition and testing
10Governance & MarketplaceG&M
  • Enforcing company-wide security policy and sharing agent assets
11Vision IntelligenceVI
  • Automatic classification and item recognition from images and photos
12Speech IntelligenceSI
  • Converting speech input to text and automatically restoring domain-specific terms
13Graph SearchGS
  • Exploring relationships via a knowledge graph and enhancing search
14Issue Management AIIM AI
  • Automatically classifying and analyzing issues and generating resolution plans
3 Development Principles (Trust, Safety, Transparency)

Thoroughly Ensuring Safety and Accuracy

01
model context protocol

Model Context Protocol

Every AI tool integration and data source access follows the global MCP standard protocol — an open standard, not tied to any vendor, ensuring future scalability.
  • Infrastructure encapsulated via the Docker MCP Toolkit
  • New AI tools integrate instantly through the standard interface
02
human in the loop

The Human Review Principle

At critical decision points, AI acts only as an "assistant." Actions like executing DB queries, approving requests, or changing ontologies always require human review and approval.
  • An intuitive Approval Interface
  • no auto-apply — every automated judgment is presented only as a "recommendation"
03
explainable ai · xai

Explainable AI

The basis for any AI conclusion can always be traced back. Every response is mapped with FS-Context (evidence coordinates), so the source document and page can be checked instantly.
  • Prevents hallucination
  • Every AI query, decision, and API call is recorded in an audit trail
Expected Impact — Before / After

The Change Go AI Framework Brings

Before Adoption (AS-IS)
After Adoption (TO-BE)
✗Gathering information manually via email — takes hours
✓AI agents automatically search and gather internal documents — done in minutes
✗Asking a person for every question — creates bottlenecks
✓Instant answers via natural-language queries, with source documents
✗Scattered internal documents and reports — unusable
✓Automatically analyzed and structured into searchable knowledge
✗Uncertain AI reliability — people avoid using it
✓Sources verifiable via FS-Context evidence mapping — trusted for real work
✗Sensitive data sent to external AI services — a security risk
✓Processed exclusively within an internal VNet with a sandbox — leaks blocked at the source
✗Dependent on a single AI vendor — cost and lock-in risk
✓Built on a proprietary framework — swap in any AI model
✗Adding a new AI feature means full development — takes months
✓Low-code Builder Studio — deploy a new agent within days
Implementation Roadmap (12 Weeks, 4 Stages)

Built Safely Through a Systematic 4-Stage Roadmap

Each stage is independently validated as we go — building with a focus on quality, never rushing the schedule.
1
STAGE 01
Infrastructure Build
INFRA
  • Provisioning the Azure cloud environment
  • Designing the database schema
  • Building and testing the Redis communication bus
Weeks 1–2
Key Deliverables
Requirements Document / Infrastructure Diagram / Detailed Design Document
2
STAGE 02
Agent Build
AGENT
  • Document Intelligence module (PDF parsing, vectorization)
  • Hybrid search engine (Vector + Graph)
  • Basic Q&A agent prototype
Weeks 4–5
Key Deliverables
Agent Prototype / API Specification
3
STAGE 03
Orchestration
ORCH
  • Workflow Planner (.NET-based)
  • Natural Language Router (intent branching)
  • AI dashboard UI development
Weeks 7–8
Key Deliverables
Integrated System Build / Unit Test Results
4
STAGE 04
Validation & Stabilization
FINAL
  • Decision Support AI complex reasoning logic
  • Security governance (RBAC, guardrails)
  • Full end-to-end scenario testing
Weeks 10–11
Key Deliverables
Validation Report / Security Review Report / Operations Manual
Key Use Cases

Go AI Framework Handles Tasks Like These Autonomously

📋
Automated Document Drafting & Approval Requests
Auto-drafts reports and approval requests on company-standard templates / Integrated with the approval workflow
🔍
Internal Knowledge Search
Instantly search thousands of internal documents in natural language, with source pages included
⚖️
Policy & Compliance Review
Automatically cross-checks input against relevant policies / risk scoring / expert assistance
📊
Data Analysis & Reporting
Query data in natural language → auto-generates SQL → visualizes results and surfaces insights
FAQ

자주 묻는 질문 (FAQ)

A simple chatbot just answers questions. Go AI Framework’s agents autonomously carry out multi-step work — document search, analysis, drafting, and more — and can be configured so multiple agents divide roles and collaborate based on task complexity. For example, a single request like “draft this month’s inspection report” is automatically handled from document search, similar-case analysis, and policy review, through drafting and requesting review.

Go AI Framework processes data exclusively within a dedicated enterprise cloud (Azure VNet). While it uses the Anthropic Claude API via Azure to generate AI responses, under the Azure agreement customer data is never used to train AI models, and every processing event is kept in an audit record. All AI actions are logged in an audit trail and can be reviewed at any time.

Yes. It integrates with existing systems via the MCP (Model Context Protocol) standard, with support for GoWorks e-approval and GoData GRAI integration being rolled out progressively; the exact integration scope can be confirmed during an adoption consultation. It supports a range of data sources including MSSQL, PostgreSQL, file systems, and REST APIs.

Every Go AI Framework answer includes FS-Context (evidence coordinates), so you can immediately check which document and page the AI referenced. Critical decisions — such as running a database query or approving a request — always follow the Human-in-the-Loop principle, executing only after human review and approval.

During implementation, a dedicated GowIT team supports everything from Azure environment setup to agent deployment. During operation, the low-code Builder Studio lets even non-developers add and modify agents. Ongoing operational support is also available through an SM (maintenance) contract.

No. Go AI Framework is a proprietary architecture that isn’t tied to any specific AI model. It currently uses Anthropic Claude as its primary AI model, with OpenAI used for embedding technology to improve document search accuracy.

A standard implementation targets 12 weeks (about 3 months), proceeding through 4 sprints with results validated at each stage before moving to the next. Exact cost and timeline depend on the scope (number of modules, data volume, integrated systems), so we provide a customized proposal after an initial consultation.

Yes, absolutely. As an early adoption partner, you get: ① priority adoption and a POC (proof of concept) opportunity, ② direct requirements discussions with GowIT’s AI planning team, and ③ early-adopter pricing after launch. Reach out now and our AI planning team will contact you directly.

Contact Us About Go AI Framework

A professional consultant will review your operational environment and propose the optimal path for adopting the Go AI Framework.
Request a Consultation →
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