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Alioth AI Platform™
From Task Analysis and Context Generation to AI Agent Coding
All-in-One Air-Gapped AI Development Platform
With Alioth AI Platform™, Planner generates verified Context, and Coder writes precise code based strictly on the verified Context, delivering defect-free software development.
01 TASK Definition & Analysis
Analyzes source specifications, clarifies requirements, and divides into functional units.
02 Design & Context Generation
Derives verified 3-Layer Context with confirmed specifications based on a Single Source of Truth.
03 Context Handover
Standardized Context delivery between Planner and Coder minimizes AI hallucination and wasted tokens.
04 Agentic Coding
Establishes precise code planning and generates source code strictly from verified Context.
05 Regression & Verification Build
Executes 4-stage self-regression and build checks, automatically feeding back errors to Planner.
AI Collaboration Principles
Clear separation of roles and organic linkage between Planner and Coder
- Planner : Document analysis, requirement clarification, part-by-part Context finalization
- Coder : Precision Agentic Coding strictly based on confirmed Context
- Context Contract : Context standardization between Planner and Coder eliminates hallucinations and excess tokens
- Regression Loop : 4-stage automated self-verification with immediate feedback loops
Context Standardization
1 Screen = 1 Package = 1 Mapping
- Common Context : Enforces project-wide architectural guidelines and conventions
- Screen Context : Defines granular task instructions for a single screen unit
- Essential References : Excludes unnecessary code and points only to required assets
- Error Minimization : Eliminates token noise, drastically reducing AI errors and GPU compute
On-Premise & Open LLM
Direct in-house operation of open-source LLMs
- Absolute Security : Full isolation from external networks, fundamentally preventing code leaks
- Multi-LLM Switching : Host and toggle models such as Gemma, Qwen, Chat OSS, and Kimi
- Harness Engineering : sLLM-optimized pipeline with end-to-end prompt compaction
- Cost-Effective : Built-in enterprise deployment with zero token-based variable charges
Core Competitiveness
Direct on-premise air-gapped installation fundamentally prevents data leakage, while an adaptable open-source multi-LLM framework optimizes both cost and performance.
| Category | Alioth AI Platform | Public Cloud AI |
|---|---|---|
| Deployment Method | Direct On-Premise Enterprise Installation | Cloud-based SaaS Subscription |
| External Data Transmission | None (Fully Internal Air-Gapped Operation) | Required (Transmitted to External Cloud Servers) |
| Source Code Leakage Risk | Fundamentally Blocked (Internal Isolation) | Risk of IP Leaks & Unintended AI Retraining |
| Model Architecture | Multi-LLM Switching (Gemma, Qwen, etc.) | Locked to Single Commercial Vendor Model |
| Cost Structure | Unlimited Usage Post-Deployment (Zero Usage Fees) | Metered Pay-as-You-Go per Token (Ever-Increasing Costs) |
| Financial Security Standards | Optimized for Network Separation & Compliance Audits | Requires Complex Exceptions & External Approvals |
| Internet Connection Requirement | Not Required (100% Offline Operation) | Constant High-Bandwidth Internet Required |
Service Architecture
Planner, dedicated to planning and requirements analysis, organically collaborates with Coder, responsible for precision agentic coding, completing a defect-free pipeline.
Alioth Planner
Analyzes user requirements and selectively extracts confirmed rules to generate standardized Context.
- Requirement analysis and single source confirmation per screen/function
- 3-Layer Context generation stripped of irrelevant noise
- Pre-injection of project architecture rules (Constitution)
- Standardized deliverables delivered per 1 Screen = 1 Package unit
Alioth Coder
Overcomes sLLM limitations using verified Context to craft high-quality source code.
- Intelligent task subdivision via Job Splitter
- GPU savings through Prompt Compactor compression
- Precision coding based on enterprise Knowledge Data guidelines
- Automated security inspection complying with financial standards (Quality Guard)
1. On-Premise & OpenSource LLM
Hosts standalone Open LLMs within the corporate network, completely blocking external threats and guaranteeing security.
2. Automated Model Switching per Task
Automatically transitions between optimal models suited to task complexity, from analysis to coding.
3. Flexible Model Upgradeability
Provides an agile architecture allowing newly released open-source models to be verified and swapped in instantly.
Alioth Planner Core Features
Supplies only confirmed specifications to AI, eliminating errors and token costs while offering full auditability.
Confirmed-Only AI Delivery
Only verified requirements and specs are authorized into the AI work scope, preventing errant attempts and operational mistakes.
Single Source of Truth
Centrally manages requirements, screen definitions, and API specs within Planner to guarantee cross-artifact consistency.
Essential-Only Compaction
Includes only confirmed rules and specs in the payload while indexing remaining data via file paths, minimizing AI overhead.
AI Scope Isolation
Constrains function-level AI sessions to touch only designated files, strictly preventing unintended changes in other areas.
Pre-Injected Project Rules
Enforces strict compliance with coding standards and architectural principles right from session initialization.
Automated Regression on Failure
Instantly feeds back build or test failures into Planner for immediate re-analysis and automated code correction.
End-to-End Auditability
Preserves complete and transparent audit trails from requirement modifications to final AI code generation.
Automated Deliverable Generation
Automatically generates formal project deliverables including function inventories, WBS, and menu site maps.
Role-Based Collaboration Space
PMs, planners, developers, and analysts collaborate within a unified workspace to review specs and exchange feedback.
Alioth Coder Core Features
Empowers compact sLLMs to accomplish massive enterprise workloads through advanced prompt compaction and bank-grade security verification.
Intelligent Task Subdivision
Optimally segments extensive enterprise specifications into bite-sized units that sLLMs can process with extreme precision.
Internal Knowledge & Guide Injection
Progressively injects in-house standard frameworks and best-practice sample codes to guarantee consistent quality.
Full-Pipeline Context Compaction
Precision compaction algorithms allow sustained multi-turn sessions and massive codebase generation on limited GPUs.
Automated Quality & Security Audits
Automatically executes build verification and scans for security vulnerabilities according to strict financial compliance standards.
Code Revision History Tracking
Fully records and inspects granular modification steps and snapshot versions produced by AI agents.
Fully Air-Gapped Offline Packages
Provides self-contained VSIX extensions and standalone CLI packages tailored for strictly disconnected network environments.
2-Channel Developer Interfaces
Offers both an interactive IDE extension for conversation-driven coding and a robust CLI for background batch processing.
3-Tier Safety Approval Control
Combines 3-tier safety execution (Allow / Deny / Ask) with checkpoint rollbacks to instantly revert undesirable modifications.
Heavy Batch Task Scheduler
Autonomously executes extensive overnight refactoring, test suite generation, and build verification during off-hours.