Neural SDLC Orchestration

9 AI Agents.
One Neural Brain.

From requirements to deployment—autonomous agents collaborate through a self-learning orchestration network.

4.5x
Faster than traditional SDLC
100%
End-to-end traceability
9
Specialized AI agents
<1ms
Context retrieval latency
check_circle Self-improving
check_circle Model agnostic
check_circle GraphRAG powered
description
Requirements
schema
Planning
code
Development
bug_report
Testing
neurology
Neural Brain
rocket_launch
Deployment
auto_graph
Improvement
security
Security
verified
Quality
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Context Awareness

GraphRAG Knowledge Engine

Context stored as semantic relationships in a knowledge graph. The neural engine queries, reasons, and distributes precise instructions to each agent.

Data Sources
description
Requirements
REQ-001: User authentication...
task
Tickets
TKT-042: Implement OAuth flow
code
Code Files
auth.service.ts, user.model.ts
science
Test Results
auth.spec.ts: 24/24 passed
Knowledge Graph
Context REQ-001 REQ-002 TKT-01 TKT-02 TKT-03 TKT-04 auth.ts user.ts api.ts auth.spec user.spec api.spec v1.2.0
data_array Vector embeddings: 1536 dimensions
Requirements
Tickets
Code
Tests
Deploy
Agent Instructions
neurology Neural Engine Query
"Find all code related to REQ-001 and generate test coverage report"
description
Requirements Agent
"Validate REQ-001 acceptance criteria completeness"
code
Development Agent
"Implement OAuth flow in auth.service.ts per TKT-042"
bug_report
Testing Agent
"Generate property tests for auth.service.ts"
shield
Security Agent
"Scan OAuth implementation for OWASP vulnerabilities"
input
1. Ingest
Data enters as entities with semantic embeddings
hub
2. Connect
Relationships form graph edges automatically
search
3. Query
Neural engine traverses graph + vector search
send
4. Instruct
Precise context delivered to each agent
account_tree
Semantic Relationships

Every entity connected through meaningful edges. Query "what code implements REQ-001?" and get instant answers.

bolt
Sub-ms Retrieval

Multi-tier caching (L1 memory, L2 distributed, L3 persistent) ensures agents get context instantly.

target
Precise Instructions

Each agent receives only the context it needs. No hallucinations, no missing information.

Core Capabilities

Built for the Future

Model-agnostic, self-learning, and infinitely adaptable.

hub

GraphRAG Storage

Knowledge graph with vector embeddings for semantic context retrieval.

bolt

Multi-Tier Cache

L1 in-memory, L2 distributed, L3 persistent for sub-ms access.

swap_horiz

Model Agnostic

Swap GPT-4, Claude, Gemini via config. Auto capability detection.

psychology

Self-Improving

Analyzes failures, proposes fixes, modifies own codebase safely.

Autonomous Workforce

The Neural Collective

Nine specialized AI agents working in perfect synchrony.

description

Requirements Agent

Transforms natural language into EARS-pattern SRS documents.

schema

Planning Agent

Creates sprint plans with effort estimates and dependency graphs.

code

Development Agent

Implements features with full traceability across any language.

bug_report

Testing Agent

Generates property-based tests and auto-fixes regressions.

rocket_launch

Deployment Agent

Zero-downtime deployments with automatic rollback.

auto_graph

Improvement Agent

Self-optimizes by analyzing metrics and modifying code.

security

Security Intelligence

Monitors CVE databases daily and predicts emerging threats.

shield

Security Enforcement

Scans code for vulnerabilities and blocks insecure deploys.

verified

Quality Assurance

Enforces best practices and coaches agents to improve.

Comparison

Traditional vs Neural

Capability
Traditional
Ackleom
Traceability
Req → Code → Deploy
Manual & Fragmented
Automated & Complete
Context Storage
How data is organized
Flat Databases
GraphRAG Knowledge Graph
Learning
Adapting over time
Static Processes
Self-Optimizing Neural Net
Security
Vulnerability handling
Periodic Audits
Real-time CVE Monitoring
Data Retrieval
Access speed
Database Queries
Multi-Tier Cache + Vector