The Multi-Model Architecture
Why one model isn't enough. How AESL routes intent to the perfect brain for vision, reasoning, and speed.
Language
The Vision Layer
Multimodal inputs. Understanding screenshots of receipts, legacy software interfaces, and complex documents. Analyzing images, PDFs, and visual data that other models miss.
Massive 1M+ token context window allows it to see the entire business state at once. Can process entire codebases, full document sets, and multi-page invoices in a single pass without losing context.
The Reasoning Layer
Code generation and complex logic. Writing the Python/JSON glue that connects APIs. Building multi-step workflows that require precise conditional logic and error handling.
Superior reasoning capabilities reduce hallucination in critical logic steps. Excels at understanding nuanced business rules, handling edge cases, and generating production-ready code with proper error boundaries.
The Speed Layer
Real-time interaction. Drafting emails, quick categorization, and chat responses. Handling high-volume, low-latency tasks that require immediate feedback to users.
Lowest latency for human-facing interactions. Optimized for conversational speed while maintaining quality. Perfect for tasks where sub-second response time matters more than absolute depth.
The Multimodal Defense Pipeline
Before any image hits a vision model, it passes through 4 hardened checkpoints. This prevents injection attacks, token bloat, and hallucinated execution.
Client-side downsampling to 1024px. Reduces latency by 40% and prevents token bloat.
Pre-flight text scan. Detects and blocks hidden prompt injections or malicious text embedded in pixels.
We don't ask models to 'describe' images. We force structured JSON extraction to ensure precise API mapping.
Vision is intent, API is truth. We verify the image analysis against real-time data before executing.
Why This Matters: Sending raw images to GPT-4o is lazy architecture. It costs $0.04/run and is vulnerable to injection. This pipeline cuts costs by 60% and eliminates visual attack vectors.
The Autonomy Engine™
Models are just brains. They need hands. AESL provides the deterministic runtime, the legal firewall, and the audit logs that turn intelligence into action.
Why Not Just Use ChatGPT?
Single Model
ChatGPT is one brain trying to do everything. Great at conversation, limited at vision, struggles with complex code generation. No audit trail, no API orchestration, no business logic layer.
Multi-Model System
Autonomy Engine™ is a team of specialist brains working in concert. Vision tasks go to Gemini. Logic to Claude. Speed to GPT-4o. With legal guardrails and production infrastructure built in.