Technical Architecture of A-AI
A-AI is designed as a modular and transparent architecture, ensuring security, reliability, and adaptability to enterprise environments. Its design follows modern best practices in machine learning and knowledge engineering, while remaining simple enough to be auditable and fully controlled by Authentify It and its clients.
1. Key Concepts and Vocabulary
To better understand how A-AI operates, the following core concepts are essential:
Artificial Intelligence (AI): The simulation of human intelligence in machines.
Machine Learning (ML): A method allowing systems to learn from data without explicit programming.
Deep Learning: A subset of ML using neural networks to model complex patterns.
Data: Raw information used to train, validate, and query AI models.
Model: A mathematical/statistical representation used to make predictions or generate outputs.
Prompt: The input provided by a user to guide the AI’s response.
Context: Additional information or constraints that shape the AI’s reasoning.
RAG (Retrieval-Augmented Generation): A technique combining external knowledge retrieval with generative models to increase accuracy and reliability.
Vector: A numerical multidimensional representation of information, enabling semantic understanding.
2. End-to-End Workflow of a Query in A-AI
When a user sends a request to A-AI, the system follows a structured workflow:
Preprocessing: The query is cleaned, tokenized, and transformed into a machine-readable format.
Vector Representation: The query is converted into a vector embedding that captures its semantic meaning.
Similarity Computation: A-AI calculates the similarity between the query vector and its knowledge base using methods such as cosine similarity or Euclidean distance.
Knowledge Retrieval: Relevant information is retrieved from dedicated, validated corpora (e.g., Authentify It’s internal documentation, regulatory manuals, domain-specific datasets).
Context Integration: The retrieved knowledge is combined with contextual rules and domain-specific constraints to refine the scope of the answer.
Response Generation: A-AI generates a coherent and accurate response, grounded in both the retrieved knowledge and the applied context.
Post-Processing & Validation: Before delivery, the output can be filtered, validated, and adapted to match security, compliance, and brand requirements.
3. Prompt vs Context
A-AI distinguishes between two complementary concepts:
Prompt: The explicit input from the user (e.g., a question or instruction).
Context: The background information, rules, and constraints that define how A-AI should interpret the prompt.
This dual mechanism ensures that A-AI produces outputs that are accurate, relevant, and compliant with enterprise rules, rather than generic or uncontrolled.
4. Modular and Domain-Specific Architecture
A-AI is structured to allow specialization per use case:
Core AI engine (general reasoning and communication).
Domain-specific modules (Documentalist, Support, Vision, etc.).
Integration with enterprise datasets via RAG pipelines.
Secure deployment across on-premise or cloud environments.
This modularity allows A-AI to scale efficiently, adapt to new business needs, and remain fully under client control.
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