4. Methodology

4.1 Feeda Labs Methodology

Feeda Labs is the foundational AI training and development division of Feeda. We specialize in building domain-specific AI Agents (AIA) and AI-enhanced copilots that combine leading large language models (LLMs) with human-in-the-loop (HITL) collaboration via our proprietary Feeda aiOS.

Feeda Labs transforms AI from a generic tool into a purposeful, actionable system by aligning the best of AI performance with domain expertise and verified human intelligence. Below are four key points on which feeda labs's methodology is based on;

4.1.1 Understand

“AI that understands because it's trained by those who know.”

Feeda’s foundation rests on contextual intelligence. Our team custom-trains domain AI agents using knowledge from real professionals (lawyers, realtors, football agents, chefs, etc.) via:

• Feeda Kwiki (Knowledge Wiki) for domain knowledge base management.

• Domain Wrappers that act as semantic layers over raw LLMs.

• Prompt Engineering Pipelines optimized for context retention and specialization.

Through this, our AI agents don’t just parse text—they understand intent and execute domain-relevant logic.

4.1.2. Generate

“Best-in-class LLMs, wrapped with task-aware precision.”

Feeda aiOS is model-agnostic and multi-modal, giving each AI Agent access to the world’s best open and closed-source models like GPT-4, Claude, Mistral, Gemini, and LLaMA. Via Feeda’s infrastructure:

• Our AIA Wrappers choose the best model per prompt or chain them.

• Real-time agent augmentation is provided through Feeda Apps, Agents, and SDKs.

• The Feeda Store gives developers and users access to AI Agents built using this framework.

Feeda AI Agents are not just outputs—they’re outcomes.

4.1.3. Private + Proprietary RAG Systems

“Proprietary intelligence, securely delivered.”

Unlike open internet-trained models, Feeda Labs enables businesses to build agents on secure Retrieval-Augmented Generation (RAG) using:

• Feeda Kwiki + custom vector databases.

• Integration with private datasets, CMSs, and CRM systems.

• Granular access controls across users, departments, or endpoints.

Your business logic, insights, and workflows—encoded and protected for agentic use.

4.1.4. Validate

“Results that are verified, not guessed.”

Feeda agents validate outputs using multi-layer evaluation pipelines:

• Real-time citations and data source attribution.

• Multi-agent chain-of-verification, comparing outputs from several LLMs and knowledge contexts.

• Human oversight via Feeda HooP (Human-in-the-Loop Product Studio).

We build AI agents you can trust to execute with clarity and compliance.