Ask Magoya AI

Internal Need

Magoya internal operations team supporting cross-functional staff including agronomists, engineers, product managers, and analysts.

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Challenge

Accessing institutional knowledge was inefficient and fragmented—spanning technical documentation, project notes, and internal research stored across platforms. This slowed project execution and resulted in inconsistent outputs across teams.

Solution

Magoya built an internal AI assistant to centralize access to operational, agronomic, and technical knowledge. The assistant enables:- Natural language queries across structured and unstructured content- Context-aware summarization of long-form technical documents- Transparent source attribution and document-based grounding- Seamless integration with internal tools and workflows.

Approach

Magoya assembled a specialized cross-functional team to lead the build:- Conducted internal research to identify content silos and workflow gaps- Developed a domain-tuned RAG (retrieval-augmented generation) system- Embedded a feedback loop to capture usage insights and fine-tune performance- Iterated rapidly using short sprint cycles for integration and UX alignment

Results

The assistant significantly improved response consistency, reduced time spent locating documentation, and empowered teams to move faster with better-aligned knowledge access.

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Tech Stack

Agent Orchestration: LangGraph on FastAPI (Dockerized)

Frontend: React

Extract Storage: S3

Load Storage/Vector Search: PostgreSQL + pgvector

Session Memor: Redis

Document Preprocessing: Markdown normalization, metadata enrichment, URL crawling (LangChain doc transformers, crawl4ai, etc)

Summarization Model: Fine-tuned LLaMA 3.1 8B for summarization and chunk contextualization (Unsloth + Comet)

Large Model: GPT-4

Evaluation Layer: Opik

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