Large Language Models (LLMs)
Aim
Often, companies have data that contain a lot of hidden information. Asking the right questions and analyzing the data will give you relevant insights. The goal is to answer the right questions that will help the company to make the right decisions.
Result
Results are presented in an interactive report with recommendations and follow-up steps. Based on this report, clear and well-founded decisions can be made, costs can be saved and processes optimized.
Project Duration
Project duration can vary between 2 weeks and 3 months. In order to carry out the project as quickly as possible, it is important that the relevant data is available, complete and clean. This might require collaboration with the data engineering team.
Some of the use cases for business LLM adoption include:
Text Generation
Advanced natural language generation for content creation, chatbots, and creative writing applications.
Summarization
Context-aware summarization of documents, articles, and reports with customizable detail levels.
Question Answering
Domain-specific QA systems with citation support and evidence-based responses.
Translation
Multilingual translation with cultural modulation preservation and industry-specific terminology.
Fine-Tuning
Custom model training using domain-specific datasets for specialized applications.
Safety & Ethics
Bias mitigation, content filtering, and ethical AI frameworks for responsible deployment.
Case Study: AI in Action
Protecting Maize Crops in Western Kenya
In early 2024, AgriGuard was deployed across several maize farms in Bungoma County. Within the first two weeks, the system identified a rise in Fall Armyworm activity through user-uploaded images and environmental triggers.
Using our predictive model, the farmers were alerted 10 days before a regional outbreak. This allowed timely intervention with organic treatments, reducing crop damage by over 70% and improving yield outcomes.
Outcome: Over 500 acres saved from infestation and a 40% increase in harvest profitability.
Objective: Develop a user-friendly application for farm assistance
Core Capabilities
Pest Identification
AI models trained to detect over 500 agricultural pests from images with 95% accuracy.
Crop Disease Diagnosis
Early detection of plant diseases through visual symptom analysis and environmental data.
Growth Monitoring
Track plant development stages and predict harvest times using computer vision.
Environmental Analysis
Integrate weather, soil, and satellite data for predictive pest outbreak modeling.
Pest Detection Technology
The client's AI pest detection system reduces crop losses by 35% while improving identification accuracy by 78% compared to manual inspection. The system processes drone and ground-level imagery to identify threats in real-time.
AI Pest Detection Flow
Advanced Detection Architecture
Our pest detection system combines computer vision with environmental data to provide accurate, real-time threat analysis for farmers.
Key Components:
- Multi-Source Imaging: Drone, satellite, and ground-level image processing
- Feature Recognition: Identify pest characteristics and damage patterns
- Environmental Context: Integrate weather, soil, and crop data
- Species Database: 500+ pest profiles with lifecycle information
- Treatment Advisor: Organic and chemical solutions tailored to infestation
Common Pest Detection
Field Analysis Process
Data Collection
Gather target pest images, soil samples, weather data, and crop health indicators.
Image Processing
Analyze visual data for signs of pests, disease, or nutrient deficiencies.
Threat Identification
Classify pests/diseases using computer vision and species databases.
Impact Assessment
Predict crop damage levels and spread patterns based on environmental factors.
Action Plan
Generate targeted treatment recommendations with cost-benefit analysis.
Interactive Farm Assistant
We developed a deployable app that allows a farmer to ask queries based on what they observe on their plants.
Below is an overview of the functionality of the app.
Want to integrate LLMs into business processes? Please get in touch with me here.