Overview
Upstaff partnered with a startup developing a data platform for wildlife nonprofits.
The project aimed to create a first version of the platform which AI-driven automation to streamline reporting processes and help wildlife nonprofits in reporting and securing future funding.
The majority of these organizations used paper records or spreadsheets to manage their documentation, which caused delays and inconsistencies in accounting and reporting. Africa’s rhinoceros conservation teams represented the first users cohort along with other fieldwork teams.
Challenge
The client required engineering support to convert their idea into a viable AI-driven workflow automation and reporting domain specific product. At the time of Upstaff’s involvement several generic prototypes were present but no comprehensive system definition existed.
Upstaff engineers both developed the backend and combined core AI tools to power the platform.
The platform Upstaff has been working upon, needed to process financial data together with field reports and location-based details to provide accurate and useful insights.
The primary challenge involved creating a reliable technical infrastructure capable of meeting real-time requirements for the initial and growing user base.
Solution
Upstaff has developed the AI/ML backend and AWS-based infrastructure with flexibility and automation in mind. AI approach was deeply integrated from the start. The development process covered the following areas:
- Backend microservices: a modular backend using Python, FastAPI and Docker. SQLAlchemy handled relational data access.
- Cloud infrastructure: The system uses AWS with EKS, Elastic Container Storage, and CloudFormation as a platform for scaling and deployment automation.
- AI-powered Analysis: Integrated with AWS Bedrock and Anthropic. Used LangChain, PandasAI, and OpenAI to extract insights from relational data sources. Dynamic document chunking has been developed to to help AI understand and summarize long texts.
- Reports and Maps: Automated PDF and DOCX reports include charts and graphs generated with Pandas and Matplotlib. React frontend with Leaflet for visualizing location-based reports and spatial data.
- Data Pipeline: Used NumPy and Shapely for fast, clean processing of geospatial and numerical data. Upstaff engineers led every step-from backend design to AI integration, data pipelines, and infrastructure setup.
Outcome
Reports that once took days are now ready in minutes. Teams can now efficiently track and visualize conservation data, track insights across regions, and respond faster in the field. The platform handles hundreds of requests per second. The cloud-native, modular system is ready to scale to over 1,500 organizations.
About the Client
A mission-driven startup helping African wildlife conservation nonprofits shift from paper-based reporting to real-time, AI-powered decision-making. Upstaff made that vision a reality.
Power Environmental Impact with AI-Driven Data Platforms
Unlock smarter conservation with AI-powered automation and real-time reporting. Upstaff provides engineering expertise to design and build AI-Driven Data Platforms tailored to nonprofits. Our experts integrate geospatial analytics, automated documentation, and ML-powered insights—helping organizations process complex field data, generate instant reports, and scale impact across regions. Learn more about how to hire AI and ML engineers with Upstaff to bring your mission-driven technology to life.
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