Data Science / Computer Vision / NLP

Silicon Valley Enterprise Drives AI Innovation with Outsourced AI Tech Reconnaissance Team

Summary

A top tech Silicon Valley team collaborates with Stratpoint to enhance early-stage AI experimentation without significantly increasing costs by engaging Stratpoint’s team of offshore Artificial Intelligence/Machine Learning (AI/ML) engineers based in the Philippines.

Background

With the recent boom in AI technology and the high demand for ML engineers in the top US enterprises, companies are hard-pressed to find highly-skilled AI resources without going beyond the pre-determined budget. While it may seem obvious that the most cost-effective solution is to go offshore, that approach introduces challenges in effective communication and time zone coordination, especially for complex AI solutions.

Challenge

The Silicon Valley team faced a significant challenge: the need to quickly identify and test the most effective AI technologies. This required a dedicated team with specialized skills in AI/ML technologies, capable of working autonomously and efficiently.

Solution

Stratpoint assembled a specialized AI Tech Reconnaissance team to serve as an extension of the Silicon Valley team. The three-man AI Tech team of engineers possessing expertise in natural language processing, computer vision, and predictive modeling, had the following mission:

  • Find and test the most suitable AI technologies for assigned tasks with minimal supervision.
  • Communicate early results in weekly or biweekly sprints, citing key insights and recommendations.
  • Pivot and adapt to changing directions as needed to support the client team.

During the collaboration, the Stratpoint team undertook several high-impact experiments, including:

Predictive modeling for site suitability
The viability of solar power stations is not the same across any place just because there is solar irradiance. To address the problem of identifying suitable sites, the team was tasked to build a predictive model that can assess and score potential solar station locations. The team researched and identified model features, sourced and transformed data, tested and ran machine learning models, and developed an ML pipeline across AWS, GEE, and local Python platforms.
Geospatial localization
The team ran a series of experiments to explore, test and determine the best approach to geolocate areas in the continental United States, given only an off-nadir aerial image with low metadata. This computer vision challenge had the team investigate both classical and ML-based methods that delved into domains of camera pose estimation, homography, and search space optimization.
LLM-Copilot prototyping
Using Gemini and Langchain, the team prototyped an AI copilot that can assist users in climate modeling. The tool carries abilities such as assisted sourcing of white papers and feature data, and recommending GEE public datasets relevant to any climate modeling objective. Prompt engineering design applies the state-of-the-art “Reasoning and Acting” (ReAct) framework for disambiguation, reducing hallucinations and improving overall response quality.

Outcome

The results of these AI initiatives were transformative. Stratpoint’s AI engineering team demonstrated exceptional ML expertise, agility, autonomy, and communication skills, all rated 10/10 by the Silicon Valley team. The AI artifacts and insights delivered by Stratpoint enabled the client to focus on their core strategic tasks with minimal disruption, ensuring that only the most viable technologies were pursued.

Technologies Used