Pallas Analytics

Driving Technological Innovation at Public Institutions

Pallas Analytics engages with educational and public health organizations on how to improve systems and processes by adopting specific technological solutions. Our industry focus includes:

Cell Therapy and the use of AI to reduce supply chain costs as a patient level.
Community and public health efficiencies at point of care.
AI-education for providers.

Our solutions focus on enabling teams to think critically and harness technology as opposed to replacing human capacity. We value systems design and architecture and how it interplays with new and evolving technologies to solve salient problems.

Traditionally, our prototypes focused on data and machine learning modeling solutions. Lately, we are leveraging agentic AI to creating working systems as quickly as possible so that we can get to the root of the problem: uncover the gaps and requirements of an envisioned end-state. In our practice, we aim to transform an agentic idea into a functional, differentiated product or process.

Our thesis remains the same: good products and sustainable technology will always rely on clearly defined designs, an understanding of data sources, and complete control over system architecture. The projects here are experiments in making complex decisions more visible: regulated model review, GxP execution, publishing intelligence, climate-to-sensory risk, and the future of work.

Prototype archive

Projects

These projects were commissioned to explore feasibility. Our approach is to understand the problem, define the architecture, source the data, and then build prototype applications using agentic AI, since we have found that we can quickly build in a matter of days, if not hours. After building, our core work is to identify the gap between prototype and product from a technology perspective.

01 regulated operations Regulated Pipeline CRM This project was built for a biotech startup with limited resources on quality control personnel. We transformed validation plans, requirements, approvals, and operating signals into a live GxP execution pipeline. The end result became a "Control Center" for product and process owners to coordinate and manage the development pipeline of a GxP regulated product. 02 regulated AI Regulated AI Sandbox Within RFP-driven sectors, we wanted to accelerate market entry for smaller machine learning companies to compete for contracts. We designed a governed model evaluation environment built for reviewability, evidence, and deployment decisions. In this system, data sharing is managed based on security clearance, and clients are able to continuously provide model updates in a competitive "Kaggle-style" setup. We liked this idea in helping industry quickly identify promising and state-of-the-art work rather than defaulting to the "old guard." 03 publishing intelligence Translation Scout Moves foreign-market coverage and rights signals into a ranked scouting pipeline. 04 critic intelligence Good Reviews Ranks reviewer and critic fit from industry taste data, review data, source catalogs, and taste vectors. 05 climate and sensory data ClimateSensory Explores climate, sensory quality, and supply-chain risk for wine, coffee, vanilla, vetiver, lavender, and perfume materials.

How to structure curriculums for AI-heavy industries

The Future of Curriculum Development

Lately, our research focus has been on understanding how technical majors can adjust to the changing norm and expectations of junior STEM roles. Our view is to increase the exposure students typically get during their early years on the job, during their time in the classroom. Our work is the measure the impact of curriculum innovation at university curricula and ultimately have students feel prepared with changes to the future of work.

01 working paper The Junior Analyst Problem What changes when entry-level software engineering opportunities are automated or abstracted away. 02 working paper The Hidden Curriculum of Office Work The informal skills students learn only after entering professional environments. 03 working paper The AI Apprenticeship Audit A framework for where AI supports, weakens, or reshapes learning on the job. 04 working paper From Prompt Literacy to Professional Agency Moving beyond tool fluency toward judgment, authorship, and critical adoption.

Contact

How we work

We value in-depth discovery after which we present clients with processes with clear automation potential.
We include prototyping with a design-centered workflow and clear, visible pipelines where the user understands the product implementation.
MVPs include tracing data sources, highlighting gaps instead of hiding them behind a polished-looking demo.
We believe the prototype reveals what architecture, governance, and product surface the domain actually needs. This is where the work begins.

Reach out to discuss prototype development and research collaborations on the future of work. We are happy to share our approaches to the work highlighted above and further research in any of these areas.

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