
Numantic Solutions is a full-stack data consultancy working with clients to build and leverage strategic datasets.We help our customers build powerful data and AI products and applications that move their organizations forward and advance their missions.If you think AI can do more than just make your staff more efficient, we should talk. Take a look around our site or contact us to get some ideas on data and AI can scale your initiatives.You’re welcome to reach out even if you’re still exploring what’s possible.
Recent Projects
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We're a data consultancy helping clients leverage their data assets. We assist throughout the data lifecycle from the collection and curation of data to the analysis of data to gain insights to the use of analytical results in setting strategy to the deployment of machine learning models in production environments.For more information: ⦿ Meet the team ⦿ View Recent Projects
The Origin of Our NameNumantic is a portmanteau blending numeric and semantic to reflect our focus on delivering quantitative and programmatic solutions that address real-world problems and capitalize on untapped opportunities.Our advanced product management and software engineering expertise provide a foundation for identifying opportunities, defining objectives and deliver data solutions.
How We Can HelpIf you are in the early stages of building a data foundation, our services offer: ⦿ Lower costs compared to hiring full analytical and engineering teams ⦿ Faster delivery by employing our extensive experience deploying data-science solutions in production ⦿ More valuable solutions by utilizing our product management approach to data-system design
If you already have an established data science environment, we can:
⦿ Augment your teams with senior programming, modeling, machine-learning and AI resources ⦿ Complement your data-science approaches and methodologies with new perspectives from broad industry and project experience
Our ClientsWe work with organizations of all sizes across industries and locations. Many of our clients are in the social impact space, and we're passionate about helping them help the world.
Our LocationWe are based in Palo Alto, California and work with clients across the U.S. and throughout the world.

We can help in the following areas, but to summarize we have extensive technical, product and research experience.
Advanced Data Engineering – Data engineering, ETL processes, structured and unstructured types, storage and management, and database design and optimization
Machine Learning & AI Solutions – Intelligent model development for generative AI, predictive analytics, natural language processing, and image handling
Custom Data Visualization & Dashboards – Interactive and intuitive analytics using open-source and cloud tools
Sensor & Measurement Analytics – Processing and analysis of real-time sensor data for informed decision-making
Technical Product Management – End-to-end development and execution of product strategies leveraging data
Research & Development – Cutting-edge innovation in statistical modeling, automation, and AI-driven applications
We've worked extensively with these tools, but we're happy to quickly learn new things:
Languages - We mostly work in Python and SQL, but we've also used other languages such as R, C++, JavaScript & VisualBasic
Libraries & Frameworks - Beautiful Soup, Django, Fake Data, FastAPI, Flask, Geopandas, Matplotlib, Pandas, Plotly, Puppeteer, Seaborn, Selenium
AI & Machine Learning - ChatGPT (API), Gemini (API), Google Vertex AI, Google Agent Development Kit (ADK), TensorFlow, Hugging Face, LangChain, Microsoft's Platform for Situated Intelligence (PSI), PyTorch, Scikit-learn, Stable Diffusion, Gensim (Doc2Vec)
Databases - BigQuery, MySQL, Postgres, Snowflake, Teradata
Cloud Computing - Amazon Web Services (AWS), Google Cloud Platform (GCP)
Application Programming Interfaces (API) - CenPy, Enimoh, GitHub (API), Google Docs (API), MS Office 365 (API), PRAW (Reddit API), Tweepy (X/Twitter API), LinkedIn, Zinc
Tools - Asana, Confluence, GitHub, Google Colab, Jira, Jupyter, Notion, PyCharm, Sharepoint

What it's Like to Work With Us
We work collaboratively and transparently. Our goal is to uncover opportunities, develop strategies to capitalize, design achieve plans, and deliver work that has real operational value.You’ll always know: ⦿ What we're doing ⦿ Why we’re doing it ⦿ How it enables your goals
Our Engagement Model
Step 1: Introductory Call (60 minutes)The introductory call is a 60-minute conversation designed to understand: ⦿ Your goals and constraints ⦿ What data you have (and what you don't) ⦿ What success would look like for your organizationIf it’s a good fit, we’ll recommend either an assessment or moving directly into a project.
Step 2: AssessmentAn assessment is a paid engagement designed to clarify direction, identify opportunities, and reduce risk before any build work begins. Clients walk away with actionable insight whether or not they proceed with a project.It typically includes: ⦿ Review of data, systems, and constraints ⦿ Identification of opportunities, costs and risks ⦿ Evaluation of project viability ⦿ Development of an action plan with clear recommendations for next stepsTypical cost: ⦿ $1,750–$3,500 (nonprofits usually on the lower end)Typical duration: ⦿ Assessments can range from a couple of days to a couple of weeks, depending on scope and data access.
Step 3: Project WorkProjects are where design and implementation happen.
This may include: ⦿ Development and deployment of data pipelines and database infrastructure ⦿ Design and construction of analytical and reporting systems ⦿ Build, evaluation and deployment of AI and machine learning applications ⦿ Composition of documentation and establishment of operational processesTypical project range: ⦿ Costs vary depending on project scope and complexity, but a lot can be accomplished with spending in the $10,000 - $50,000 range.
Frequently Asked Questions (FAQs)
How Long Does It Take? ⦿ Introductory call: 60 minutes ⦿ Assessments: A few days to a couple of weeks ⦿ Projects: Length varies based on scope, complexity and readiness, but initial output is available 3 - 6 weeks after the project's start
Do You Offer Nonprofit/Social Impact Pricing? ⦿ Yes. We regularly work with mission-driven and nonprofit organizations and offer reduced assessment pricing when appropriate.

Reach out - We Want to Chat!
Curious about how data or AI can further your mission and grow your business?Reach out; We enjoy listening, learning and brainstorming.There are no fees for engagement calls.Contact us by booking a meeting or via LinkedIn, GitHub or the form below.

⦿ Steve Godfrey is a technical product manager and data scientist. In those roles, he helps organizations leverage data and analytics to improve their products and services and to support their missions.He frequently writes and speaks about tools and methods to manage, analyze and model datasets as well as applying data analytics in real-world use cases.His professional career has been in financial and technology services working for investment management, banking and financial technology firms including PayPal in data science and Wells Fargo and Bank of America in currency trading, global payments and risk-management research. In addition, his volunteer work includes evaluating statistical techniques, constructing machine-learning pipelines and software consulting for organizations such as Statistics Without Borders and the Technology Accelerator Fund.He holds an MBA and BS Mathematics/Applied Economics from UCLA and completed a data-science bootcamp. Steve lives in Palo Alto, CA with his wife, has two grown children and enjoys travel and outdoor activities and working with wood and in the garden.

⦿ Nathan Moroney is an experienced research engineer. He has worked across organizations to invent, develop and ship a range of technologies. Nathan has written over 90 papers and patents with over 1,000 combined citations. He has been collecting and analyzing web-based data sets since before the term crowdsourcing was coined.He started as a research consultant at the RIT Research Corporation and then joined Hewlett-Packard Español (Barcelona) as a writing systems engineer. Nathan then transferred to HP Labs (Palo Alto) where he contributed to imaging, data, 3D, sensor and machine learning projects. His accomplishments as a principal scientist were recognized with the awarding of Fellow status at the Society for Imaging Science and Technology.Nathan has a BS degree from Jefferson University and Master of Sciences degree from the Rochester Institute of Technology. Both of his degrees are in the field of color science and his masters research topic was color space selection for JPEG image compression. He and his wife are Palo Alto residents with two grown children. When he cooks, it is often with one (or more) skillets and his current deep reading project is about rivers of the world.
Generative AI
Social Impact
Data Science

⦿ Overview: An AI tool providing in-depth information to stakeholders in policy decisions affecting California Community Colleges⦿ Our contribution: The CCC Policy Assistant report-writing bot is an artificial intelligence (AI) agent trained on policy topics related to California's Community Colleges.The target audience are stakeholders who would like to participate in community college decision making and would benefit from curated and detailed information related to community colleges. Some examples might include board members, administrators, staff, students, community activists or legislators.Our goal in creating it is to demonstrate how advocates can use technology to help further their missions.Some sample questions include:- What workforce training programs do California Community Colleges offer?- What percentage of California community college students receive needs-based scholarships?- How does proposition 98 affect California community college funding?- How many California community colleges partner with the California Department of Corrections and Rehabilitation (CDCR) to provide in‑person courses?⦿ Resources:
- CCC Report Writer Bot
- GitHub

⦿ Overview : A data-driven web application helping schools improve their mental health, trauma, social emotional learning and equity student supports.⦿ Our contribution : Using Python and cloud database tools, we helped build pipelines to collect, clean and enhance both publicly available and privately procured data. Data are stored in a data warehouse within a cloud data science environment and are available research, analysis and pre-processing before being passed to a front-end web application for presentation to school users. In addition to data analytics, the Numantic team provided product management expertise and assistance in designing early versions of the application.⦿ Resources:
- RSSI
- New online platform offers educators tools to support student mental health
- Welcome video

⦿ Overview: Through a Statistics Without Borders engagement, we helped Brazil's Institute for Ecological Research (IPÊ) redesign their research-data engineering infrastructure. Rethinking the platform was required because their expanding research projects are generating large amounts of new data types including LiDAR from drone imaging and audio recordings from jungle sound recorders.⦿ Our contribution: Our platform and operational recommendations addressed: - Data-management best practices,
- Data-management resources and teams,
- Version-control tools for data-enrichment scripts,
- Practical actions to free data-storage capacity.⦿ Resources:
- Bridging Data and Biodiversity

⦿ Overview: A natural language processing (NLP) sentiment analysis tool pulling relevant Reddit posts and X (Twitter) tweets and assessing sentiment about homelessness in the Capital Region of British Columbia. This project was a collaboration between Statistics Without Borders and the Alliance To End Homelessness in the Capital. The work is being presented at the 2024 Joint Statistical Meetings (JSM).⦿ Our contribution: Using Python, we built a data-collection pipeline that employs application programming interfaces (API) from X (Twitter) and Reddit to collect tweet and post data and machine learning models to assess their relevance and sentiment. The pipeline runs on a Google Cloud instance and results are published to a public website.⦿ Resources:
- GitHub

⦿ Overview: Thousands of new patent drawings are published each week by the US Patent Office – how can we better search this collection of images?⦿ Our contribution: We leverage image vector embeddings to create a database of weekly Patent Gazette drawings. This database is then queried using text descriptions, example images or categorical prompts. The embeddings and retrieval are performed using a Python toolbox running locally to achieve more stringent privacy requirements.⦿ Resources:
- GitHub

⦿ Overview : How many color terms does a text to image generative AI learn through its training process?⦿ Our contribution : We developed batch mode prompt rendering using Python and proposed an initial automated analysis process.⦿ Try it out : The interactive demo is shown below, use the sliders to explore different prompts.⦿ Resources:
- Conference Proceedings Paper
⦿ Overview : What statistical methods should be employed to account for survey design effects?⦿ Our contribution : In this blog, we discuss a recent collaboration between Statistics Without Borders (SWB) and IMPACT Initiatives addressing survey design effects in the Multi-sectoral Needs Assessments (MSNA) conducted by IMPACT in countries experiencing humanitarian challenges and crises.⦿ Resources:
- Blog
⦿ Overview : How can current AI tools be used by a data analyst?⦿ Our contribution : In this electronic book on AI Data Analysis, we explore using Large Language Models (LLM) to analyze datasets with the goal of developing some operating instructions or guidelines on how to effectively use these tools in the data-analysis task.⦿ Resources:
- Blog
⦿ Overview : What pandemic-relief resources are available to small businesses?⦿ Our contribution : This video and paper cover a collaboration between Statistics Without Borders (SWB) and Client to Consultant Bridge (C2CB) to develop and deploy a multistage data pipeline to automatically curate a national list of small-business aid programs during the COVID-19 pandemic shutdown period. The pipeline's results were presented at a website and helped small business users efficiently research and find relevant aid programs.⦿ Resources:
- Chance Journal Paper
⦿ Overview : How can data scientists add rigor and structure to their modeling experiments?⦿ Our contribution : This blog post discusses approaches and tools that can help machine learning modelers organize, evaluate and record their experiments with algorithms and configurations. The post explores tools though a practical demonstration in malaria image-detection use case.⦿ Resources:
- Blog
Presentations
Writings
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