Applied AI for decisions under uncertainty
SignalPop builds probabilistic forecasting and decision systems: multi-variate prediction for demand forecasting, anomaly and change-point detection, trend analysis, and the portfolio and risk questions that sit on top of them.
Our Expertise
We work on time-series and large language model problems where the data is rich and the decisions are consequential. Beyond forecasting, our practice covers probabilistic modeling, quantitative analysis and portfolio valuation.
Deep AI experience: from Temporal Fusion Transformers for multi-variate time series, to transformer and large-language models such as GPT and Llama, we choose the model that fits the problem rather than the one that is fashionable. We implement at the foundation level, down to the GPU kernels, so we can explain and fix what a model is doing.
How We Help
Extend your team’s capacity: low-level GPU kernels in C/C++, models in Python/PyTorch, interactive results on an Azure-hosted Next.js site, Python FastAPI REST Servers, MCP Servers, or the integration work in C# and .NET that ties it together.
Build prototypes that prove an idea on your data, designed from the start to move to production quickly.
Debug difficult models to improve accuracy, calibration and reliability.
What sets us apart
SignalPop is founder-led, so the person you talk to is the person who builds the models. Our founder wrote a complete deep-learning framework from the GPU kernels up, so nothing in the stack is a black box to us; we work in C/C++, C#, Python, PyTorch and SQL, and we understand how complex AI and robotic systems behave in production because we have built and run them ourselves. Larger projects are staffed with specialists we have worked with before, under our direction and our accountability. We also understand the patent process from the inventor’s side of the table (see below).
Collaboration & Transparency
All work products live in a private GitHub repository you can access at any time. We hold regular project meetings on Zoom at a time that suits you, so there are no surprises about progress or direction.
PATENT AND IP ENGINEERING
Our founder built and managed a worldwide portfolio of 50 robotics patents at his previous company. Several survived the older re-examination process with every claim intact, and two were the first patents to survive an AIA inter partes review with every claim upheld. We bring that inventor’s perspective to your work: turning existing software or new designs into filings ready for provisional or full applications, mapping the prior-art landscape, and working with your patent counsel on claim construction and office-action responses. On the defensive side, we provide the technical depth needed to build an IPR challenge against an adverse patent.
Get Started Today
Contact us to arrange a Zoom call about your problem. We will tell you plainly whether it is one we can help with.
Our Process
Models change fast; the organizations that benefit are the ones that learn to apply them to their own data. The SignalPop Advisory Service helps you do that inside your existing operations, rather than beside them.
Because we have implemented most of these techniques ourselves in the MyCaffe platform, we can tell you what will work on your data and what will not before you spend money finding out.
From Data to Decision
The goal is better decisions from the data you already have. We get there in three phases: understand your data, design the AI architecture, plan the implementation.
Phase 1 – Understanding Your Data
We start by understanding your data in its entirety: what it is, how it is structured, how often it is collected, and what can and cannot be learned from it. Many organizations hold far more data than they have characterized, and this step decides everything that follows.
Deliverable: a data design document that concisely describes your data profile.
Phase 2 – AI Architectural Planning
With the data understood, we design the system that extracts value from it: data formation, model selection and the training plan. Prototypes test each hypothesis on your data. Every prototype is validated on held-out history with stated error bars, and forecasts are delivered as calibrated ranges rather than single points, so you can see how much to trust them before you rely on them.
Deliverable: the architectural design and system plan, with the prototypes that prove the models.
Phase 3 – Implementation Planning
Implementation planning is the roadmap from validated prototypes to a production system, and it turns data you could not previously act on into information you can. We may recommend third-party components, identify gaps that need custom work, or lay out a full plan for your internal team.
Deliverable: an implementation plan with specific recommendations for integrating the Phase 2 architecture into your operations.
Next Steps
Projects are structured as time and materials plus expenses, or as fixed-price scoped studies with agreed acceptance criteria. Contact us for a free assessment of how we can help you make better decisions from your data.