Research & Models

This page explains how we think, not what we sell. It's an internal research wiki made public.

We document our thinking across multiple dimensions: research areas we focus on, model types we build, when we use different approaches, and where existing solutions fail.

This is where we break down our decision-making openly. It builds trust because it shows rigor.

Research Areas

Representation Learning

How to structure data and learned representations so they capture domain knowledge. What makes a good feature? How do we enforce meaningful structure before training?

Domain-Specific Intelligence

Building models that understand the nuances of specific domains. Custom architectures for custom problems. Not generic, always particular.

Data-Centric AI

The belief that data organization matters more than model complexity. We invest heavily in understanding data before building models.

Model Failure Analysis

Understanding where and why models fail. Documenting edge cases, latency constraints, and failure modes. Building systems that gracefully degrade.

Human-Aligned Systems

Building AI that operates within clear human values. Particularly important for mental health and medical systems where alignment is non-negotiable.

Efficient Intelligence

Creating models that do more with less. Lower latency. Smaller footprints. Better interpretability. Never sacrificing capability for efficiency.

Model Taxonomy

We think about models in clear categories. Select a tier to explore projects and their architectural diagrams.

Model Usage Philosophy

How we think about model selection and deployment.

We do not default to APIs

Off-the-shelf models are a starting point, not an ending point. We choose them thoughtfully, not by default.

LLMs are powerful for some problems

Language understanding, reasoning, generation. LLMs excel here. We use them where they shine.

LLMs are harmful for others

Structured prediction, real-time constraints, interpretability requirements. LLMs often overcomplicate and introduce brittleness.

Control matters

Custom systems give us control over latency, cost, failure modes, and intellectual property. This matters for serious work.

Fit drives architecture

We choose the right tool for each problem. Sometimes that is a large model. Sometimes it is a carefully tuned classifier.

Bottom Line: We think deeply about model selection. We do not use a hammer because it is shiny. We use it because the problem requires it.

Failure Modes & Limits

This is rare and valuable. We document where AI fails, and what we do about it.

Hallucinations

LLMs generate plausible but false information. Our approach: Constrain outputs, verify against structured data, use verification layers.

Data Drift

Real-world data changes. Models trained on yesterday's data may fail today. Our approach: Continuous monitoring, regular retraining cycles, robust validation.

Latency Constraints

Some problems require sub-millisecond responses. Large models are impossible. Our approach: Right-size architectures, optimize for your latency budget.

Infrastructure Limits

GPUs are expensive. Some systems require edge deployment. Our approach: Design architectures that fit real infrastructure constraints.

Interpretability Loss

Black-box models harm trust in high-stakes domains. Our approach: Build interpretable systems where it matters. Accept opacity only when necessary.

Cold-Start Problems

New domains with limited data. Our approach: Leverage domain knowledge, synthetic data carefully, human-in-the-loop validation.

Important: The fact that we document these failures signals real research behavior. We are not selling hype. We are solving problems rigorously.

Long-Term R&D: Mental Health Neural Networks

Why We Focus Here

Mental health is a domain where AI can provide meaningful impact, but only if built with deep domain understanding and human alignment. Off-the-shelf solutions are inadequate.

Our Approach

  • •Human-Centered Design: Every decision validated with mental health professionals.
  • •Interpretability First: Models must be explainable. Black boxes have no place in mental health.
  • •Failure Mode Focus: We document where the system fails and what happens when it does.
  • •Data Ethics: Privacy, consent, and data ownership are non-negotiable.

Current Status

This is active R&D. We are building systems in collaboration with mental health researchers and practitioners. This work is not yet deployed commercially. It is research that will eventually lead to high-impact systems.

Why This Matters

This work signals our values. We build AI for impact. We invest in hard problems. We do not cut corners on ethics. If you are working on mental health or other high-impact domains, this is the kind of partner you want.