Research Division

Research & Models

The theoretical work and architectural systems that power everything we build. This is not a blog — it is the documentation of how we think.

Knowledge EngineeringNeural Architecture DesignIntelligence InfrastructureMemory Systems
Research Focus

Research Areas

Six domains where we invest the most research time. These are the foundations of every system we build.

Knowledge Engineering

Foundation

Structuring domain knowledge into learnable representations. How information is organized determines what intelligence is possible. We invest deeply in knowledge architecture before any model is designed.

Neural Architecture Design

Core Research

Custom network topologies for specific problem classes. Not every problem needs a transformer. We design architectures that fit the problem — RNN-CNN hybrids, attention mechanisms, custom decoders, and novel fusion strategies.

Intelligence Infrastructure

Systems

Scalable systems for deploying and operating AI at enterprise scale. Inference servers, vector databases, retrieval pipelines, and monitoring architectures — designed for long-term reliability, not just launch.

Memory Systems

Applied Research

Long-term context, retrieval augmentation, and persistent intelligence. How systems remember, retrieve, and reason over accumulated knowledge across sessions, conversations, and time.

Evaluation Frameworks

Quality Science

Measuring what matters: alignment, reliability, and real-world performance. We build domain-specific evaluation systems that go beyond benchmark accuracy to test genuine intelligence quality under realistic conditions.

Domain Intelligence

Specialisation

Building models that understand the deep nuances of specific domains. Healthcare, legal, financial, personality, and creative domains each demand fundamentally different approaches to architecture and knowledge encoding.

Model Taxonomy

How We Classify Models

Three clear tiers. Select one to explore projects and their architectural diagrams.

Model Selection

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.

Honest Research

Failure Modes & Limits

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

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, optimise 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 behaviour. We are not selling hype. We are solving problems rigorously.

Long-term R&D

Mental Health Neural Networks

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.

Human-Centred 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.

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.