designingintelligencedesigning intelligence
Debug and improve ML pipelines and models by pin-pointing issues and simulating fixes. Ranging from reducing hallucinations to ensuring safety.
Backed by
build models with
precision of writing code
Attribution & Training
Every output token traced to the exact prompt span and layer where the answer formed. Stream loss, grad norms, and dead layers live.
Evals & Benchmarks
Consistency, suppression, and boundary probes exposing failure modes benchmarks miss. InterpScore, FeaturePurityScore, MUI metrics.
Security
Trojan scanning, prompt injection, jailbreak probing — full attack surface mapped.
Inspect all major architectures
Transformers & LLMs
Vision transformers
Dense & MoE
Embeddings
Reasoning models
Machine learning
Inspect all major training methods
LoRA
Low-rank adapter.
QLoRA
4-bit base + LoRA adapters.
Full fine-tune
All parameters updated.
DPO
Direct preference optimization.
PPO
Proximal policy gradient from human feedback.
Distillation
Teacher compresses into student.
GRPO
Group relative policy optimization.
Pre-training
From scratch on massive corpora.
The science underneath: Mechanistic interpretability
Mechanistic interpretability reverse-engineers how neural networks compute, not just what they output. Aquin applies sparse autoencoders, logit lens, activation patching, and causal tracing to expose which features fire, which layers encode a concept, and which circuits produce each token. Stop guessing why a model hallucinated, drifted, or refused — trace the answer back to the exact prompt span that caused it and patch at the source.
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Aquin