Sage Nord
Understand and manage your money with AI-powered financial statements, insights, and goal tracking.
AI-generated bedtime stories with interactive narration, soothing sounds, and dimming lights for kids.
Learn English, German, Russian, Spanish, or Italian with AI-tailored reading, writing, and grammar practice.
Guided meditation, breathing, and journaling, with a 3D planet that reflects your mental health progress.
Deterministic hallucination-detection engine for AI agents: verifies model output in under 5ms with zero LLM calls, using symbolic logic instead of asking another model to grade the first one.
MCP-native context delivery engine for coding agents: 96.8% R@5 on LME-500, local-first, and no runtime model, so long-lived conversations stay fast and cache-friendly.
Four working principles that shape every decision we make, from architecture down to how we pair with AI.
Problem framing · TRIZ
Most trade-offs get treated as compromises: faster or safer, simple or flexible. We treat them as contradictions to resolve instead — using TRIZ, Genrich Altshuller's engineering methodology, to push every hard decision toward its Ideal Final Result: the outcome the system needs, without the cost usually assumed to come with it.
Type-driven development
Runtime checks catch mistakes after they ship. We push correctness earlier: if a state is invalid, the type system refuses to compile it. Illegal states become unrepresentable, so whole categories of bugs — nulls, impossible combinations, mismatched units — are gone before a test even runs.
Process & architecture
We don't judge architecture by taste — we judge it by how fast and how safely it lets us learn. Every process and design decision answers one question: does this shorten the loop from idea to code to feedback to the next idea, without trading away stability to get there? It's the same signal DORA's research keeps finding: lead time, deploy frequency, and change stability together, not size or ceremony, predict high-performing teams.
AI-assisted engineering
General-purpose code gives an AI too many ways to say the same thing — which means too many ways for it to be subtly wrong. We constrain AI collaboration with small, typed, embedded domain-specific languages: narrow grammars that state intent precisely, are mechanically checked, and leave no room for hallucinated APIs. The result is AI-assisted code that's verifiably correct by construction — not by luck, and not by trusting the model's word for it.
