AI for Intelligence
What Namířeno taught me about translating expert language with AI
The three layers Namířeno ships
Namířeno reads Czech parliamentary legislation and produces three layered outputs:
- Source ingestion. Direct feed from the authoritative source (Czech Parliament's legislative tracker), with full chain of custody to the original document. No scraping aggregators; no second-hand summaries.
- AI-assisted translation. Domain language (legalese, procedural shorthand, cross-references to other acts) compressed into plain language without losing the technical precision a lawyer needs.
- Signal detection. The trained eye, encoded. Weak signals, amendments in-flight, procedural patterns most automated tools miss, flagged by rules I'd written manually for years, now executed automatically.
What I learned about layer 2
Layer 2 is where most people assume AI is doing the magic. It is and it isn't.
AI translation of expert language works when:
- The source corpus is bounded. Czech parliamentary legislation has perhaps 5,000 distinct procedural patterns. That's a corpus a system can be evaluated against. Translating any legal text in any jurisdiction is not a tractable problem; translating this jurisdiction's parliamentary output is.
- The failure mode is detectable. If the translation drifts, a lawyer reading both the original and the translation will spot it. The deployment workflow keeps the original visible, side by side. If the output stood alone, the failure mode would be invisible.
- The human-in-the-loop reads the original. Not "spot-checks." Reads. The translation is a scaffolding for attention, not a replacement for reading.
Where it predictably fails
When any of those three conditions slip, the failure is predictable: confident-sounding plain language that drifts from the source in ways only a specialist would catch. The risk profile is "subtle wrong" rather than "obviously broken," which is the worst kind of failure mode for downstream decisions.
The architectural fix isn't better prompts. It's structural, present the original prominently, surface confidence indicators per claim, design the workflow so the user has to look at the source.
This is the methodology pattern I'm carrying into the patent / IP intelligence tool we're building next. Same constraints: bounded corpus (a defined claim space), detectable failure (an IP attorney can spot drift against the original claim), human-in-the-loop reading both.
Why "subtle wrong" is the real risk
The failure mode I keep designing against has a name in the intelligence community: AI hallucination, output that is fluent, confident, and wrong. Practitioners working in competitive intelligence and OSINT now treat it as a structural property of language models, not a bug you can prompt away: the model predicts plausible text, it doesn't verify facts (Competitive Intelligence Alliance, 2025; Indago, 2026).
The fix that survives contact with real work is architectural, not prompt-level: keep a deterministic evidence layer underneath the model, and use AI only to interpret evidence that already exists and can be inspected (Metrivant, 2026). That is exactly Namířeno's design: the original text stays visible, every claim traces back to a source. WIPO took the same two-stage route in its 2024 GenAI patent landscape: a high-recall search, then a trained classifier to lift precision, with humans in the loop (WIPO GenAI Patent Landscape, methodology).
Sources
FAQ
What's Namířeno?
A tool that ingests Czech parliamentary legislative proposals, translates them into plain language, and flags the weak signals, amendments, procedural moves, and clauses most readers miss. Live at namireno.cz.