Building Isolyne (Part 4): Building a Typed LLM Extraction Layer for a Deterministic CQRS Kernel

One of the biggest mistakes in modern AI engineering is building an "LLM wrapper that does everything": It chats with the user. It parses the data. It makes the business decisions. It writes to the database. When an LLM controls your business logic, your app inherits the LLM's flaws: non-determinism, hallucinations, unpredictable schema changes, and high latency. When we designed Isolyne for Shipaton 2026, we established a strict architectural rule: The LLM is a sensory organ, not the brain. We chose Gemini 1.5 Flash for its speed ( { const controller = new AbortController(); const timeoutId = setTimeout( () => controller.abort(), 8000 ); // 8-second circuit breaker try { const res = await fetch(url, { ...options, signal: controller.signal }); clearTimeout(timeoutId); // If rate-limited (429), respect Retry-After or backoff if (res.status === 429 && retries > 0) { const retryAfter = res.headers.get('Retry-After'); const wait = retryAfter ? parseInt(retryAfter, 10) * 1000 : delayMs; await new Promise(r => setTimeout(r, wait)); return fetchWithRetry( url, options, retries - 1, delayMs * 2 ); } return res; } catch (err: any) { clearTimeout(timeoutId); if (retries > 0 && err.name !== 'AbortError') { await new Promise(r => setTimeout(r, delayMs)); return fetchWithRetry( url, options, retries - 1, delayMs * 2 ); } throw err; } } The RevenueCat Connection: High-Value Signals Fueling Pro Features How does clean NLP extraction support our RevenueCat monetization strategy? Every extracted { topic, choice } is paired with its raw verbatim user input and written to our immutable event log. When users upgrade to Isolyne Pro via RevenueCat, they unlock: Verbatim Signal Tracing: Expanding any radar card reveals the exact conversation snippets that produced the decision. Structured Exporting: Downloading team history as clean Markdown Architectural Decision Records (ADRs) ready for GitHub or Jira. By scoping Gemini to clean extraction, we turn messy conversation into high-value, structured data that users gladly pay to preserve. In Part 5... What happens when there is no internet connection at all, or the user's API quota is exhausted? In Part 5, we’ll explore The Graceful Fallback—how we built a local keyword-matching engine that ensures Isolyne remains 100% functional offline.