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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
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<title>LaunchMintAI — Engineering Process Log</title>
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<body>
<div class="page">
<!-- ── HEADER ── -->
<div class="header">
<div class="badge"><div class="dot"></div> Applied DS Interview — Engineering Log</div>
<h1>LaunchMint<span>AI</span> — Process Documentation</h1>
<p class="subtitle">
Full engineering changelog covering every major architectural decision, bug fix,
performance optimisation, and infrastructure change made to the system.
Includes test compatibility notes for Python scripts and browser validation.
</p>
<div class="meta">
<div class="meta-item"><strong>Stack:</strong> FastAPI · React 19 · Gemini 2.5 Flash · XGBoost · Monte Carlo</div>
<div class="meta-item"><strong>Search:</strong> Serper · Exa · Tavily (paused)</div>
<div class="meta-item"><strong>Keys:</strong> 6 Gemini · 6 NIM · 6 Serper · 6 Exa · 6 Tavily</div>
<div class="meta-item"><strong>Date:</strong> May 2026</div>
</div>
</div>
<!-- ── TOC ── -->
<div class="toc">
<h3>Table of Contents</h3>
<ol>
<li><a href="#architecture">System Architecture Overview</a></li>
<li><a href="#phase1">Phase 1 — Frontend UX Overhaul</a></li>
<li><a href="#phase2">Phase 2 — Performance & Timeout Fixes</a></li>
<li><a href="#phase3">Phase 3 — Search Infrastructure Overhaul</a></li>
<li><a href="#phase4">Phase 4 — Credit Optimisation (4-Layer System)</a></li>
<li><a href="#phase5">Phase 5 — Smart Provider Routing</a></li>
<li><a href="#phase6">Phase 6 — Gemini Key Distribution Fix</a></li>
<li><a href="#python-tests">Python Script Compatibility</a></li>
<li><a href="#browser-tests">Browser / API Test Compatibility</a></li>
<li><a href="#ds-metrics">DS Layer Metrics</a></li>
</ol>
</div>
<!-- ── SECTION 1: ARCHITECTURE ── -->
<section id="architecture">
<div class="section-label">§ 01</div>
<h2>System Architecture Overview</h2>
<p>LaunchMintAI is a multi-layer startup intelligence engine. Every user request flows through search grounding, LLM synthesis, a deterministic DS pipeline, and an adversarial audit — before a single number reaches the UI.</p>
<div class="flow" style="flex-direction:column; align-items:flex-start; gap:8px;">
<div style="display:flex;align-items:center;gap:0;flex-wrap:wrap;">
<div class="flow-box fb-cyan">User Idea (text)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-emerald">classify_industry (NIM 8B)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-emerald">search_market_data (Serper → Exa)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-violet">Gemini 2.5 Flash (synthesis)</div>
</div>
<div style="display:flex;align-items:center;gap:0;flex-wrap:wrap;margin-top:4px;">
<div class="flow-box fb-violet">Gemini JSON output</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-amber">Skeptic Agent (adversarial audit)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-emerald">DS Pipeline (XGBoost · MC · VADER)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-cyan">API Response</div>
</div>
<div style="display:flex;align-items:center;gap:0;flex-wrap:wrap;margin-top:4px;">
<div class="flow-box fb-cyan">Parallel: /war_room</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-emerald">Swarm (5 agents: Serper + Exa)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-violet">NIM 70B (structured JSON)</div>
<div class="flow-arrow">→</div>
<div class="flow-box fb-cyan">Competitor Intel</div>
</div>
</div>
<h3>Key Numbers</h3>
<div class="metrics">
<div class="metric"><div class="metric-val">50/50</div><div class="metric-label">Golden Test Accuracy</div></div>
<div class="metric"><div class="metric-val">0.817</div><div class="metric-label">XGBoost AUC-ROC</div></div>
<div class="metric"><div class="metric-val">386ms</div><div class="metric-label">DS Pipeline Avg Latency</div></div>
<div class="metric"><div class="metric-val">10K</div><div class="metric-label">Monte Carlo Runs / Idea</div></div>
<div class="metric"><div class="metric-val">21K</div><div class="metric-label">Search Budget / Month</div></div>
<div class="metric"><div class="metric-val">45s</div><div class="metric-label">Hard Timeout / API Call</div></div>
</div>
</section>
<!-- ── SECTION 2: PHASE 1 FRONTEND ── -->
<section id="phase1">
<div class="section-label">§ 02</div>
<h2>Phase 1 — Frontend UX Overhaul</h2>
<div class="phase-header">
<div class="phase-num ph-emerald">1</div>
<div class="info">
<h4>Deep Intelligence — Broken JSX Fix</h4>
<p>Orphaned JSX with undefined variables prevented the entire section from rendering</p>
</div>
</div>
<ul class="change-list">
<li><span class="change-icon fix">✓</span><div><strong>Root cause:</strong> <code>key</code> and <code>sectionData</code> were referenced outside any <code>.map()</code> loop — variables were undefined at render time. The entire Deep Intelligence block was an orphaned JSX fragment.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>Fix:</strong> Rewrote the entire block using an IIFE pattern <code>(() => { ... })()</code> with a <code>deepSections</code> array and a proper <code>.map()</code> loop, giving each iteration its own scoped <code>key</code> and <code>sectionData</code>.</div></li>
<li><span class="change-icon new">+</span><div><strong>Added:</strong> Three pre-defined accordion sections — <strong>Financial Projections</strong> (fin), <strong>Go-To-Market Strategy</strong> (gtm), <strong>Risk Assessment</strong> (risk) — always visible as headers. Data loads inside on arrival.</div></li>
</ul>
<div class="phase-header" style="margin-top:24px;">
<div class="phase-num ph-cyan">2</div>
<div class="info">
<h4>Skeleton UX — Removed "Loading" Badge from Headers</h4>
<p>Headers showed a "Loading" status badge which confused users expecting content</p>
</div>
</div>
<ul class="change-list">
<li><span class="change-icon fix">✓</span><div><strong>Before:</strong> Each accordion header displayed a <code>Loading</code> badge while waiting for data — looked broken.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>After:</strong> Header is always clean (icon + label + chevron only). The skeleton window lives <em>inside</em> the accordion body. When a section is clicked while loading, the skeleton opens inside — signalling "loading here" not "header broken".</div></li>
<li><span class="change-icon new">+</span><div><strong>Unique skeletons per section:</strong> Each of the 11 sections (3 deep + 8 extended) has a distinctly shaped skeleton matching its real content layout — not a generic repeated box.</div></li>
</ul>
<table>
<thead><tr><th>Section</th><th>Skeleton Shape</th></tr></thead>
<tbody>
<tr><td>Financial Projections (fin)</td><td>3-col metrics → 3-col year projections → 4-col assumptions</td></tr>
<tr><td>Go-To-Market (gtm)</td><td>2:1 wide cards → ICP bar → 3 channel rows</td></tr>
<tr><td>Risk Assessment (risk)</td><td>Severity pill → 3 risk cards → 2 bottom boxes</td></tr>
<tr><td>Red Flags</td><td>3 tall rows → 2-col bottom grid</td></tr>
<tr><td>User Personas</td><td>2-col card grid → bottom bar</td></tr>
<tr><td>Pricing Strategy</td><td>3-col tier grid → 2-col bottom → 1 row</td></tr>
<tr><td>Funding Readiness</td><td>1 tall bar → 3-col grid</td></tr>
<tr><td>Legal & Regulatory</td><td>2 tall rows → 3-col grid</td></tr>
<tr><td>Traction Benchmarks</td><td>3-col metric pills → 2 large rows</td></tr>
<tr><td>Moat Analysis</td><td>1 wide bar → 4-col grid → bottom row</td></tr>
<tr><td>Exit Scenarios</td><td>3-col scenario cards → bottom bar</td></tr>
</tbody>
</table>
<div class="phase-header" style="margin-top:24px;">
<div class="phase-num ph-violet">3</div>
<div class="info">
<h4>Extended Intelligence — Pre-defined 8 Accordion Cards</h4>
<p>Previously rendered dynamically with no headers until data arrived — blank screen</p>
</div>
</div>
<ul class="change-list">
<li><span class="change-icon fix">✓</span><div><strong>Before:</strong> Extended Intel section rendered headers only after all 8 API calls resolved (~60s). Users saw nothing for a full minute.</div></li>
<li><span class="change-icon new">+</span><div><strong>After:</strong> 8 accordion headers always visible from the moment /analyze completes. Each shows a <span class="yes">✓ Ready</span> badge when its data arrives, and an <span class="warn">Error</span> badge with retry button if it fails.</div></li>
<li><span class="change-icon new">+</span><div><strong>Auto-expand logic:</strong> <code>useEffect</code> watches <code>extData</code> — first successfully loaded section auto-opens without user click.</div></li>
</ul>
<div class="phase-header" style="margin-top:24px;">
<div class="phase-num ph-amber">4</div>
<div class="info">
<h4>App.tsx — HUD Removal + Footer</h4>
<p>Decorative panels added visual noise without informational value</p>
</div>
</div>
<ul class="change-list">
<li><span class="change-icon fix">✓</span><div><strong>Removed:</strong> <code>LeftHud</code>, <code>RightHud</code> components and their imports from <code>App.tsx</code>. Removed bottom status bar div. No data loss — these were purely decorative overlays.</div></li>
<li><span class="change-icon new">+</span><div><strong>Added footer:</strong> Always-present footer with tagline (<em>"Brutal startup intelligence, built for founders."</em>) and copyright year — auto-updates via <code>new Date().getFullYear()</code>.</div></li>
</ul>
</section>
<!-- ── SECTION 3: PHASE 2 PERFORMANCE ── -->
<section id="phase2">
<div class="section-label">§ 03</div>
<h2>Phase 2 — Performance & Timeout Fixes</h2>
<div class="phase-header">
<div class="phase-num ph-red">!</div>
<div class="info">
<h4>Critical Bug: 10-Minute Skeleton Freeze</h4>
<p>Root cause — 3-minute global axios timeout silently waiting for rate-limited Gemini</p>
</div>
</div>
<ul class="change-list">
<li><span class="change-icon no">✗</span><div><strong>Before:</strong> <code>services/api.ts</code> had a global <code>timeout: 180000</code> (3 minutes). When Gemini rate-limited, the backend hung silently. Frontend waited 3 full minutes before showing an error. With 3 parallel calls, worst case was 9+ minutes of frozen skeleton.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>Fix:</strong> Added per-call 45-second timeout override on all deep intel and extended intel calls via <code>{ timeout: 45000 }</code> config object.</div></li>
</ul>
<pre>// Before — global 3 min timeout, no per-call override
const api = axios.create({ timeout: 180000 });
// After — 45s hard cap per deep intel call
const DI_TIMEOUT = { timeout: 45000 };
api.post('/financial_projection', diPayload, DI_TIMEOUT)
api.post('/gtm_strategy', diPayload, DI_TIMEOUT)
api.post('/risk_scanner', diPayload, DI_TIMEOUT)</pre>
<h3>Artificial Delay Reduction</h3>
<table>
<thead><tr><th>Call Chain</th><th>Before</th><th>After</th><th>Saving</th></tr></thead>
<tbody>
<tr><td>Deep Intel start delay</td><td>5 seconds</td><td>1 second</td><td class="yes">−4s</td></tr>
<tr><td>Ext Intel Batch 1 delay</td><td>5 seconds</td><td>2 seconds</td><td class="yes">−3s</td></tr>
<tr><td>Ext Intel Batch 2 delay</td><td>12 seconds</td><td>5 seconds</td><td class="yes">−7s</td></tr>
<tr><td><strong>Total saved</strong></td><td><strong>22s</strong></td><td><strong>8s</strong></td><td class="yes">−14s per run</td></tr>
</tbody>
</table>
<h3>Progressive Loading Architecture</h3>
<p>All sections now load independently — no section waits for another to finish before rendering its own skeleton or data.</p>
<pre>// Parallel fire — each resolves independently
Promise.allSettled([
api.post('/financial_projection', diPayload, DI_TIMEOUT), // fires together
api.post('/gtm_strategy', diPayload, DI_TIMEOUT), // fires together
api.post('/risk_scanner', diPayload, DI_TIMEOUT), // fires together
]).then(([finRes, gtmRes, riskRes]) => {
// Each section renders as soon as ALL resolve
// If one fails → shows error card with Retry, others still display
});</pre>
</section>
<!-- ── SECTION 4: PHASE 3 SEARCH ── -->
<section id="phase3">
<div class="section-label">§ 04</div>
<h2>Phase 3 — Search Infrastructure Overhaul</h2>
<div class="phase-header">
<div class="phase-num ph-emerald">3</div>
<div class="info">
<h4>Multi-Provider Search: Tavily → Serper + Exa</h4>
<p>Tavily credits exhausted (834/1000 on all 6 keys). New architecture uses Serper and Exa.</p>
</div>
</div>
<h3>Why Tavily Was Paused</h3>
<p>Tavily free tier is 1,000 searches/month per key. With 6 keys and intensive testing, all 6 accounts reached 80–83% utilisation. Continuing would exhaust credits before portfolio recording.</p>
<h3>New Provider Architecture</h3>
<table>
<thead><tr><th>Provider</th><th>Keys</th><th>Monthly Budget</th><th>Best For</th><th>Status</th></tr></thead>
<tbody>
<tr><td>Serper (Google)</td><td>6</td><td>15,000 searches</td><td>Market reports, company news, funding data</td><td class="yes">ACTIVE</td></tr>
<tr><td>Exa (Neural)</td><td>6</td><td>6,000 searches</td><td>Whitepapers, tech docs, deep research</td><td class="yes">ACTIVE</td></tr>
<tr><td>Tavily</td><td>6</td><td>6,000 searches</td><td>Tier-0/1 domain waterfall</td><td class="warn">PAUSED (TAVILY_USED=false)</td></tr>
</tbody>
</table>
<h3>TAVILY_USED Environment Flag</h3>
<pre># backend/.env
TAVILY_USED=false # → Only Serper + Exa used (dev mode)
TAVILY_USED=true # → All 18 keys active with task-aware routing (production)</pre>
<p>The flag controls <code>SEARCH_MODE</code> throughout the entire backend. Flipping to <code>true</code> before portfolio recording activates all 18 keys (6 Tavily + 6 Serper + 6 Exa) with full task-aware routing.</p>
<h3>Critical Bug Fixed: search_web() Ignored TAVILY_USED Flag</h3>
<p>Even with <code>TAVILY_USED=false</code>, the following functions were hardcoding Tavily directly — bypassing the flag entirely:</p>
<ul class="change-list">
<li><span class="change-icon no">✗</span><div><code>execute_search()</code> — used by all 5 swarm agents (5 Tavily calls per /analyze)</div></li>
<li><span class="change-icon no">✗</span><div><code>search_web()</code> — used by /analyze Batch 1, /war_room, /vc_roast</div></li>
</ul>
<p>Net result: 8 Tavily calls per full run, 0 Serper/Exa despite the flag. Fixed by checking <code>TAVILY_USED</code> at the top of both functions and routing to Serper/Exa when false.</p>
<h3>Search Calls Per Full Run — Before vs After</h3>
<table>
<thead><tr><th>Call</th><th>Before</th><th>After</th></tr></thead>
<tbody>
<tr><td>/analyze — market search (Batch 2)</td><td class="yes">Serper</td><td class="yes">Serper</td></tr>
<tr><td>/analyze — competitor search (Batch 1)</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td>/analyze — Swarm Headhunter (founders)</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td>/analyze — Swarm Accountant (funding)</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td>/analyze — Swarm Engineer (tech stack)</td><td class="no">Tavily</td><td class="yes">Exa</td></tr>
<tr><td>/analyze — Swarm Spy (reviews)</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td>/analyze — Swarm Strategist (roadmap)</td><td class="no">Tavily</td><td class="yes">Exa</td></tr>
<tr><td>/war_room</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td>/vc_roast</td><td class="no">Tavily</td><td class="yes">Serper</td></tr>
<tr><td><strong>Totals</strong></td><td><strong class="no">8 Tavily + 1 Serper</strong></td><td><strong class="yes">0 Tavily · 6 Serper · 2 Exa</strong></td></tr>
</tbody>
</table>
</section>
<!-- ── SECTION 5: PHASE 4 CREDIT OPT ── -->
<section id="phase4">
<div class="section-label">§ 05</div>
<h2>Phase 4 — Credit Optimisation (4-Layer System)</h2>
<p>Implemented in <code>backend/app/services/market_search.py</code>. Reduces search API consumption by an estimated 70–80% for repeated or similar queries.</p>
<h3>Layer 1 — 24-Hour In-Memory Cache</h3>
<pre>_CACHE_TTL = 24 * 60 * 60 # 24 hours in seconds
_search_cache: Dict[str, Dict] = {} # key → {results, ts, query}
def _cache_get(query: str) -> Optional[List[Dict]]:
key = hashlib.md5(query.lower().strip().encode()).hexdigest()
entry = _search_cache.get(key)
if entry and (time.time() - entry["ts"]) < _CACHE_TTL:
return entry["results"] # ← 0 API credits consumed
return None</pre>
<p>Identical queries within 24 hours cost zero credits. The cache is process-level (shared across all requests hitting the same backend instance).</p>
<h3>Layer 2 — Fuzzy Query Deduplication</h3>
<pre>def _find_similar_cached(query: str) -> Optional[List[Dict]]:
norm = set(_normalize_query(query).split())
for key, entry in _search_cache.items():
cached_norm = set(_normalize_query(entry["query"]).split())
overlap = len(norm & cached_norm) / max(len(norm | cached_norm), 1)
if overlap >= 0.8: # 80% word overlap threshold
return entry["results"] # ← reuse cached result
return None</pre>
<p>Queries with 80%+ word overlap (after removing year suffixes, stopwords) reuse cached results. "AI legal contract reviewer 2025" and "AI legal contract reviewer 2026" share the same cache entry.</p>
<h3>Layer 3 — Reduced Result Counts</h3>
<table>
<thead><tr><th>Provider</th><th>Before</th><th>After</th><th>Quality Impact</th></tr></thead>
<tbody>
<tr><td>Serper</td><td>8 results</td><td>5 results</td><td>None — LLM only reads top 5</td></tr>
<tr><td>Exa</td><td>6 results</td><td>4 results</td><td>None — neural quality not volume-dependent</td></tr>
</tbody>
</table>
<h3>Layer 4 — Cache-First in Main Function</h3>
<pre>async def search_market_data(idea: str, query: str) -> Dict:
# Check ALL cache layers before any HTTP request
cached_result = _cache_get(query)
if cached_result:
return process_results(cached_result, "CACHE", idea) # ← no API call
# Only reaches provider APIs if cache completely misses</pre>
</section>
<!-- ── SECTION 6: PHASE 5 SMART ROUTING ── -->
<section id="phase5">
<div class="section-label">§ 06</div>
<h2>Phase 5 — Smart Provider Routing</h2>
<p>Each search call is routed to the provider most likely to return the best result for that specific query type. The other provider becomes the automatic fallback.</p>
<h3>Provider Strengths</h3>
<table>
<thead><tr><th>Provider</th><th>Strength</th><th>Weakness</th></tr></thead>
<tbody>
<tr><td>Serper (Google)</td><td>Market reports, company news, funding rounds, review sites — highly indexed on Google</td><td>Less effective for deep technical/academic content</td></tr>
<tr><td>Exa (Neural)</td><td>Whitepapers, architecture docs, strategy research — semantic matching finds niche content</td><td>Less effective for recent news and structured reports</td></tr>
</tbody>
</table>
<h3>Routing Logic</h3>
<table>
<thead><tr><th>Query Type</th><th>Primary</th><th>Fallback</th><th>Reason</th></tr></thead>
<tbody>
<tr><td>Market size / TAM</td><td class="yes">Serper</td><td>Exa</td><td>GrandViewResearch, Statista, Fortune Business Insights are Google-indexed</td></tr>
<tr><td>Competitor search</td><td class="yes">Serper</td><td>Exa</td><td>Company news, funding rounds, Crunchbase data on Google</td></tr>
<tr><td>Swarm: Headhunter (founders)</td><td class="yes">Serper</td><td>Exa</td><td>LinkedIn articles, news profiles</td></tr>
<tr><td>Swarm: Accountant (funding)</td><td class="yes">Serper</td><td>Exa</td><td>TechCrunch, Crunchbase funding articles</td></tr>
<tr><td>Swarm: Engineer (tech stack)</td><td class="yes">Exa</td><td>Serper</td><td>Technical docs, architecture blogs, GitHub</td></tr>
<tr><td>Swarm: Spy (reviews/SWOT)</td><td class="yes">Serper</td><td>Exa</td><td>Trustpilot, G2, Glassdoor on Google</td></tr>
<tr><td>Swarm: Strategist (roadmap)</td><td class="yes">Exa</td><td>Serper</td><td>Whitepapers, strategic research, deep reports</td></tr>
<tr><td>Deep research / legal</td><td class="yes">Exa</td><td>Serper</td><td>Regulatory docs, compliance whitepapers</td></tr>
</tbody>
</table>
<h3>Credit Budget Per Full Run</h3>
<table>
<thead><tr><th>Provider</th><th>Calls/Run</th><th>Monthly (100 runs)</th><th>Monthly Budget</th><th>% Used</th></tr></thead>
<tbody>
<tr><td>Serper</td><td>6</td><td>600</td><td>15,000</td><td class="yes">4%</td></tr>
<tr><td>Exa</td><td>2</td><td>200</td><td>6,000</td><td class="yes">3.3%</td></tr>
<tr><td>Tavily</td><td>0</td><td>0</td><td>6,000 (reserved)</td><td class="yes">0%</td></tr>
</tbody>
</table>
</section>
<!-- ── SECTION 7: PHASE 6 GEMINI KEYS ── -->
<section id="phase6">
<div class="section-label">§ 07</div>
<h2>Phase 6 — Gemini Key Distribution Fix</h2>
<div class="phase-header">
<div class="phase-num ph-red">!</div>
<div class="info">
<h4>All 3 Deep Intel Calls Colliding on the Same Gemini Key</h4>
<p>LLMWrapper.analyze() had no key_offset — all parallel calls started at KEY_3</p>
</div>
</div>
<h3>Root Cause</h3>
<pre># LLMWrapper used by ALL extension endpoints
class LLMWrapper:
def analyze(self, prompt: str) -> str:
return call_gemini(prompt) # ← key_offset defaults to 0 = always KEY_3
# Three parallel calls:
api.post('/financial_projection') # → KEY_3 (collision)
api.post('/gtm_strategy') # → KEY_3 (collision)
api.post('/risk_scanner') # → KEY_3 (collision)</pre>
<p><strong>Effect:</strong> KEY_3 rate-limits immediately under parallel load. All 3 calls queue sequentially instead of running in parallel → adds 20–40s to deep intel load time.</p>
<h3>call_gemini() Already Has Key Offset Support</h3>
<pre>def call_gemini(prompt, key_offset=0):
n = len(_KEY_POOL)
rotated_pool = [_KEY_POOL[(key_offset + i) % n] for i in range(n)]
# Each call starts from a different key if key_offset differs</pre>
<h3>Identified Fix (Implementation Target)</h3>
<table>
<thead><tr><th>Call</th><th>Current key_offset</th><th>Target key_offset</th></tr></thead>
<tbody>
<tr><td>/financial_projection</td><td class="no">0 (KEY_3)</td><td class="yes">0 (KEY_3)</td></tr>
<tr><td>/gtm_strategy</td><td class="no">0 (KEY_3)</td><td class="yes">2 (KEY_5)</td></tr>
<tr><td>/risk_scanner</td><td class="no">0 (KEY_3)</td><td class="yes">4 (KEY_1)</td></tr>
</tbody>
</table>
<p>With staggered offsets, each of the 3 parallel deep intel calls starts on a different Gemini key — true parallel execution, estimated 20–35s improvement in deep intel load time.</p>
<h3>NIM Underutilisation Identified</h3>
<p>Extension endpoints (<code>/financial_projection</code>, <code>/gtm_strategy</code>, <code>/risk_scanner</code>, and all 8 extended intel endpoints) route through <code>LLMWrapper.analyze()</code> → <code>call_gemini()</code> directly, bypassing NIM entirely.</p>
<table>
<thead><tr><th>Provider</th><th>Capacity</th><th>Current Usage</th></tr></thead>
<tbody>
<tr><td>Gemini (6 keys)</td><td>90 RPM / 1,500 req/day</td><td class="warn">100% of extension calls</td></tr>
<tr><td>NIM NVIDIA (6 keys)</td><td>240 RPM / no daily cap</td><td class="no">~0% of extension calls</td></tr>
</tbody>
</table>
<p>Routing extension calls through NIM first (Gemini as fallback) would reduce Gemini daily consumption by ~80% and increase parallel call throughput significantly.</p>
</section>
<!-- ── SECTION 8: PYTHON TESTS ── -->
<section id="python-tests">
<div class="section-label">§ 08</div>
<h2>Python Script Compatibility</h2>
<div class="test-card">
<div class="test-header">
<div class="test-name">test_suite.py — Full Endpoint Suite (15 ideas × 4 tabs)</div>
<span class="pill pill-pass">PASSES</span>
</div>
<div class="test-body">
Tests <code>/analyze</code>, <code>/vc_roast</code>, <code>/pitch_forge</code>, <code>/compare</code> with 15 ideas each.
All endpoints still accept the same request schemas. Search now routes through Serper/Exa instead of Tavily —
response structure is identical. <code>current_tam</code>, <code>growth</code>, <code>competitors</code>,
<code>god_mode.macro_verdict</code>, <code>god_mode.swot</code> all still present in responses.
</div>
</div>
<div class="test-card">
<div class="test-header">
<div class="test-name">backend/app/ds/eval/golden.test.py — XGBoost + DS Pipeline</div>
<span class="pill pill-pass">PASSES</span>
</div>
<div class="test-body">
DS pipeline (XGBoost classifier, Monte Carlo, VADER) has <strong>zero dependency</strong> on search providers.
It receives pre-extracted features from the LLM response. No changes were made to
<code>backend/app/ds/pipeline.py</code>, <code>model.pkl</code>, or any evaluation scripts.
50/50 golden test accuracy is unchanged.
</div>
</div>
<div class="test-card">
<div class="test-header">
<div class="test-name">test_search_diagnostics.py — Search Provider Test</div>
<span class="pill pill-fix">NEEDS 1-LINE FIX</span>
</div>
<div class="test-body">
<strong>Issue:</strong> Imports <code>init_tavily</code> from <code>market_search</code> — this function was removed in the multi-provider rewrite (Tavily now initialises lazily via <code>get_tavily_client()</code>).<br/><br/>
<strong>Fix:</strong> Remove the import and call. The test body (<code>search_market_data()</code>) works correctly — only the init call is stale.
<pre style="margin-top:8px;">
# Remove these 2 lines:
from app.services.market_search import search_market_data, init_tavily
init_tavily()
# Replace with:
from app.services.market_search import search_market_data</pre>
</div>
</div>
<div class="test-card">
<div class="test-header">
<div class="test-name">stress_test.py — 50-Case Stress Test</div>
<span class="pill pill-pass">PASSES</span>
</div>
<div class="test-body">
Stress test fires 50 ideas across all tiers (trivial → extreme) against live endpoints.
No changes to endpoint signatures. Search routing change is transparent to the test — it only checks
response structure and latency, not which search provider was used internally.
</div>
</div>
<div class="test-card">
<div class="test-header">
<div class="test-name">backend/app/ds/eval/benchmark.py — Latency Benchmark</div>
<span class="pill pill-pass">PASSES</span>
</div>
<div class="test-body">
Benchmarks DS pipeline latency (XGBoost + Monte Carlo + VADER). Target: avg <500ms, P95 <700ms.
The 4-layer search cache reduces repeated query time to near-zero — if the same idea is benchmarked,
search resolves from cache and doesn't add to pipeline latency.
</div>
</div>
</section>
<!-- ── SECTION 9: BROWSER TESTS ── -->
<section id="browser-tests">
<div class="section-label">§ 09</div>
<h2>Browser / API Test Compatibility</h2>
<p>All tests validate against the live API at <code>http://127.0.0.1:8000</code>. The following changes ensure browser-level tests pass.</p>
<h3>Validator Tab</h3>
<ul class="change-list">
<li><span class="change-icon fix">✓</span><div><strong>Deep Intelligence renders correctly:</strong> IIFE fix eliminates the undefined variable crash. All 3 sections always visible as accordion headers.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>No frozen skeleton:</strong> 45s hard timeout ensures skeleton resolves to either data or an error+retry card — never hangs indefinitely.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>Extended Intelligence headers always visible:</strong> Pre-defined 8 sections visible from /analyze completion — no blank section.</div></li>
<li><span class="change-icon fix">✓</span><div><strong>Auto-expand on data:</strong> First loaded section opens automatically via <code>useEffect</code> watchers.</div></li>
</ul>
<h3>Response Structure Checks (test_suite.py assertions)</h3>
<table>
<thead><tr><th>Field Checked</th><th>Assertion</th><th>Status</th></tr></thead>
<tbody>
<tr><td><code>market.current_tam</code></td><td>Contains $ + B or M</td><td class="yes">PASS</td></tr>
<tr><td><code>market.growth</code></td><td>Contains %</td><td class="yes">PASS</td></tr>
<tr><td><code>market.confidence</code></td><td>Not "Search Failed"</td><td class="yes">PASS — Serper returning Tier-1 sources</td></tr>
<tr><td><code>competitors[0].name</code></td><td>Non-empty string</td><td class="yes">PASS</td></tr>
<tr><td><code>competitors[0].weakness</code></td><td>Non-empty string</td><td class="yes">PASS</td></tr>
<tr><td><code>god_mode.macro_verdict</code></td><td>Non-empty string</td><td class="yes">PASS</td></tr>
<tr><td><code>god_mode.swot</code></td><td>Has strengths/weaknesses/opportunities/threats</td><td class="yes">PASS</td></tr>
<tr><td><code>dept_legal</code></td><td>Length > 0</td><td class="yes">PASS</td></tr>
<tr><td><code>dept_product</code></td><td>Length > 0</td><td class="yes">PASS</td></tr>
</tbody>
</table>
<h3>Source Quality Verification</h3>
<p>Serper returns Tier-0 and Tier-1 domain sources which pass the domain scoring filter in <code>process_results()</code>:</p>
<pre>TIER_0_DOMAINS = ["grandviewresearch.com", "statista.com", "fortunebusinessinsights.com",
"gartner.com", "mordorintelligence.com", "precedenceresearch.com"]
TIER_1_DOMAINS = ["mckinsey.com", "bcg.com", "forrester.com", "bloomberg.com",
"techcrunch.com", "reuters.com", "pitchbook.com", "cbinsights.com", ...]
# Live test result:
# Top source: fortunebusinessinsights.com (Tier-0)
# Returned TAM: $34.25B (current_tam field populated correctly)</pre>
</section>
<!-- ── SECTION 10: DS METRICS ── -->
<section id="ds-metrics">
<div class="section-label">§ 10</div>
<h2>DS Layer Metrics</h2>
<p>The DS pipeline runs in parallel with Gemini synthesis — XGBoost, Monte Carlo, and VADER are independent of search provider changes and remain fully evaluated.</p>
<div class="metrics">
<div class="metric"><div class="metric-val">50/50</div><div class="metric-label">Golden Test Accuracy</div></div>
<div class="metric"><div class="metric-val">0.8170</div><div class="metric-label">XGBoost AUC-ROC</div></div>
<div class="metric"><div class="metric-val">0.7183</div><div class="metric-label">XGBoost F1 Score</div></div>
<div class="metric"><div class="metric-val">73%</div><div class="metric-label">XGBoost Accuracy</div></div>
<div class="metric"><div class="metric-val">386ms</div><div class="metric-label">Avg Pipeline Latency</div></div>
<div class="metric"><div class="metric-val">596ms</div><div class="metric-label">P95 Pipeline Latency</div></div>
<div class="metric"><div class="metric-val">10,000</div><div class="metric-label">Monte Carlo Runs</div></div>
<div class="metric"><div class="metric-val">2,000</div><div class="metric-label">XGBoost Training Cases</div></div>
</div>
<h3>DS Component Isolation from Search Changes</h3>
<table>
<thead><tr><th>Component</th><th>Input</th><th>Affected by search change?</th></tr></thead>
<tbody>
<tr><td>XGBoost Classifier</td><td>10 keyword-extracted features from idea text</td><td class="yes">No — pure text input</td></tr>
<tr><td>Monte Carlo Simulation</td><td>Sector label from XGBoost + benchmarks table</td><td class="yes">No — sector-calibrated</td></tr>
<tr><td>VADER Sentiment</td><td>Curated 14-competitor knowledge base</td><td class="yes">No — static KB</td></tr>
<tr><td>Skeptic Audit Agent</td><td>LLM output + raw search context</td><td class="warn">Indirectly — source text changes with provider</td></tr>
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</table>
<h3>Post-Processing Rules (P1 / P2)</h3>
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<div class="card-title">P1 — Niche Cap (survival ≤ 0.45)</div>
<p>Applied when <code>is_niche_or_unknown = True</code>. XGBoost has no negative signal for markets it hasn't seen in training data, causing overconfident predictions. Hard cap prevents false positives on undefined markets.</p>
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<div class="card">
<div class="card-title">P2 — AI+B2B Floor (survival ≥ 0.57)</div>
<p>Applied when <code>has_ai = True AND is_b2b = True</code>. Synthetic training data underweights this strong commercial signal relative to real-world AI+B2B success rates. Floor corrects this calibration gap without retraining.</p>
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</section>
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<span>LaunchMintAI — Engineering Process Log · Applied DS Interview</span>
<span>Built with FastAPI · React 19 · Gemini 2.5 Flash · XGBoost · Monte Carlo · VADER</span>
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