Pre-Founder Intelligent Decision Platform
Before you spend your first dollar,
first sharpen your judgment.
ZhiQing PreFounder pairs multi-agent workflows with verifiable data sources for people who are seriously considering a startup but haven't incorporated yet — run track comparison, business-model canvas, competitive landscape, and regulatory / scenario stress tests before you commit capital and time.
Multi-Agent Reasoning Mesh
12 Agents · 8 Layers · 9 Models
Quantified Outcomes · Model Output
More than a nice story — every number comes from reproducible Python models.
SEED 20260501 · 9 independent models · 12,000–30,000 Monte Carlo paths · reconciled against Business Plan v3.0 source data.
TAM · Mid scenario
$0M / yr
Deep-analysis annual fee pool · Model 01
ARR · Year-5 median
$0.0M
20K-path Monte Carlo · Model 04
≥ Tier-2 commercial success
0.00%
30K-path success rate · Model 05
User NPV uplift · median
$0.0K
Smart ranking vs random baseline · Model 04
5-year cumulative revenue
$0.0M
Subscription + cash + median equity exit · Model 02+07
5-year cumulative net profit
$0.0M
After-tax basis · Model 07
Equity exit · 5-year median
$0.0M
12K paths · 5% stake · Model 02
NPV @ 35% founder hurdle
$0.0M
Conservative basis · Model 06
Why ZhiQing
Three pillars that redefine pre-founding decisions.
Structured
From track radar to business-model canvas — a consistent assumption-evidence-conclusion chain. Every judgment is traceable, auditable, and reviewable.
Agent collaboration
Agents divide the work: industry research, competitor financial proxies, policy scanning, interview guides and due-diligence checklists — retrieving in parallel and cross-falsifying each other.
Quantifiable
Scenarios and sensitivity: how price, acquisition cost, and regulatory events hit your model — 12,000+ Monte Carlo paths reconstruct the uncertainty.
Product Lines
Three product lines for three depths of decision.
Five-Year Trajectory
Triple revenue: subscription · cash advisory · 5% equity exit.
We don't rely on stories — we rely on a conservative basis: unrealized paper valuation is not booked as revenue; only legally registered equity-exit cash counts toward base-case P&L.
USD M · five-year path · Model 02 + 07
Compliance & defenses
Five lines of defense — write the risk into the contract.
Data traceability chain
Every conclusion traces back to the original evidence fragment (industry reports, policy documents, financials, patents).
Critic Agent counter-falsification
An independent agent argues against the main agent's output, surfacing survivorship bias and collusive errors.
Audit trail
Prompts, model versions, temperature, and retrieved fragments are fully logged — reproducible for investors and regulators.
Content-safety filtering
Benchmarked against China's Interim Measures for Generative AI Services, outputs carry risk tags and explainability caveats.
Equity legal framework
The 5% stake uses standard capital-increase / transfer / advisory equity-swap terms, with price, lock-up, and disclosure itemized.
ZhiQing PreFounder is the most serious pre-founding decision tool I've seen. It doesn't decide for you — it lays out, one by one, the counter-scenarios you'd rather not face.
Lu Jiachen
SaaS founder · Series A, US$80M
Handing 12,000 Monte Carlo paths straight to the investment committee saved us at least 60% of due-diligence time.
Zhang Wei
Partner at a USD fund
Rather than building a pretty deck, they'd rather first tell me 'why this track shouldn't be pursued.' That's why I signed the Deep Program.
Maria L.
Serial medical-device entrepreneur
Take the Decision Seriously
Leave the judgment · to the data.
Sign up to use the Standard Edition right away; after an NDA, we'll co-design your track stress test and business-model canvas.