The InvestmentReasoning Engine
Markets don't compile.
Vibe coding works because code has a compiler — the machine tells you, instantly and without mercy, when you're wrong. Markets tell you slowly, noisily, and sometimes lie for years. Finquill is the missing toolchain for conviction: capture the thesis, arm its kill criteria, red-team it before you size it, monitor it while you live your life, and grade the record when reality reports back.
Say it in plain English. The engine structures it into falsifiable claims with deadlines.
A panel of adversarial agents tries to kill it. Four lenses; default verdict: refuted.
Always-on graphs watch your kill criteria against news, filings, earnings, and your own trades.
Claims come due. Reality grades them.
Brier-scored calibration plus process grades: was the decision right given what was knowable?
Thesis structured. 3 falsifiable claims: (1) top-3 hyperscaler capex guidance holds or rises through FY27 — resolve by each earnings; (2) Blackwell lead times stay >6mo through H1; (3) AI-accelerator mix >35% of DC revenue. Kill criteria armed — I'll flag guidance cuts >10% the morning they print.
What our AI builds for you, in seconds.
Ask any ticker, any thesis. The platform composes a custom surface — KPIs, bull/bear cases, comparable history — that you can interrogate, refine, save, or share.
Everything you need.
Nothing you don't.
Fifteen integrated surfaces on one reasoning engine. Built AI-native from the first commit.
Dashboard
What changed overnight for what you hold and watch — a belief-delta digest, not a quote wall.
Watchlist
Every name carries a live thesis. Always-on graphs wake on news, earnings, and your own trades.
Journal
A coach, not a diary — post-event interviews that separate your edge from your tilt.
Research
Cited theses with testable claims. Every number from a tool; every quote traceable to a source.
AIStudio
Ten specialists behind one conversation — a planner fans work out in parallel; one voice synthesizes.
Trading
Preview → confirm on every order. Typed confirmation when it's live and the size is real.
Monitor
The world on one lens, your exposure on the other — quakes, storms, chokepoints, sanctions, touching your book.
Calendar
Earnings, dividends, and macro events — with your exposure and your theses attached.
Explore
Public dossiers, trending research, and the firm's own graded record.
Some questions aren't language problems.
Ask a language model for a Sharpe ratio and it will give you a confident, plausible-sounding number — computed from nothing but the shape of your question. Finquill trains its agents to catch the difference: when a question is actually a statistics problem, it gets handed to a real model, not answered from the language model's memory. Eighteen classical ML and statistics tools are bound directly to the agents — extending what the LLM can do, not standing in for it. The LLM writes the sentence. It never invents the number.
A moving-average crossover, tested on data it never saw. The strategy is picked using only the training window — the test window plays no role in the pick, so the result can't cheat.
A trained model, calibrated against data it wasn't trained on — so a 70% call actually resolves around 70% of the time. Ships with its own reliability curve and Brier score, not just a headline number.
The tools a quant desk reaches for — GARCH volatility forecasts, rolling beta, cointegration tests — run directly by the agent, not approximated by a language model guessing at the math.
Every one of these tools has a floor — ask for a probability on six months of data, and the answer is a refusal, not a guess. Try it in AI Studio, or ask the trading specialist directly.
We run it on ourselves.
In public.
An autonomous research firm runs this same engine on its own coverage — publishing beliefs pre-market with kill criteria attached, admitting changes of mind, and grading the record where anyone can check. Editions are hash-committed.
Integrated research
environment.
Our intelligent RAG pipeline ingests SEC filings, earnings transcripts, news, and market data — then synthesizes it into actionable research you can query in natural language.
Multi-Source Ingestion
Continuously indexes SEC filings, earnings calls, analyst reports, and news into one queryable corpus.
Contextual Retrieval
Semantic search across your entire research corpus — ask a question and get answers grounded in real source documents.
AI Synthesis
Generates investment theses, competitive analyses, and risk assessments with full citation trails back to source material.
NVDA: data-center capex sustainable through FY27
Captured in plain English, then decomposed into claims that can actually be settled. Every figure is read out of a filing or transcript at generation time — a number the tools can't retrieve is refused, never estimated.
Think like Buffett. Or Dalio. Or Munger.
Finquill started as a prompt library — years of engineered frameworks that let you run any question through the minds of the investors you learn from. Switch lenses by tag, or stack multiple frameworks on one query.
Screen the way Berkshire does — moat strength, owner earnings, returns on tangible capital.
Look past the consensus. What does everyone already know, and where is the non-obvious read?
Pressure-test portfolios against growth, inflation, and deflation regimes.
Simple story, growing earnings, boring industry — the ten-bagger pattern, systematized.
Defensive-investor criteria with today's data — quantitative, uncompromising.
Short-setup discipline: where is sentiment wrong and what's the catalyst to close the gap?
Build your own. Codify your investment philosophy as a reusable prompt once, then apply it to every new name you research.
Your context, routed to the right model.
Finquill enriches every query with your watchlist, economic calendar, and media feed — then picks the frontier model whose strengths fit the question. Different questions want different models.
Cross-portfolio implications of today's CPI print — reasoned through each of your names with their individual rate-sensitivity.
Side-by-side comparison of the last four filings for three names you're researching — all in a single context window, nothing summarized away.
Live sentiment pulse on the meme-adjacent names in your watchlist, with retail positioning and social chatter refreshed in real time.
Override the routing any time — pin the model you want, or swap mid-conversation if a query needs something different.
You decide which model touches your work.
Open-source for full data sovereignty. Frontier closed-source for max capability. Diffusion models when you want ten draft theses to compare in seconds. Your call, query by query.
Every major frontier model. Open and closed source.
Pin a specific model for a particular workflow. Default to the strongest available frontier for everything else. Switch mid-conversation if a query needs more horsepower.
Answers at the speed of thought.
We route to optimized inference compute (including Groq for ultra-low-latency models) so iterative research doesn't turn into a coffee break between every question. The faster the loop, the more questions you ask.
Managing client assets? Finquill has a dedicated advisor product — cited research, household-level portfolio reviews, and human-in-the-loop approval built for RIAs.