How Kapnova turns a question into a defensible answer
We use AI for what it's good at — reading the world, parsing signals, translating math into narrative. We use real quantitative models for what AI is bad at — the actual decision.
The founding insight
Hedge funds beat the market with three ingredients
Every consumer company has the same three. They just don't have the quant engine. Kapnova does for businesses what quant funds do for stocks.
Point72 manages $35B+ and is one of the world's most sophisticated quantitative hedge funds. Shenbo Xu built models there. Now he's building them for your brand.
Former research scientist at Scale AI
Scale AI trains the world's most capable AI systems. The same data rigor that goes into frontier AI now goes into Kapnova's demand models — so the math is as good as it gets.
Quant funds do this for stocks. Kapnova does this for businesses.
Where AI ends. Where math begins.
AI for the interface. Math for the answer.
Most "AI analytics" tools use a language model to generate the answer itself. That's why they hallucinate. Kapnova uses AI for the parts AI is genuinely good at, and runs your actual decision through deterministic quantitative models — the same kind hedge funds use to allocate billions.
LAYER 01
AI
reads the world
Interface
Natural-language question in. Structured intent out — resolved against a CMO decision ontology we continue to build upon.
Ingestion of signals
Reviews, social, search, earnings, macros, competitive moves — parsed by LLMs, then calibrated to category-specific weights we've learned from thousands of historical outcomes.
Narrative
The math's answer, translated into a recommendation a CMO can defend — every number traceable to a source, a model, and an assumption a CEO can challenge.
LAYER 02
MATH
runs the decision
Quant models, as specialist agents
A coordinated set of quant agents — demand, sentiment, macro, risk — each running the right econometric method, then negotiating a consensus. Not one model. A research desk.
Simulation
Monte Carlo over 10,000+ scenarios with competitive response and macro regime shifts — on dedicated compute, not a shared chat session. Unknown unknowns handled as stochastic variance, not guessed at.
Causal attribution
Every recommendation decomposed by cause — which drivers moved which number, by how much, with what confidence. Causal inference, not correlation.
We use AI for what it's good at. We use math for what AI is bad at.
LLMs' internal confidence circuit misfires and confabulates — they don't know when they don't know.
These are exactly the failure modes that disqualify LLMs from running a $5M decision. Our math doesn't have these problems.
The Kapnova method
A quant team for your question. Not a chatbot's guess.
Every Kapnova simulation runs through a coordinated set of specialist agents — each playing a role a real quant research desk would have. They each do their job, then negotiate a consensus.
01
Data Analyst
Pulls your Shopify, NetSuite, SKU sales, and Nielsen data. Listens to reviews, social, search, and competitor moves in real time.
NLP · Sentiment-to-revenue calibration
1,247 sources per query
02
Data Scientist
Picks the right modeling approach for your question — price elasticity for pricing, MMM for spend, attribution for campaigns.
Elasticity · Hierarchical Bayesian · Granger
3 years of SKU history
03
Risk & Forecast Manager
Plays every campaign forward in 10,000+ variations. Shows you the downside, not just the upside.
Monte Carlo · Scenario analysis
10,000+ scenarios per answer
04
Brand Manager
Synthesizes every specialist's finding. Names the risks, ranks the confidence, gives you something to defend in the meeting.
Causal attribution · Confidence synthesis
One answer your CMO can challenge
The math, specifically
The formula nobody else has.
Most demand models stop at price elasticity. Kapnova's adds a sentiment time series — the term that catches the Reddit backlash, the TikTok velocity, the competitor counter-move that the naive model misses.
Where demand is today, calibrated to your category and brand. Not a category average if your data is connected.
(P/P₀)⁻ᵉ — elasticity term
How sensitive your customers are to price. Calculated from your sales when connected; benchmarked from 300+ comparable brands when not.
S(t) — sentiment term
The signal most models miss. Reddit, TikTok, search, competitor moves — weighted by category-specific learning.
What feeds S(t)
Reddit communities
r/SkincareAddiction, r/MakeupAddiction, r/BeautyDeals — where price sensitivity surfaces first
TikTok trend velocity
Category and brand-specific trend signals — a viral moment changes your decision window
Google Trends
Search interest in your brand, category, and competitors — shows intent before sales
Competitor signals
4 direct competitor benchmarks per simulation — pricing, campaigns, new SKUs
Example: the naive model said +6%, the sentiment-adjusted said −2%
That 8-point gap is S(t) detecting a Reddit thread about competitor price increases that was seeding consumer backlash. Raising price into that environment would have been a costly mistake. The naive model never sees the thread.
Industry-specific. Compounding.
Three phases that sharpen with every decision you make.
Kapnova has industry-calibrated models — beauty, skincare, CPG today, with new verticals on the way. The longer you use it, the sharper the answers get.
01
Simulate
"What happens if we do X?"
Pricing, launch, campaign, spend, compete, or forecast. 60-second answer with confidence bands and sourced reasoning.
→
02
Test
We design the smallest valid experiment.
Hypothesis, sample size, go/kill thresholds. You launch it. Real data replaces guesswork on the next decision.
→
03
Deploy & learn
Roll out. Model sharpens.
Your data trains your dedicated model. Outcomes feed back into the category model. Nothing leaks across tenants.
How teams adopt Kapnova
One person starts. The whole team benefits.
STEP 01
You ask the first question
No signup, no data upload, no IT. Ask your first question free on the homepage. Get a real answer in 60 seconds.
STEP 02
You bring it to a meeting
The answer is sharp enough to share. You paste the snapshot card into Slack or show it in Monday's meeting. Your VP asks: "what is this?"
STEP 03
Your team joins the thread
You invite your VP to the simulation thread. They see the result free, sign up to reply, and try Kapnova on their own decision. Two people on the same domain — team workspace prompt fires.
Three reasons backed by recent frontier research. (1) OpenAI showed in September 2025 that LLM training rewards guessing over uncertainty — models are optimized to sound confident even when they shouldn't be. (2) Google DeepMind has shown LLMs systematically fail on underspecified reasoning, which is exactly the kind of question a CMO is asking. (3) Anthropic has shown that LLMs' internal confidence circuit misfires and confabulates — they don't know when they don't know. None of those failure modes are acceptable when you're allocating $5M.
Three places where AI is genuinely good. (1) The interface — natural-language questions in, structured intent out. (2) Signal ingestion — parsing reviews, social posts, search trends, earnings calls into structured features the math models can use. (3) Narrative — translating the math's answer into a recommendation a CMO can defend. The actual quantitative decision runs on deterministic models, on dedicated compute, with auditable sources.
Different categories have different elasticity profiles, different sentiment sources, different competitive structures. A beauty brand's price elasticity isn't the same as a food brand's. The Reddit communities that matter for skincare aren't the same as the ones that matter for supplements. Kapnova has category-calibrated models — beauty, skincare, CPG today — and adds new ones as we move into new verticals. The math engine is the same. The weights and signal packs are different.
When no first-party data is connected, the demand model uses category-level price elasticity benchmarks calibrated across 300+ comparable consumer brands. The simulation is still quantitative and sourced — it just uses the category average for your elasticity coefficient instead of your specific brand's. Connecting your sales data replaces that average with your actual number.
Each simulation pulls from a live dataset of public sources: Reddit posts and comments, Google Trends data, publicly available competitor pricing signals, industry benchmark databases, and social sentiment signals. The number varies by simulation type and category. All sources are logged — you can see what was pulled and when.
The confidence score reflects the spread of the Monte Carlo distribution — how tightly clustered the 10,000 simulations are around the recommendation. A 91% confidence score means 91% of simulated scenarios pointed to the same recommendation direction. It's a statistical measure of signal clarity, not a claim that the answer will be right 91% of the time.
Price elasticity (e) measures how much demand changes when price changes. An elasticity of 1.0 means a 10% price increase causes a 10% volume decrease — net revenue flat. An elasticity of 1.4 means a 10% price increase causes a 14% volume decrease — net revenue down. Most brands don't know their own elasticity, which is why they use the category average. Kapnova calculates yours from your sales data.
A dashboard shows you what happened. Kapnova tells you what will happen if you make a specific decision. It's a forward-looking simulation engine, not a reporting tool. You're not looking at last month's sales — you're asking "if I raise price 8% on Thursday, what's the likely revenue impact given current sentiment and competitive conditions?"