01
Find what to build
Turn scattered market signals into one visible opportunity gap.
Simulate your market response before launch
novochoice builds synthetic consumer markets so teams can see how product ideas, messages, packaging, listings, and launch plans may behave before real production, inventory, or media spend.
Build a virtual market around one business decision.
01
Define the decision
Turn a launch, product, message, or market-entry question into clear test inputs.
02
Simulate the market
Generate target-buyer groups and observe reactions to scenarios, claims, assets, and tradeoffs.
03
Return the memo
Translate signals into a reviewed decision: build, revise, validate, localize, or stop.
Simulate before decisions get expensive.
novochoice sits between a generic AI brainstorm and a full research project. It helps teams narrow choices before costly fieldwork, production, inventory, or media spend.
01
Traditional research
Best for
Best when the team needs final validation with real consumers.
Limit
Often too slow and expensive for pruning early product, price, pack, and message options.
02
Generic AI prompt
Best for
Useful for brainstorming copy, ideas, and summaries.
Limit
Not enough for an internal launch decision: no designed audience, scenario matrix, calibration notes, or review boundary.
03
novochoice private pilot
Best for
Best before commitment: compare options, surface risks, and turn signals into a reviewed decision memo.
Limit
It is directional evidence, not sales proof. Its job is to make the next real test sharper and cheaper.
Why it changes the workflow
Earlier
before production, inventory, and media spend
Faster
screen many options in days instead of weeks
Broader
test product, price, pack, claim, content, and channel together
Sharper
turn uncertainty into build / revise / validate / stop
See what the simulation returns.
For beta pilots, each frame shows one concrete output a team can receive: input brief, synthetic buyers, ranked options, friction map, buyer reasoning, or memo.
Decision brief
The product turns a business question into test inputs
Launch question
Should novokits launch a refill cleaning kit?
Variables to test
Starter kit, refill pouch, proof claim, usage steps
Output requested
Launch / revise / hold with reasons
Use it anywhere a team needs to judge before it spends.
From opportunity discovery to post-launch diagnosis, the system turns messy options into a focused simulation and a decision memo.
02
Choose between ideas
Compare plausible concepts until the strongest one separates.
03
Shape the offer
Combine features, pack, claim, and bundle into a cleaner offer.
04
Prepare to launch
Check launch readiness before production, inventory, and media spend.
05
Fix weak sales
Cluster weak signals into the real objection holding conversion back.
06
Expand markets
Sequence the next market, channel, or variant before scaling.
Built for consumer-product categories.
Channels such as Amazon, Shopify, TikTok Shop, DTC, and retail sit inside these categories. The simulation adapts to the category, market, channel, and evidence available.
Beauty & personal care
Skincare, haircare, body care, fragrance, beauty devices, and personal-care routines.
View industry page
Food & beverage
Packaged food, snacks, drinks, functional beverages, grocery, and convenience occasions.
View industry page
Health & wellness
Supplements, wellness products, functional nutrition, self-care, and repeat-use offers.
View industry page
Home & household
Home, kitchen, cleaning, organization, refillables, laundry, and daily household goods.
View industry page
Consumer electronics
Accessories, charging, audio, smart-home devices, small electronics, and compatibility-led products.
View industry page
Apparel & lifestyle
Apparel, accessories, travel goods, fitness, outdoor, personal goods, and brand-led products.
View industry page
Baby & family
Baby care, family products, feeding, safety-adjacent goods, and parent-led purchase decisions.
View industry page
Pet care
Pet food, treats, toys, care, accessories, replenishment, and owner-trust decisions.
View industry page
Retail & private label
Retailers, marketplaces, private-label teams, assortment planning, shelf fit, and buyer pitches.
View industry page
Questions teams ask before trusting AI simulation.
Written for buyers comparing synthetic consumer testing with surveys, focus groups, ChatGPT, and traditional market research.
01
What is AI market simulation?
AI market simulation is a structured way to model how target buyers may react to a product, message, pack, offer, channel, or launch plan before a team commits real budget.
02
What are synthetic consumers?
Synthetic consumers are AI-modeled buyer profiles used to simulate reactions from defined audiences. They are built around the decision, category context, target segments, and available market signals.
03
How is synthetic consumer testing different from surveys or focus groups?
Surveys and focus groups collect real human responses. Synthetic consumer testing helps teams narrow options earlier, before they spend the time and money required for real-world research.
04
Does novochoice replace traditional consumer research?
No. novochoice is designed to make the next real test sharper and cheaper. It helps teams decide what to build, revise, validate, or stop before expensive research or launch spend.
05
When should a team use novochoice?
Use it when the team has several product, packaging, claim, price, listing, creative, channel, or launch options and needs to choose before production, inventory, or media spend.
06
What decisions can be simulated?
Common decisions include category opportunity, concept choice, feature priority, packaging, claim credibility, price-pack tradeoffs, listing readiness, launch blockers, weak-sales diagnosis, and market expansion.
+10 more questions
07
Which industries are the best fit?
The best fit is consumer products: beauty and personal care, food and beverage, health and wellness, home and household, consumer electronics, apparel, baby and family, pet care, and retail private label.
08
Can it support Amazon, Shopify, TikTok Shop, or retail teams?
Yes. Those are channels and operating models. The same simulation can test marketplace listings, DTC landing pages, creator angles, retail shelf logic, and buyer-facing pitch materials.
09
What inputs do we need for a pilot?
A focused business question is enough to start. Better inputs include the product brief, target audience, market or channel, competing options, reviews, listings, claims, creative assets, and known constraints.
10
What does a private pilot produce?
The usual output is a reviewed decision memo with ranked options, simulated buyer reasoning, objection maps, assumptions, limits, and a recommended next validation plan.
11
How long does a pilot take?
The workflow is designed to move in days, not weeks. Timing depends on scope, number of options, quality of input materials, and how much human review is needed.
12
How does novochoice reduce hallucination or bias?
We separate inputs, assumptions, scenario design, simulated reactions, and human review. Outputs are labeled as directional evidence, not guaranteed sales prediction.
13
How is novochoice different from asking ChatGPT?
novochoice is not one-off prompting. It scopes the audience, decision options, scenario matrix, evidence format, review boundary, and final readout into a repeatable pilot workflow.
14
Can novochoice predict sales?
It can surface directional demand signals and launch risks. It should not be treated as sales proof unless there is enough real-world data to support a calibrated forecasting study.
15
Is client data used to train models?
Pilot materials are private by default. Customer inputs, concepts, and results are not used for public proof or broader model training without explicit permission.
16
How do we start?
Start with one real decision: what to build, which idea to choose, how to shape the offer, whether launch is ready, why sales are weak, or which market should come next.
AI-simulated respondents are entering serious research, but only with clear guardrails.
Use AI-simulated responses to compare options and expose assumptions, then validate the strongest direction with real-world evidence.
ICML / Microsoft Research
01
Why it matters
A direct academic reference for evaluating whether LLMs can simulate representative human samples.
ICML / Microsoft Research
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Open external source↗
Cambridge / Political Analysis
02
Why it matters
A peer-reviewed paper studying language models as proxies for specific human subpopulations.
Cambridge / Political Analysis
Out of One, Many: Using Language Models to Simulate Human Samples
Open external source↗
MRS Delphi Group
03
Why it matters
An industry report examining synthetic participants from legal, ethical, methodological, and financial angles.
MRS Delphi Group
Using synthetic participants for market research
Open external source↗
NIM Market Decisions
04
Why it matters
A market research article focused on synthetic respondents and when simulated survey insights can be reliable.
NIM Market Decisions
Generative AI in Market Research: Can Machines Simulate Human Insights?
Open external source↗
Private founding beta
Bring one decision. See the pilot map.
In a 30-minute demo call, we map your launch question into inputs, target-buyer groups, scenarios, review boundaries, and a pilot readout.
Pilot map
From first call to private readout.
beta
1
Demo call
Map one launch question
30m
2
Private pilot
One real decision only
1
3
Readout
Decision memo target
72h
4
Public proof
Only with approval
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