Decision infrastructure for product teams

Every Choice Should Teach the System.

Subtle Beacon turns product, pricing, packaging, and preference questions into adaptive Bayesian studies: designing the right tradeoffs, fielding them in the world, updating the posterior as responses arrive, and choosing the next question by what it still needs to learn.

Research should not end as a deck of findings. It should become a live decision surface that gets sharper every time customers choose, compare, convert, churn, or hesitate. Subtle Beacon keeps each study as a living posterior, so A/B tests, conjoint, MaxDiff, preference learning, and behavioral signals all update the same evidence base. It is the experimentation layer that helps teams learn what people value, test what changes behavior, and keep fielding the next best question until there is enough confidence to act. Studies design themselves, field themselves, and resolve themselves on posterior evidence, callable by your team, your workflows, or the agents doing the work. It is decades of choice-modeling and experimentation methodology, reimagined as a continuous, self-fielding system that any team or AI agent can invoke.

From one-off studies to learning that compounds.

Subtle Beacon

End-to-end posterior machine. Designs and fields studies across A/B, conjoint, MaxDiff, GP-preference, and behavioral choice. Traffic allocated by Thompson Sampling, next tasks chosen by Bayesian optimal experiment design, and stopping decided on probability-of-superiority and expected regret.

Scope

Headless API, MCP, and GraphQL surfaces for system, workflow, and agent integration.

Availability

Pre-wired into Iridae Decision Infrastructure. Also available as an individually licensable enterprise component.

What Subtle Beacon Makes Possible.

Get answers without becoming a research methodologist. Ask the product, pricing, packaging, or preference question in plain business terms. Subtle Beacon chooses the right evidence path, designs the study, fields it, updates the posterior, and returns the answer with confidence and risk. When a methodologist wants to drive, choosing the family of designs, tuning priors, inspecting allocations, and defending stopping rules, every layer is exposed and traceable. Subtle Beacon automates the rote work; the methodology stays open to the people who care about it.

Know what customers value before the roadmap hardens. See which concepts, bundles, messages, price points, features, and tradeoffs actually move preference or behavior, early enough to change the plan before it becomes expensive to reverse. Whether the question calls for an A/B test, a conjoint, a MaxDiff, a preference study, or behavior learning, every method returns the same shape of answer: a current best estimate with confidence on it, scaled to the same posterior view.

Let the system decide what to learn next. When the answer is not strong enough, Subtle Beacon does not hand you a vague “more research needed.” It identifies the next question, task, audience, or allocation that would most reduce decision-critical uncertainty. When another part of your decision loop (a plan that cannot pick between tactics, a forecast that needs better priors) has a question it cannot answer confidently, it can request the study directly, and the result feeds back the moment it lands.

Move from evidence to action without interpretation theater. Subtle Beacon tells you when the evidence is strong enough to act, when the risk of being wrong is still too high, and whether to ship, stop, continue, segment, or learn more.

Give agents a real path from uncertainty to evidence. When context is not enough, agents do not have to invent confidence. They can trigger Subtle Beacon, monitor the posterior, and return decision-ready answers to the systems planning, forecasting, and executing the work. For AI-product teams building agents, that is the difference between an agent that hallucinates its way to a recommendation and one that runs an actual study with real customers when the answer isn’t already in the model.

Not a research project. A continuous posterior machine.

Your product is already generating evidence: what people click, compare, ignore, buy, abandon, upgrade, downgrade, and come back for. Subtle Beacon turns that evidence into a continuous Bayesian study.

Preference research, live experiments, behavioral signals, and product telemetry all update the same posterior view of what people value and what changes behavior, so research stops being a one-off project and becomes a learning loop running through the product itself.

Other customers’ learning seeds yours without sharing their data: priors arrive as summary statistics, not raw data, and a study unlike anything seen before does not get silently averaged into the rest.

Choice modeling, conjoint, MaxDiff, and preference-learning software as the industry has known them for two decades, reimagined for a continuous, agentic world. Studies that once required a methodologist, a panel vendor, and a six-week timeline now design, field, and stop themselves on posterior evidence.

It replaces work scattered across vendors (research agencies, conjoint engagements, pricing studies, analyst writeups, panel ops, hand-built pipelines) with one continuously-running posterior machine any team, workflow, or AI agent can invoke.

Run the study the decision needs

  • A/B and multi-arm tests
  • Conversion, count, rate, and ordinal metrics
  • Conjoint and adaptive conjoint
  • MaxDiff / best-worst scaling
  • Pairwise preference studies
  • GP-preference studies
  • Behavioral choice learning
  • Pricing, packaging, and bundle studies
  • Message and concept testing
  • Feature tradeoff studies
  • Segment and cohort preference cuts
  • Willingness-to-pay modeling
  • Market and choice-share simulation

Adapt while the evidence arrives

  • Living posterior per study
  • Thompson Sampling allocation
  • Bayesian optimal experiment design
  • Expected-information-gain task selection
  • Adaptive traffic and task allocation
  • Allocation rails to preserve exploration
  • Posterior-driven stopping
  • Expected-regret thresholds
  • Probability-of-superiority rules
  • Credible-interval-width goals
  • Guardrail and non-inferiority rules
  • Peeking mitigation
  • Drift detection
  • Portfolio anomaly monitoring

Return evidence ready to use

  • Winning option and confidence
  • Probability each option is best
  • Expected regret of acting now
  • Credible intervals on outcomes
  • Segment-level preference differences
  • Attribute importances and part-worths
  • WTP ranges
  • Choice-share simulations
  • Sensitivity and scenario results
  • Decision guardrail status
  • Risks and failure modes
  • Next best study

Standalone, built to compose.

Subtle Beacon deploys standalone behind your experimentation, research, and product workflows, or as part of Iridae’s Decision Infrastructure, where Latent Spark belief states inform segment priors, Patient Cartographer requests studies on uncertain tactics, and completed posteriors flow back into Anchored Horizon priors and Latent Spark calibration. Every experiment teaches more than the experiment alone.

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