Tracking the herd.
Synthetic audience segments, tested against real people across forty sealed, pre-registered cycles. What they can do reliably today, what is still in test, and the record behind every claim.
Ask your audience a question before you field the survey.
Coridor answers a question about a group of people from how real people in that group, holding the same position, have already answered it. On surveys it had never seen, those answers sat closer to the real respondents than the population average. It answers only where the data is strong enough, and says nothing rather than guess. It runs on our own data; your own history is enrichment, never a requirement.
Know your people before you spend on them.
A Coridor is a synthetic audience segment built from a segment’s aggregate data, never from any individual’s record. Forty sealed cycles later, this is what they do. Each line below states its evidence and its limit.
- Reliable
See how your segments differ.
When two real segments disagree on an attitude, Coridors disagree the same way about nine times in ten on policy questions. That is what lets a team ask which audience a message lands with, and trust the answer’s direction.
- Reliable
Read the room, not the average.
Coridors sit closer to their own human segment than to any baseline tried, and closer than a year of work ago: distance fell by three points across the phase and has held for four constructions. Their answers are sharper too, six points more at the ends of the scale, where real people put their strong opinions.
- Reliable
Ask what a sub-segment does.
Take a segment, add one thing its members have told you, and ask what that group does next. The answer layer reads it from the data: on people it had never seen, its answers sat 38% closer to the humans than the population average, and on 277 surveys it had never seen, 9.52 points from the real answers against 11.94, with no trained model in the path. Every answer is a group’s distribution, never a person’s.
- Reliable
Ask in your own words.
A question phrased your way is matched to the question the data actually holds, and refused when there is no match. On 100 rewordings the layer answered 83 at 95.2% precision and refused 19 of 20 questions with no counterpart. It says nothing rather than guess.
- Reliable
Know when to answer.
A confidence score computed from the data decides whether to answer, fall back to the population average, or decline. Answering only where it is strongest, the layer beat the population average on 77.4% of the held-out cells it answered, 14% of them, and on surveys nothing had touched, on 79.5% under a perfect question match. That precision clears its bar narrowly, and the record says so.
Find the niche.
The cells where a minority of a segment holds a strong position are the ones a launch misses. Coridors now find 7 of 20 such cells, up from 4, one short of the bar. The lever that moved it is measured; so is the limit of thin data.
Answer more of a new survey.
Coridors run on our own data spine, built from a partner panel under privacy floors that never expose a person; your data enriches them and is never required. The spine now covers 4,182 questions, and about a third of a new survey’s questions have a counterpart in it. The limit is which questions surveys ask together, so reach is stated as it is.
Bring your own history.
In a test with its rules fixed in advance, we replayed 1,666 real surveys from 243 companies in order, each answered only from what came before it. A company’s own earlier surveys answered 26% of the informative questions in its next one, across 64 companies; the pooled data reached almost none of them. Claims about its confidence score are held for a sealed cycle. This test is not one, and is not in the papers.
A placebo, a seal, and no moving the bar.
Most synthetic-respondent research plays the twin game: one model per person. Ask the same 2,058 people the same question two weeks apart and a person gives the identical answer only about half the time, so a perfect twin is graded against noise. Aggregate those people into a segment and the target becomes stable, 12.7 times more so. Coridor plays the segment game.
Every synthetic segment is scored beside a placebo: the identical construction, fed the real statistics of the wrong segment. Whatever the model brings on its own reaches both. Only segment-specific signal separates them. And every cycle is sealed: the arms, the bars, the seeds, and the call count are written down before any call, a threshold moved after a draw voids the run, and the verdict is published either way.
A cycle passes only if every pre-registered gate holds. Most do not, by design: each cycle raises the bar or changes one thing, and a cycle that clears three gates of four is recorded as a fail. The record below reads by what each cycle bought; the audit strip at the end reads by verdict.
What each cycle bought.
Phase 1 (eleven cycles) proved the claim and found the defect. Phase 2 (ten cycles) built every layer of the architecture and measured what each one is worth. Phase 3 (nineteen cycles) stopped asking a model to carry what the data already knows. Three milestones from the first phase, then the rest in full.
Phase 1 · three milestones
- 3Segments beat their placeboPASS
Conditioned on real answer distributions, synthetic segments sat closer to their own human segment than to a shuffled one, at 2.4 times the noise floor. The claim the whole program rests on.
- 6The full product passesPASS
The same exam through the production runtime. The affect path that had buried the signal was rebuilt, and the margin came back at 1.44 times the bar; the mixture of 90 distinct voices posted 2.26 times.
- 11Segments order the way people doFAIL TO BEAT
Each voice given one real stance from its segment's own distribution: the first direction pass in the program, 73.21% on decided pairs, with the whole interval above a coin flip.
Phase 2 · building the Coridor
- 12Direction closed on attitude itemsFAIL TO BEAT
Turning the invented-memory path off on survey turns took direction to 80.77%, and 90.91% on policy questions. When two real segments disagree on an attitude, Coridors now disagree the same way nine times in ten.
- 13Memory tethered, distance movesFAIL TO BEAT
The memory was rebuilt to imply the voice's own stance and witnessed on every call. Distance to the human panel fell by 1.98 points, the first movement of that number in the program, while direction held.
- 14A leak caught and disclosedFAIL TO BEAT
A beliefs block carried the segment's own answer to the question being asked. The record grades the arm inadmissible, and an exclusion witness now proves on every call that no rendered content names the answer.
- 15The same question, answered honestlyFAIL TO BEAT
With the leak removed, low-resolution public content added nothing: a paired interval including zero. Flat content cannot move a segment. Its control became the sealed baseline every later cycle is scored against.
- 16Decisive voicesFAIL TO BEAT
Each voice given a definite ideology and religion drawn from its segment, coherent with its stance. Distance and sharpness both improved with intervals excluding zero: 0.73 points closer, 2.45 points more answers at the ends of the scale.
- 17A drawn personalityFAIL TO BEAT
Personality drawn per voice from the segment's own trait distribution, with trait-by-state rules live on half the draws. Three of four gates passed at once for the first time; distance 0.95 points closer than the sealed control.
- 18Provenance on every attributeFAIL TO BEAT
One resolver behind every layer, in the order panel, partner, public. For the first time the record could say where each rendered attribute came from: 16% from the panel, 84% from public back-off. That 84% became the target.
- 19The niche movesFAIL TO BEAT
One rendering rule: a voice holding a minority position is told it is a conviction. Niche recall went from 4 to 7 of 20 cells, one short of the bar, with the program's best sharpness and distance. The remaining misses are cells the data is too thin to reach.
- 20First Coridors from Partner dataFAIL TO BEAT
Built from a partner panel's own screener positions under strict privacy floors. The positions were read but echoed rather than reasoned from, and the arms answered alike. The construction was corrected for Cycle 21.
- 21The corrected Partner-data constructionFAIL TO BEAT
Predictors chosen by measured association, personality drawn per voice, memory grown from the segment's own texture. The arm did not beat its shuffle, and a distance gain the record first read as hedging was traced by the next cycle to the baseline's own parse failure. The model reads a rendered position; it does not become the person holding it.
Phase 3 · the answer layer
- 22The position as a lived habitFAIL TO BEAT
The partner-data position rendered as a habit inside the memory, with a structured answer line on every turn. The label echo fell from 678 answers to 3 and the parse collapse that had flattered Cycle 21 was gone. A Coridor holding a position answered 4.87 points closer to the people who hold it, missed their mode, and still did not beat its shuffle.
- 23A substrate tuned on the corpusFAIL TO BEAT
A small model fine-tuned on the partner data's own conditionals passed the conditional gate, the first deciding-gate pass on partner data: it matched the modal answer of the people holding its position 80.11% of the time. It named the minority option almost everywhere, and its shuffle answered the same. The tuning had learned the average, not the difference.
- 24A contrast-built training setFAIL TO BEAT
The same idea on a larger model, trained on examples chosen to contrast one position with another. It matched the people holding its position 77.47% of the time and still answered the same way from a stranger's position, the fifth partner-data cycle in a row to do so. Fine-tuning closes as a route to the conditional.
- 25The answer layer over the spinePASS
Stop asking a model to carry the conditional; read it from the data. On people the layer had never seen, its answers sat 7.98 points from the humans against 12.85 for the population average, 38% closer, with no trained model and no generative call in the path. Questions in a client's own wording mapped to the catalog on 83 of 100 at 95.2% precision. The first cycle whose deciding measure moved on partner data.
- 26Surveys it had never seenPASS
The data spine rebuilt over the whole catalog, then tested on 277 surveys the layer never saw: 9.52 points from the real answers against 11.94 for the population average, with no model in the path.
- 27Knowing when it knowsPASS
A confidence score from the data alone: answering only where a sub-segment departs most from the average, it was right 77.4% of the time on 14% of held-out cells, a pass whose precision awaited confirmation.
- 28The product path, end to endPASS
Questions mapped, answers read, the confidence deciding whether to answer, on surveys nothing had touched. Under a perfect mapping the confidence held at 79.5%. On only 15 answered cells the end-to-end result is underpowered, and it stands by the founder's ruling.
- 29A veto that cost the hub questionsFAIL TO BEAT
A stricter check on question matching answered fewer cells, because it vetoed a few questions that connect to many others. The Cycle 28 path held on a second fresh set: 34 correct answers at 76.5% precision.
- 30Duplicates are not the limitFAIL TO BEAT
Merging near-duplicate questions changed nothing: 61 answerable cells against 62. On this evidence, coverage is limited by which questions surveys ask together.
- 31Profile answers widen reach, not accuracyFAIL TO BEAT
Standing profile answers raised the share of answerable questions from 2.0% to 8.5%, and the answers they added were worse than the population average.
- 32How two positions combineFAIL TO BEAT
A learned rule for combining two co-held positions did not beat treating them as independent. A judgment model's reading of which position should count more did help, on this data.
- 33Layers within a personFAIL TO BEAT
On the public exam, interactions between layers were not detected with single survey items and this sample, which is a failure to detect, not evidence against them.
- 34Where people are noisyFAIL TO BEAT
Whether a person repeats an answer depends far more on the question than on the segment. Stable facts repeat 81% of the time, attitudes 59%. The profile is the input for making synthetic answers vary where real people vary.
- 35Profile answers, fully labelledFAIL TO BEAT
With every profile question labelled and a confidence fitted for them, none qualified to be answered. Two sealed cycles agree on the methods tested.
- 36A client's own history, for small segmentsFAIL TO BEAT
Where a client supplies study history, it picked a small segment's top answer 74% of the time against 43% for the population average, mostly on screener-logic questions. Borrowing strength added nothing measurable.
- 37Raking small segmentsFAIL TO BEAT
Adjusting small segments to their own totals made answers worse overall; an exploratory split found it helped on single-answer questions and failed on select-all questions it could not represent.
- 38Collaborative filteringFAIL TO BEAT
Learning from everyone's answer patterns collapsed toward the population average and never picked a small segment's distinct top answer.
- 39A model per person, from their positionFAIL TO BEAT
Predicting from the position plus demographics was positive on average on single-answer questions, 1.95 points, with an interval that includes zero: not established.
- 40The confirmationFAIL TO BEAT
The Cycle 39 model, frozen and re-run on surveys it had never read, did not repeat its gain: -0.94 points, and pooled with Cycle 39 it is not established. Across five small-segment methods, none improved on the answer layer itself.
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A pass requires every gate at once. The fails carry the movement; the papers report both at the same precision.
Closer, sharper, in the same direction.
The three quantities a segment reading rests on, across the Phase 2 cycles on the public panel. Cycles 12 and 13 compare to a same-draw control; 16 through 19 to one sealed control, so the movement is real movement, not draw noise.
Distance fell from 16.28 to a band between 13.2 and 13.5 and stayed there through four constructions. The gap at the ends of the scale closed by 6.2 points, most of it in two cycles: decisive voices and the commitment rule. Overall direction has read between 74.60% and 80.77% across these cycles against a 75% bar; on the policy questions alone it read between 86% and 91% in the two cycles that measured the split.
The Phase 3 number is a different kind. The public panel is the exam we measure against and report on, never a source a Coridor draws from. The answer layer reads a partner panel’s own data spine: split the respondents in half, read what a sub-segment does from one half, and score it on the other. The paired interval excludes zero and the layer is closer in three quarters of cells.
A Coridor is six layers and a membrane.
Personality, memory, beliefs, goals, relationship, and a synthesis engine, rendered to the model through a translation membrane that proves each layer arrived, over a data spine with an answer layer that reads what the spine knows. Each carries a premise that could fail, an instrument, and a verdict on the sealed record. Unmeasured means unmeasured, never assumed.
- L01Personalityvalidated
Built and drawn per voice from the segment's own trait cells, with trait-by-state rules firing on half the draws. Changes delivery more than stated answers, as designed; its real test is conversation.
- L02Memoryvalidated
Re-integrated. Tethered to the voice's stance and witnessed on every call, the memory moved distance and sharpness by two points each and states the position it implies nineteen times in twenty.
- L03Beliefsvalidated
Decisive, coherent, panel-drawn beliefs move distance and sharpness with intervals excluding zero. Hedged beliefs add nothing, and a belief that names the answer decides it, which is why the exclusion witness exists.
- L04Goals
Built, rendered, and witnessed on every call. No measurable effect on a battery with no intention questions; that test needs a different instrument.
- L05Relationship
Built, drawn per voice, and witnessed. No measurable effect on a survey, where the moderator is a stranger by design; it acts where a conversation is not a survey.
- L06Synthesis
Precedence arbitrated 6,624 real conflicts for the first time. Blend is validated for coherence; the layer's leverage on stated answers waits on larger trait cells.
- Translationvalidated
Every rendered seam now ships with a read-back witness, the exclusion witness holds on every cell, and rendering strength is itself a measured lever. Four unrendered defects were caught before any spend.
- SIIProvenancevalidated
One resolver behind every attribute with a tier and confidence on each, in the order panel, partner, public. A canonical map now merges fifty-five screeners' wordings of the same question, which is what gives the spine its joint tables.
- SIIAnswer layervalidated
Reads what a sub-segment does from the data spine instead of asking a model to carry it. On people and on surveys it had never seen, closer to the real answers than the population average, with a confidence that decides when to answer. Its limit is coverage: which questions surveys ask together.
Four things we will not claim yet.
Finding the minority. A segment’s strong minority positions are the cells a launch decision most needs and the model most under-reads. Seven cycles left the count at 4 of 20; one rendering rule moved it to 7. Nine of the remaining misses are cells where the data is too thin for any voice to land there, which is a data question, not a modelling one.
A model that carries the conditional. Five constructions asked a language model to hold a partner-data position and answer as the people who hold it: prompted, rendered as a habit, fine-tuned twice, and judged. Every one answered the same way from its own position as from a stranger’s. That is why the answer layer reads the conditional from the data instead, and why we do not claim a model can be taught it.
The minority position on partner data. The analogue of the niche gate has not passed on the partner corpus either: the constructions that reached the minority option reached it everywhere, and the ones that did not reached it almost nowhere. Coverage of the spine is the lever the record names next.
Better answers for small segments. Five methods tried to sharpen answers for small segments of 15 to 50 people: borrowing, raking, collaborative filtering and two individual models. None improved on the answer layer itself, and one small gain did not replicate. Against the population average, the answer layer already picks a small segment’s top answer more often; a client’s own history is the strongest lever where a client supplies it.
Nothing on this page is a claim about an individual. The program measures segments, on purpose, and the papers explain why.
Request the full papers.
One paper carries the whole record, the pilot through Cycle 40, and the two phase papers it supersedes remain available as issued. Every number is traced to a sealed document by a ledger a script checks before the PDF builds, and four review passes ship beside the paper, one of them a claims audit in which a judgment model grades every claim against the record line it cites.
Tell us who you are and what you are working on, and we will send them, along with the cycle reports they are built from if you want them.
Want to see the exam run on your own audience? Book a demo, or write to info@coridor.ai.
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