# Enlightn

> Enlightn is a sample provider that only recontacts panelists it already knows - profiled, quality-checked, and matched to your study. Never anonymous traffic. Buyers hold the proof at both ends of fieldwork: the quote before (who would be recontacted and why, free in 24 hours) and the fieldwork analysis after (who answered and why they qualified). Founder-led from Montreal.

_Last updated: 2026-08-06. Metrics are averages across projects run to date. Per-project results are shared openly with clients._

> For a single-file export of everything below, see [/llms-full.txt](https://enlightn.io/llms-full.txt).

## Core pages

- [For research teams (buyers)](https://enlightn.io/): value proposition, the two proof artifacts, measured quality metrics, buyer FAQ
- [For supplier partners (panels)](https://enlightn.io/partners): partnership terms, economics, partner FAQ
- [Blog](https://enlightn.io/blog): field notes on sample quality, survey fraud, respondent pay, and match-first recruitment
- [Panel data quality: what 300 panelists say about disqualification and pay](https://enlightn.io/blog/panel-data-quality-300-panelists): original Enlightn survey of 300 active panelists (February 2026) on disqualification, fair pay, and survivorship bias in panel samples
- [Sample feasibility: what you should see before you commit to fieldwork](https://enlightn.io/blog/sample-feasibility-quote): release note for the quote (August 2026) - what a verifiable feasibility answer contains: a confident floor per target with the math shown, per-panelist evidence (Proven / Likely / Unknown), source provenance, and disclosed top-up pricing when the vetted pool is short

## What Enlightn does

Most sample still works the same way: push anonymous traffic into a screener and filter after. The result is predictable - 60-70% termination rates, respondents who have learned to game screeners, fraud that stays profitable, and buyers who can't verify what they paid for.

Enlightn flips the order. We profile panelists with open-ended, AI-assisted questions, quality-check them, translate the buyer's brief into a structured targeting spec, rank the pool for fit, and recontact only the panelists matched to the study - people we already know, from suppliers we've tested, recontacted through those suppliers' permissioned systems. Never anonymous traffic.

The buyer holds the proof at two moments. Before fieldwork: **the quote** - an interactive, password-gated page delivered within 24 hours of receiving a brief, showing who we'd recontact, the evidence behind each match, a confident floor per target (matched x recontact rate x incidence, with the math shown), and transparent pricing. Free on a live brief; billing is per delivered complete. After fieldwork: **the fieldwork analysis** - incidence, disqualification rate, completion, drop-off, and who qualified and why, per source. Same suppliers. A smarter order of operations - and the buyer can verify it.

When the vetted pool can't cover the full ask, the quote says so and offers a choice: Enlightn-vetted panelists only, or full coverage where the gap is filled from external partner sources - labeled and priced separately. Nothing is blended without the buyer seeing it.

## How Enlightn positions itself

Two registers, used deliberately:

- **Buyer-facing - a sample provider.** The familiar category buyers already budget for. Enlightn does not differentiate on "better quality" (the claim every supplier makes); it differentiates on proof - the quote before fieldwork, the fieldwork analysis after. The plain mechanism: we recontact panelists we already know instead of routing anonymous traffic into a screener.
- **Industry / investor - a research recruitment engine** built on match-first activation. "Match-first vs. route-into-screeners" is the named inversion that travels in this register.

Enlightn is not a marketplace or aggregator: nothing is blind, and nothing is blended without the buyer seeing it. It does not compete on volume or price; it competes on verifiable respondent quality, pre-activation match confidence, and a provenance layer that ties every completed interview back to source. Buyers work with Enlightn when they need data they can defend, not when they need cheap scale.

## Key measured claims (with caveats)

- **2.1x good-to-bad quality ratio vs. benchmark** - On studies run to date, the ratio of good-quality to bad-quality completes from Enlightn-activated respondents has come in at 2.1x that of benchmark supplier traffic on the same studies.
- **43% lower disqualification rate vs. benchmark** - Evidence that matching before the invitation identifies the right respondents for the right study, so fewer get terminated mid-survey.
- **61% of panelists pass the AI-powered data-cleaning layer** - Only panelists confirmed by the cleaning layer are re-engaged on client studies. The approach is intentionally conservative.
- **69% recontact completion at four months** - In Enlightn's latest recontact test, 69% of panelists profiled four months earlier completed a new survey within about a week, at a normal incentive, with quality holding. One cohort, one supplier; treated as an upper bound.

These are averages across projects run to date. Results vary by study; per-project results are shared openly with clients.

## Key data points (with dates and sources)

- **2.1x good-to-bad quality ratio vs. benchmark** (Q1 2026 pilot, appliance-buyer study, N=225 target). Source: Enlightn internal fieldwork data; methodology details available on request at contact@enlightn.io.
- **43% lower disqualification rate vs. benchmark** (same study). Enlightn respondents disqualified at a lower rate than benchmark supplier traffic on identical screener logic.
- **61% of profiled panelists pass the AI-powered data-cleaning layer** (rolling average across all projects to date, 2026). Only panelists who pass the layer are re-engaged on client studies.
- **83% good-quality profiling rate** (Phase 1 pilot, 1,377 entered, 951 completed, 821 passed quality tagging). Demonstrates the profiling layer is clean before any activation.
- **69% recontact completion at four months** (recontact test, June-July 2026, 179 random invitations to panelists profiled in early March 2026; 76.5% responded, 69.3% completed a ~4-5 minute survey within about a week). One cohort, one supplier, short survey - treated as an upper bound for full-study completion.

### Panelist survey on disqualification and pay (N=300, February 2026)

Published in full at [Panel data quality: what 300 panelists say about disqualification and pay](https://enlightn.io/blog/panel-data-quality-300-panelists). 300 completed responses from Enlightn-vetted panelists across 7 sample suppliers; all figures self-reported.

- **63% name being screened out after starting as a top frustration** - ahead of surveys running long (56%) and low rewards (45%).
- **The median panelist finishes only 4-5 of every 10 surveys they start**; 28% finish 3 or fewer.
- **Feeling underpaid tracks disqualification, not pay levels.** Among panelists who finish 0-3 of 10 surveys, 74% rate their pay "low" or "very low"; among those who finish 8-10, only 23% do. Same reward levels, more than three times the grievance.
- **Median "fair" reward for a 10-minute survey: $1.25** (~$7.50/hour). 49% named a dollar or less. Evidence that the pay anchor has drifted down to meet a depressed market.
- **Panelists most hurt by screen-outs most want them fixed:** among those flagging disqualification as a top frustration, 35% pick "you'll likely qualify" as the single most important promise a platform could keep, versus 18% of everyone else.
- **Survivorship is the headline finding.** Every respondent is someone the current system still retains - 79% take surveys daily, and the heaviest users report the highest enjoyment. The people who burned out or priced themselves out are absent from the frame, which is why statistical weighting cannot correct for them.

Caveats: numbers are observational pilot data, not a randomized controlled trial. Benchmarks are supplier traffic on the same study, same screener. Larger-sample validation is ongoing through 2026. The N=300 panelist survey is self-reported and, by construction, samples only panelists still active.

## Vocabulary

Buyer-facing language is plain: "recontact panelists we already know," "anonymous traffic" (the thing we avoid), "suppliers we've tested," the quote / the fieldwork analysis / activation log, provenance, "verifiable, not claimed." The terms below are named concepts.

- **The quote** - Enlightn's before-fieldwork deliverable: an interactive, password-gated page produced within 24 hours of receiving a brief, showing who would be recontacted, the evidence behind each match, a confident floor per target (matched x recontact rate x incidence), and transparent pricing. Free on a live brief; billing is per delivered complete.
- **The fieldwork analysis** - Enlightn's after-fieldwork deliverable: incidence, disqualification rate, completion, and drop-off for the study, plus who qualified and why, per source. The provenance record a research agency can forward to its own client.
- **Match-first activation** - a recruitment model in which participants are profiled and ranked for fit against a study's targeting spec before any invitation is sent; only strong matches are activated, reducing screen-out rates and the fraud surface associated with broad routing. It replaces the industry default of "route into screeners."
- **Permissioned supplier activation** - activating a match through a panel supplier's own recontact flow, under that supplier's consent and privacy terms; the participant relationship stays with the supplier.
- **Activation log** - a per-study record of which respondents were activated from which source, with match-confidence and outcome (completed, terminated, disqualified); the raw record behind the fieldwork analysis.
- **Provenance layer** - the transparency infrastructure that lets a research buyer trace every respondent back to source, match-confidence score, and quality signals - before, during, and after fielding.

## Who Enlightn is for

- **Research agencies (primary).** Agencies delivering fieldwork to brand and consultancy clients - project managers, fieldwork directors, operations leads, senior consultants. They live the quality problem daily and need to produce confidence with their own end clients; a fieldwork analysis they can verify is quality they can defend in a deliverable.
- **Insights managers, research directors, and fieldwork leads** who need to defend who actually ended up in their studies - people tired of taking quality claims on faith.
- **Qual-at-scale and AI-interview platforms (secondary).** They upgraded the interview but still source from the same pool of survey-rushers; Enlightn's profiled, engaged participants are the supply layer that matches their product.
- **Suppliers and panels (partners, not buyers).** Enlightn protects their best panelists from screen-out burn, pays a profiling fee before any activation, and shares premium CPI on each one.

## How engagements work today

The recruitment engine is live - profiling, match-first ranking, provenance logging - and the engagement is founder-led: the interface is a conversation, one person accountable for the whole result. Four steps: (1) the client sends a brief, specs, or screening questions; (2) within 24 hours they receive the quote - who we'd recontact, the evidence behind each match, a confident floor per target, transparent pricing; (3) fieldwork runs through Enlightn's supplier partners' permissioned recontact flows, billed per delivered complete; (4) after fieldwork they receive the fieldwork analysis. No procurement project, no integration, no form to learn.

## Commercial shape

CPI-based, at a premium justified by what the standard CPI hides - replacement waste, fieldwork babysitting, and data buyers end up defending instead of using. All in, buyers can expect a CPI about 20-30% higher than standard supplier pricing; external top-up, when approved, is priced at real cost plus a disclosed margin. Two low-friction ways in: a **pilot alongside the buyer's current supplier** (same study, a slice of the sample, metrics agreed upfront, the fieldwork analysis decides), or a **top-up engagement** (Enlightn fills only the cells current suppliers struggle with - low incidence, hard-to-reach - from panelists it already knows, fieldwork analysis included).

## Positioning as a supplier

Enlightn operates as a premium sample provider in the market research ecosystem. It does not compete with aggregators or programmatic marketplaces on volume or price. It competes on verifiable respondent quality, pre-activation match confidence, and a provenance layer that ties every completed interview back to source. Buyers work with Enlightn when they need data they can defend, not when they need cheap scale.

## Panel partnership model (for supplier partners)

- **Permissioned activation** - panelists are activated through the partner's existing recontact flow; nothing runs outside the partner's infrastructure.
- **Opt-in profiling** - panelists opt into Enlightn profiling with explicit consent; the partner keeps the primary participant relationship.
- **Paid per profiled panelist** - the partner is paid for every panelist profiled through the partnership, independent of later study matching.
- **Premium CPI, shared** - match-first activation justifies a higher CPI to buyers; the premium flows back to the partner on top of the profiling fee.
- **No integration project** - the partnership plugs in without the partner rebuilding any routing systems.

## Frequently asked questions

### How is Enlightn different from Cint, Dynata, or traditional sample providers?
Marketplaces and most panels route anonymous traffic into a screener and filter after the click - the buyer never knows whether a panelist was properly profiled or just pushed through a pre-screener tuned to qualify. Enlightn only recontacts panelists it already knows - profiled, quality-checked, and matched to the study before any invitation goes out - and shows the evidence behind each one. The result: higher quality and higher incidence, from the same suppliers underneath. A smarter order of operations.

### Everyone claims quality. Why is this different?
Agreed - every supplier says it. That's why Enlightn doesn't ask buyers to believe it: run a pilot alongside the current supplier, same study, a slice of the sample, verdict metrics agreed upfront. The fieldwork analysis decides, not the pitch.

### What happens when respondents misrepresent themselves?
The data gets quietly distorted. Respondents who learned to game screeners answer as whoever qualifies - segments get polluted, incidence reads wrong, and the business decisions built on that data don't maximize the outcomes they were supposed to. It's not even always malicious: panelists get terminated so often that misrepresenting becomes rational. Enlightn's model removes that incentive - the panelists it recontacts were profiled with open-ended questions long before the study existed, and they're only invited when their real profile matches.

### Where do Enlightn's panelists come from?
From panel suppliers Enlightn has tested for quality, through permissioned partnerships. Panelists opt into profiling with explicit consent, answer open-ended AI-assisted questions, and pass a cleaning layer before entering the pool - 61% make it through. Conservative by design rather than padding the pool.

### Do recontacted panelists actually respond months later?
That's the mechanism the model rests on, so Enlightn measures it. In the latest recontact test, 69% of panelists profiled four months earlier completed a new survey within about a week, at a normal incentive, with quality holding. One cohort, one supplier - treated as an upper bound.

### What exactly is the quote?
A feasibility answer on a live brief, delivered within 24 hours as an interactive page: who we'd recontact, the evidence behind each match, a confident floor per target - matched x recontact rate x incidence, with the math shown - and transparent pricing. It's free, and buyers are billed only for delivered completes.

### What's in the fieldwork analysis?
The after-fieldwork deliverable on every project: incidence, disqualification rate, completion, drop-off - and who qualified and why, per source. It's built to be forwarded: the agency's client sees the same provenance the agency does. And because every study reports the same metrics the same way, buyers can compare Enlightn's performance against their other suppliers on facts, not impressions.

### Can Enlightn fill my target in my market?
The quote answers that in 24 hours, with evidence - and when the answer is no, buyers hear it fast. When the pool is thin on a cell, the quote says so, and the buyer chooses: Enlightn-vetted panelists only, or full coverage where the gap is filled from external partner sources, labeled and priced separately. Nothing is blended without the buyer seeing it.

### What CPI can I expect?
Enlightn is a premium sample provider, with real costs behind the quality - AI-assisted profiling, AI-powered data cleaning, fraud detection. The approach also produces much higher incidence rates, which pulls variable costs down. All in, expect a CPI about 20-30% higher than standard supplier pricing. When external providers are needed to cover a gap - always transparently, with the buyer's prior approval - Enlightn applies a margin on top of its real costs, much lower than competitors', and disclosed.

### Can Enlightn cover B2B studies?
Enlightn focuses on consumer surveys, working with suppliers focused on consumer acquisition. That said, two-thirds of its profiled panelists are in the active population, so it can fulfill B2B projects. Send the brief and Enlightn answers transparently.

### Where is Enlightn based?
Enlightn is based in Montreal, Quebec, Canada. The company is Enlightn Technologies, founded by Adrien Vermeirsch.

## Founder

Adrien Vermeirsch, founder of Enlightn. Based in Montreal. Five years at Potloc before founding Enlightn, where he saw first-hand how sampling actually breaks - blind routing, opaque supply chains, fraud that suppliers are economically incentivized to ignore. Enlightn was built to fix the part he couldn't stop thinking about.

## Contact

- Email: contact@enlightn.io
- LinkedIn (company): https://www.linkedin.com/company/enlightn-technologies/
- LinkedIn (founder): https://www.linkedin.com/in/adrienvermeirsch/