Research infrastructure. Not a panel marketplace.

From sampling to validated data

Multi-layered AI and human verification ensures real professionals and reliable insights.

Start a Study How the System Works
2.3M
Verified B2B profiles
90+
Active markets
200+
Real-time quality checks
<4h
Time to field
How It Works

Three systems. One purpose: data you can stand behind.

Most panel providers hand you a dataset and let you figure out what's in it. We operate three distinct systems that run before, during, and after completion—so the quality work is done before delivery, not left to your cleaning scripts.

Sampling System

Quota management runs against verified profile segments—not raw panel pools. Recruitment sources, refresh cycles, and incidence rate estimates are documented before a study opens, not discovered mid-field.

Sampling methodology →

Validation System

Identity verification runs before the first question loads. Firmographic corroboration, geo-signal triangulation, device-level deduplication, and behavioral scoring run continuously through the completion window.

Validation checkpoints →

Quality Acceptance System

After completion, responses pass through post-submission processing: open-end similarity scoring, cross-response consistency checks, and final acceptance decisions. What reaches your dataset has cleared all three stages.

Acceptance criteria →
2.3M
Verified B2B professionals
Enterprise, specialist, and clinical audiences
90+
Active research markets
Local compliance frameworks, in-market verification
200+
Real-time quality checks
Behavioral analysis and dynamic fraud detection per completion
4,200+
Projects delivered
Full audit trail and quality report with every dataset
Research analyst reviewing sampling framework documentation and incidence rate data
Sampling System

Quota management against verified segments—not self-reported profiles

The sampling problem isn't finding enough respondents. It's finding the right ones and being able to document exactly how you found them.

Probabilistic quota management runs against corroborated firmographic segments—seniority, buying authority, and company size are verified, not assumed
Incidence rate estimates delivered before study launch—not discovered after quotas stall mid-field
Recruitment source documentation included in every quality audit—blended channels, refresh cycles, opt-in protocol version
90-day participation cap enforced at the panel level—respondent fatigue affects data quality in ways that are invisible without the cap
Sampling Documentation →
Validation System

Filtering happens before completion. Not after.

Post-hoc cleaning finds the obvious cases. Pre-entry and in-survey validation catches the sophisticated ones—the respondents who know what quality checks look for and have adapted their behavior accordingly.

1

Entry Screening

Before the first question loads: geo-signal validation, device fingerprinting, VPN/proxy detection, bad-actor ID matching, profile freshness check.

11 checkpoints
2

In-Survey Behavioral

During completion: per-question timing analysis, straight-line pattern detection, embedded attention items with topic-specific known-answer validation.

7 checkpoints
3

Post-Completion Acceptance

After submission: open-end AI similarity scoring, cross-response consistency checks, final acceptance decision. Flagged responses held for review—not silently excluded.

5 checkpoints
Full Validation Documentation →
Core Capabilities

What the system does, stated plainly

No superlatives. These are the mechanisms—how each part of the platform works and what it's designed to prevent.

Deterministic Identity Matching

Respondent profiles are cross-referenced against professional registries, firmographic APIs, and public signals before they enter any study. Self-reported job titles don't fill verified quota cells.

Hardware-Level Deduplication

Device identity is derived from 94 hardware attributes—not cookies. The same physical device cannot contribute more than one response per study, regardless of how many profiles or browsers it uses.

Behavioral Timing Analysis

Per-question completion times are scored against type-specific benchmarks built from 4.2M validated completions. A fast overall time doesn't matter if the per-question pattern is implausible.

Open-End Similarity Scoring

LLM-based embedding detects clustered responses, template patterns, and AI-generated text. Responses flagging on two of three similarity measures don't reach your dataset.

Quality Architecture

Structured across distinct validation stages

Because where respondents drop off matters as much as how many complete.

Screened
Every attempt evaluated against geo-signal, device fingerprint, identity, and profile criteria before the first question loads. The majority of rejected respondents are caught at this stage.
Filtered
Responses removed across all three validation stages — not in a single post-hoc sweep. Stage, checkpoint, and reason for each removal are recorded and included in your audit report.
Accepted
What cleared every quality check across all three stages. This is your delivered dataset — and every upstream rejection that produced it is documented in the quality audit shipped with every study.
View Full Acceptance Framework →
B2B research buyer journey showing professional survey respondent verification
Industries

Specialist sampling for research that needs domain credibility

General-purpose sampling gets you to a job title. Specialist panels get you to a verified credential—license board confirmation, firmographic corroboration, or stack-level verification depending on what the study requires.

Research analyst reviewing sampling framework documentation and incidence rate data
Enterprise
B2B Research
Decision-makers from SMBs to Fortune 500, segmented by role and verified buying authority.
 Research analyst reviewing sampling framework documentation and incidence rate data
Healthcare
Medical & Pharma
Clinicians, pharmacists, and administrators. License board verification across 28 markets.
 Research analyst reviewing sampling framework documentation and incidence rate data
Technology
Tech & SaaS
CISOs, CTOs, and software buyers. Stack-verified where study design requires it.
 Research analyst reviewing sampling framework documentation and incidence rate data
Financial Services
Finance & Insurance
CFOs, fund managers, and compliance professionals. Regulatory jurisdiction documented per market.
All Industry Panels →
Panel Infrastructure

A sampling source you can describe in a methods section

Peer reviewers ask about sample sourcing. So do acquisition analysts. So do regulators. The documentation you need to answer those questions ships with every study.

Full documentation of recruitment sources, verification protocols, and opt-in procedures for journal methods sections
GDPR, CCPA, and APEC Privacy Framework compliance documented by market
Incidence rate estimates before you finalize screener design—not after launch
Participation frequency caps at the panel level—90-day rolling window, enforced before invitation
Explore Panel Segments Request Feasibility Estimate
Research analyst reviewing sampling framework documentation and incidence rate data
Research & Analysis

Practitioner-level thinking on panel quality and methodology.

View All Publications →
Economics of B2B survey sample cost and data quality trade-offs
Industry Analysis
The Hidden Economics of B2B Sample: Why Cost Efficiency Often Degrades Data Quality

The unit price of a B2B complete has fallen. What that price masks is a supply chain structure whose incentives systematically select for the wrong kind of efficiency.

11 min readApr 28, 2026
AI impact on B2B survey panel supply and data reliability
Data Quality
From Traffic to Trust: How AI Is Reshaping B2B Sample Supply and Data Reliability

AI has changed both how panels recruit respondents and how bad actors exploit them — simultaneously. An analysis of three structural shifts and their implications.

9 min readApr 7, 2026
Researcher reviewing B2B panel quality benchmarks and evaluation criteria
Methodology
Why Most B2B Panel Quality Benchmarks Are Misleading — A Methodological Reframe

Incidence rates, completion percentages, and fraud flags don't reliably predict whether your delivered data will hold up under analysis. Here's a better evaluation framework.

10 min readMar 21, 2026
Work With Us

Commission your next study with full quality documentation

Share your target profile and study objectives. Our panel specialists will assess feasibility, provide incidence rate estimates, and outline the quality controls applicable to your study — before you commit to a design or budget.

Request a Feasibility Estimate Review Methodology →