An insight is only as good as the data underneath it.
Survey programming, data collection, fraud and bot screening, coding and insights operations — delivered by Philippine research specialists who protect data integrity end to end, so the chart you present to a client is one you can actually defend.
BPO Partners
Completes / Year
Delivery Hubs
A growing share of online survey sample is now bots, fraud and inattentive respondents. Field a study without rigorous screening and you are not measuring a market — you are measuring noise, then briefing a board on it. Data quality is the entire product.
Your research and your respondents decide where integrity is tested first.
These are the four research profiles we build for most often — each with its own integrity surface, each served by the same screened, calibrated operation.
The full integrity stack — programming, screening, coding, reporting.
Screened fieldwork, calibrated coding, CI pipelines, and synthesis support — the chart defensible before it’s presented.
Low-incidence, high-stakes samples — specialist screening tuned to hard-to-reach populations, with compliance-grade respondent handling.
Screening-as-a-service on your panel, removal rates reported to your clients, the quality story that wins the next RFP.
10,000 responses in, a clean dataset out — what gets caught at each gate?
Every layer removes a different kind of bad data — and a vendor that skips any one of them ships you a contaminated sample. Click a gate to see what it catches and how much.
“The frightening thing about bad survey data is that it looks exactly like good data once it is in a chart. A board cannot see the bots behind a bar. The only protection is a screening process rigorous enough that what reaches the analyst is real — and most vendors quietly skip it to hit a cost-per-complete.”
A sample filler vs. an integrity-grade research operation.
Seven controls that decide whether your dataset is signal or noise — and which model actually runs them.
Clean data can still produce a wrong finding. The coding frame is the second defense.
The quality screen guarantees that what reaches the analyst is real. It doesn’t guarantee what happens next — because a finding can be corrupted twice: once in the field by bots, and once at the desk by a coding frame that drifts. The same integrity discipline runs both.
Calibrated coding frames, inter-coder agreement measured and reported, and human coders on the judgment calls — sentiment, intent, the theme that doesn’t fit the frame. The verbatims that survived the fraud screen deserve better than a keyword bucket.
Automated tagging is fast and quietly biased — a model that over-codes one theme reshapes a report as surely as fabricated verbatims do. Every AI-assisted coding pass runs against a human-calibrated validation sample, with drift measured and corrected wave over wave. The qualitative twin of the open-end fraud screen: one catches fake respondents, the other catches a fake pattern.
Charting, dashboarding, and report production against your templates — the deliverable built by the same operation that can defend every number in it, from raw complete to final chart.
The through-line: integrity end to end now means what the hero says it means — the respondent verified at the door, the code verified at the desk, the chart defensible in the boardroom. It’s the golden-set discipline at a second surface: verified records, verified respondents — data you can bill on, data you can chart.
Competitive intelligence — sourced and validated, not scraped and hoped.
Desk research, market and competitor monitoring, and source-validated CI pipelines with metadata provenance on every input — because a strategy deck built on an unverified scrape has the same problem as a tracker full of bots: it looks exactly like the real thing until someone acts on it. The removal-rate discipline, pointed at secondary sources.
Four ways a finding dies — two in the field, one at the desk, one in the pipes.
A study’s risk isn’t one surface; it’s four, and each is a finding corrupted at a different point in the pipeline. Four failure points, four controls — and a chart you can defend at every one of them.
The through-line: the funnel catches the fake respondent; the calibration catches the fake pattern; the perimeter catches the leak; the sandbox catches the poison. Four failure points, four controls — and a chart you can defend at every one of them.
Where does the 6.5× return come from when the data is real?
From four streams a cost-per-complete ignores: bad-decision avoidance, refield costs eliminated, faster turnaround and labor arbitrage. The most expensive sample is the one that ships a wrong answer to a client’s boardroom.
$6.1M net benefit on $940K program
John Maczynski (CEO) · Q2 2026
Same tracker, same panel, one screen added — a screening-only retrofit, measured.
RS-042 proves the full integrity operation. This is the floor — and the proof is unarguable, because nothing else changed: the screen inserted into an existing, otherwise-untouched tracker. Whatever it removed was always in the data.
RS-042 proves the full integrity operation; RS-049 proves the entry point — and the proof is unarguable, because nothing else changed. Same field, same panel, one screen: whatever it removed was always being charted. A research operation doesn’t need a transformation to find out what its data is made of — one control, inserted between field and delivery, answered the only question that matters in a quarter. The scariest number in research is the removal rate you never measured.
Here is the cost per complete. Now here is what a wrong answer in a boardroom costs.
Every RFP compares cost-per-complete, so we publish the rate math. Then we switch the denominator — because a cheap complete that’s a bot isn’t a saving; it’s noise you paid to collect, chart, and present.
The rate lens prices the complete; the finding prices the study. The sample filler is cheap per complete and catastrophic per decision: the light screen ships fraud into the tracker, the refield costs the savings back, and the wrong answer — the one nobody catches — walks into a boardroom wearing a confidence interval. Switch the denominator and the four streams a cost-per-complete ignores — bad-decision avoidance ($1.6M–$3.2M), refields eliminated ($0.9M–$1.8M), faster turnaround ($0.8M–$1.6M), and programming & ops arbitrage ($1.0M–$2.0M) — stack to a $4.3M–$8.6M annual net benefit.
That is how RS-042’s $940K program returned $6.1M (6.5×): 22% of a tracker caught as fraud before delivery — data that would have shown a false brand-health decline and triggered a misguided repositioning. The cheapest complete is the real one. The most expensive is the bot you briefed a board on.
Indicative 2026 rates — the integrity roles shown apart from the processing seat.
Tabulation has a market rate; the roles that make a dataset defensible do not. The analyst who owns the removal rate and the lead who keeps the coding frame honest are what the chart stands on — and a quote at the processing band for either is the sample-filler model with a price on it.
— no generic equivalent$11–$15Owns the removal rate — runs the multi-layer screen and reports exactly what was caught, and whySCREENING
— no generic equivalent$12–$16Owns frame consistency — validation samples, inter-coder agreement, the AI-bias auditCALIBRATION
The two premium rows have no generic equivalent because they own the two numbers this page tells buyers to demand: the removal rate and the agreement rate. A sample filler doesn’t staff either — that’s how the cost-per-complete gets so low. Rates confirmed per engagement against study mix and volume.
Price my ops against the integrity-grade standard →An integrity-grade research desk live in 6 weeks — screening proven on a pilot.
A gated stand-up. No study fields at volume until the screening logic is validated on a pilot and the fraud-removal rate clears target.
Before you trust a vendor’s sample, what do you need to see them remove?
Three controls decide whether you receive signal or contaminated noise — and each is demonstrable on a pilot before you sign. A low cost-per-complete usually means one or more of them was skipped.
“Ask a research vendor one number: what percentage of sample they remove as fraud, and how. If the answer is low, or vague, they are not screening — they are filling. And a board that acts on unscreened data is making a million-dollar decision on noise. The removal rate is the whole tell.”
Where the integrity operation doesn’t fit — and whose finding it always is.
A shortlist that includes “no” is the only kind worth having. Three engagements we turn down — and why the refusal is the point.
We program your instrument, protect your data, code to your frame, and flag what we see — the anomaly, the drift, the theme that doesn’t fit. But the research design and the finding belong to your researchers. A vendor writing your conclusions is doing your job, badly, at a discount — and your client is paying for your judgment, not ours.
The integrity model only pays off when quality is measured: removal rate, reject rate, agreement rate. If the mandate is cheapest completes with a light screen, that vendor is available — and the funnel above shows exactly what you’ll be charting. Our value is the finding you can defend; noise is cheaper everywhere.
The screen runs inside your Qualtrics/Decipher/Research Defender stack under Zero-Trust VDI — the respondent, the fingerprint, and the removal log on one screen, with respondent PII at zero local residency. Data exported to a vendor’s own environment is a provenance break — the exact poisoning risk the matrix names.
A board can’t see the bots behind a bar chart. Bad data looks just like good.
Tell us where research ops strain — programming, fielding, data quality — and we’ll hand you 6–10 vetted integrity-grade hubs, each proven on a live fraud-screening pilot before reaching your shortlist.
Get my research shortlist →Our 24-Hour Response Guarantee — a reply within 24 hours, fraud-screening pilot pre-screen included.
The insight-reliability standard: the economics of research services outsourcing.
Why reports delivered is a volume vanity metric, how source reliability and decision-usable findings — never research throughput — decide the true cost of a desk-research operation once unsourced claims, stale data, cherry-picked evidence and analyst rework are counted, and the vendor-selection discipline that delivers a report an executive can act on. Volume 70 of PITON-Global’s Executive White Paper Series, by John Maczynski and Ralf Ellspermann.
The questions research leaders ask before they outsource.
In-depth answers to the questions that decide a research outsourcing engagement — from the principals who run them.