How to Stop Bribery in Field Inspections
Bribery persists in field inspections because visits are invisible to the company that dispatched them. The structural fix: make every visit generate its own evidence, with consent, video, GPS, and a signature, put AI review on 100% of visits instead of audit samples, and escalate only flagged work to supervisors.
Why bribery survives audits
Field inspections happen far from any supervisor. A bribe to overlook a problem, an inspection closed from a parking lot, a report that says no evidence was available: these thrive precisely because nobody is watching, and because traditional control is a sampling audit that reviews a small fraction of visits after the fact.
Sampling has a structural flaw. Whatever share of visits you audit, fraud simply lives in the share you do not. And by the time a sampled audit surfaces a problem, the trail is old.
Step 1: Make evidence unavoidable
The inspection itself should produce the proof. In Fluyenta's model, the technician works from a mobile app: the customer's consent is recorded before the inspection starts, the visit is captured on bodycam video with GPS coordinates and timestamps, and the work order closes with a digital signature. The evidence is sealed with a cryptographic fingerprint, so nobody, including the company, can quietly edit it afterward.
Step 2: Review everything, not a sample
The moment a visit closes, AI goes to work. The audio is transcribed and screened for bribery language. AI vision analyzes key frames from the video. Audio quality signals such as excessive silence, muffling, and sudden cuts are checked, because each can indicate tampering. Every visit receives a fraud risk score from 0 to 100. No sampling, no queue of unreviewed work.
Step 3: Let humans handle only what matters
Clean visits flow through untouched. Visits above the review threshold route to a supervisor. Visits above the critical threshold fire an urgent task immediately, with the complete evidence chain attached: video, transcript, GPS trail, consent record, and signature. Supervisors stop reviewing paperwork and start reviewing exceptions.
The counterintuitive rule that closes the loophole
If recording is optional, the fraudster's move is obvious: do not record. So the system treats the absence of evidence as a signal in its own right. A work order without evidence does not close cleanly, and the missing evidence raises the visit's risk score by itself. Trying to game the system is what gets a visit flagged.
You can't put a supervisor in every truck. You can put an AI witness on every visit.
This model is not a concept. It runs in production with real field crews at a Colombian electric utility, live since July 2026.
Where would fraud hide in your operation?
Bring one inspection type. We will map the evidence chain and scoring against it.
Book a consultationStraight answers
Will bodycams on our own employees cause pushback?
The deployment model starts with customer-facing consent: recording begins with the customer's recorded consent, and the evidence protects the technician as much as the company. GPS, video, and signatures end disputes about whether a visit happened, and honest workers stop being suspects when evidence exonerates them by default.
What about false positives?
The AI never accuses anyone. It scores visits and routes the flagged ones to a human supervisor who reviews the actual evidence. Thresholds are configurable by the client's own managers.
How is worker and customer privacy protected?
Consent is captured at the start of every session, access to footage is role-gated, every access is itself logged, and evidence is encrypted. The platform holds ISO 27001:2022 and ISO/IEC 27701:2019 certifications and is GDPR aligned.