We build synthetic insurance documents and security logs. Broken tables, corrupted fonts, scan artifacts, carrier-specific layouts, attack scenarios buried in noise. The stuff your agents choke on in production, with labels already attached.
HIPAA, SOC2, carrier agreements. The documents your agent needs to handle are the ones you can't legally keep or reuse for training.
Teams that try it get clean, self-consistent docs. The agent looks great in eval, then falls apart on real inputs with real formatting problems.
SME hourly rates, weeks of turnaround. You get maybe a few hundred labeled docs before the budget or the timeline kills you, and that's nowhere near production volume.
Carriers format differently, lines of business have different fields, and every scan introduces different artifacts. The combinations multiply faster than any labeling team can keep up.
Frankly it's enough to convince me you'd be capable of getting us real-enough data for benchmarking and potentially finetuning. You have genuinely neat tech I have not seen from any other company.
I used the data to validate a patent-pending authorization framework against real enterprise access patterns. The quality of the synthetic data was good enough to build production AI on — that's an extremely high bar.
Complete document packets with ground truth at three levels: document, field, and bounding box. Loss runs, ACORD forms, SOVs, dec pages, broker narratives, and more, each rendered through 82 carrier-specific templates with 56 visual variants sourced from real reference PDFs.
Okta System Log event streams with attack signals buried in 52K events of normal traffic. Attack events are <0.2% of volume, and 33 false-positive patterns make sure your agent can't cheat with single-signal detection. No structural tells. Agents have to reason about behavior.
Tell us the doc type, carrier format, or attack scenarios you care about, and which edge cases matter most.
Our engine builds the documents or logs with the specific layout problems, format variation, and corruption you asked for.
Same idea as computer vision: we placed the data, so we already know what's in it. Every file ships with ground truth. No annotation step, no SME bottleneck.
I shipped 250+ AI agents to Fortune 500 companies at Moveworks and watched them break on real-world inputs. Before that I spent a decade in security research finding bugs in Apple, Chrome, and Qualcomm. Aginor came from putting those two things together: I know what production data does to agents, and I know how to generate the inputs that break them.
Tell us your doc type or attack scenario and we'll generate a sample batch with labels.
Request sample