Solutions & Use Cases

Yes. Transcription is benchmarked across 82 languages, each with a published word error rate. Text recognition routes by script class, and the Perso-Arabic class covers Arabic, Urdu, Sindhi, Dari and Pashto, so a request does not stall because the record is not in English.

Originals are preserved untouched, every redaction carries its statutory justification, and a full audit trail runs from intake to disclosure, ready when a request becomes a dispute.

It is the tooling used after a fraud is suspected: examining the documents that were submitted, the footage and photographs on the file, and the statements that were recorded, then building a package that survives referral. It is distinct from fraud detection, which scores transactions or identities in real time to raise the alert in the first place.

Detection platforms score transactions, identities and onboarding attempts and raise alerts. They do not read the loan file, watch the dashcam or listen to the recorded statement. This examines that material. It complements a detection platform rather than replacing it, and it is deliberately not transaction monitoring.

Yes, at the document level. Submitted files are read with OCR and handwriting recognition, signatures are detected and compared across the set, file integrity is checked for alteration, and exact and near-duplicate detection catches paperwork reused across applications. Agentic workflows then cross-check names, amounts, dates and terms for inconsistencies and score the file.

That is what near-duplicate detection is for. Submissions are compared against prior files rather than assessed in isolation, so the same bank statement or invoice appearing on a second application surfaces instead of passing because each file looks fine on its own.

Both. The same examination applies to a loan or trade-finance file and to a claim, and on the claims side it extends to photographs, dashcam and surveillance footage and recorded statements held in one evidence record per claim.

The package leaves with personal data removed on the shared copy while the original stays intact, and with an audit trail of who accessed what and when. That trail is built as the work happens rather than reconstructed afterwards.

Yes. Deployment covers your own cloud, on-premises and fully air-gapped, which is normally a condition of handling case material and personal data at this level.

Using the cameras an agency already operates, fixed and vehicle-mounted, to continuously detect infrastructure problems like road surface defects and obstructions, and automatically file the maintenance work orders.

No. Cameras on buses, patrol cars, and service vehicles that already drive your network daily become the inspection fleet, connected over RTSP/ONVIF.

AI Intelligence Hub workflows create and route tickets into 311, CRM, and dispatch systems, with snapshots attached and duplicate sightings collapsed into one order.

Road surface defects, obstructions, improperly parked vehicles, and condition changes across monitored corridors, with detections correlated across passes to cut false reports.

In government cloud or on-premises. Camera streams and detection history stay inside your network.

IDP turns bulk document intake into structured, validated data: OCR and ICR reading, layout-aware extraction, automatic classification, and business-rule validation, with human review reserved for exceptions.

They describe the same work. Intelligent document processing is the analyst term for the category; document processing automation is how buyers usually describe the outcome. Both mean reading bulk documents, classifying and extracting them, applying business rules, and sending only exceptions to a person. Where they differ is emphasis: "intelligent" points at the OCR, ICR and classification models, "automation" at the workflow that runs end to end without a queue of reviewers.

Yes. Handwriting, Perso-Arabic scripts, and skewed or low-quality scans, not just clean PDFs. Layout detection preserves tables, headers, and columns so extraction lands in the right field.

Into your systems: structured output flows to ERP, CMS, and databases through APIs, with runs triggered by schedule, webhook, or API call, and an anonymization option where downstream systems should not see PII.

No-code agentic workflows apply your business logic and route only low-confidence or rule-breaking documents to human review, so people check judgment calls, not every page.

Integrity checks, malware detection, and duplicate identification run at intake, before a corrupt or recycled file gets into the pipeline.