Comparison

Chequedb vs Azimut: Which Cheque Processing System Fits?

Azimut and Chequedb both address bank cheque processing, but they emphasize different parts of the problem. This comparison focuses on the decision points a bank or finance team actually has to test: handwriting, capture channels, deployment, workflow, audit evidence, and integration.

Public positioning reviewed 2026-10-19. Competitor details are taken from their public pages and should be confirmed directly. This comparison is not an endorsement or a substitute for a scoped evaluation.

Chequedb and Azimut capability comparison

Dimension	Chequedb	Azimut
Primary focus	Handwritten cheque OCR, on-premise processing, and audit-ready review workflow	Cheque processing system for banks and corporates, from capture to posting
Capture channels	Scanner, mobile, and API capture; confirm the exact adapters for your deployment	Mobile, desktop scanners, and kiosks, per its public page
Handwriting OCR	Explicit focus; benchmark protocol covers payee, date, CAR, LAR, MICR, and abstention	OCR and validation advertised; confirm handwriting scope and accuracy evidence
Deployment	On-premise and private-environment options; security/architecture page available	Confirm deployment and data-residency options directly
Workflow and approvals	Review queues, exceptions, approvals, reconciliation, and searchable archive	Exceptions controlled and auditable, per its public page; confirm approval depth
Audit evidence	Reviewer decisions, source image, model/rule context, and exportable audit history	Auditable exception handling advertised; confirm export and retention detail
Integration	API and agreed export formats to banking, ERP, and finance systems	Posting to core banking, clearing, and ERP systems, per its public page
Best fit	Handwriting-heavy, on-premise, audit-sensitive cheque operations	Multi-channel capture with core banking and clearing posting requirements
Pricing	$0.125/check with a $250/month minimum; 12,500/month = $18,750/year; $5k–$7.5k pilot credited; free synthetic sandbox	Confirm directly

For Chequedb capture planning, compare evaluation candidates in the cheque scanner hardware catalogue and review the scanner SDK integration guide to define the image, MICR, and metadata handoff for your deployment.

Azimut details are based on its public cheque processing page, reviewed 2026-10-19: azimutlabs.com/cheque-processing-system . Confirm current features and deployment options directly.

Where Chequedb may fit better

Handwritten cheques are a material share of the volume.
The bank needs an on-premise or private-environment deployment.
Audit evidence, reviewer decisions, and CAR/LAR exception handling are first-class requirements.
A scanner-agnostic software layer is preferred over a fixed capture stack.

Where Azimut may fit better

The priority is one system across mobile, desktop, and kiosk capture channels.
Posting into core banking, clearing, or ERP is the central integration requirement.
The bank already has a defined capture estate and wants a broader processing layer.
Azimut’s current deployment and handwriting scope match the bank’s requirements after direct confirmation.

Evaluate cheque processing and hardware integration

Use the same 50–100 de-identified cheques, including handwritten and degraded images.

Ask for per-field results: payee, date, CAR, LAR, CAR/LAR agreement, MICR, and signature presence.

Ask for abstentions and false accepts separately; do not accept one overall accuracy number.

Test the exception path: what happens when a field is wrong, uncertain, or contradictory?

Map the integration work: API, export, core banking, clearing, ERP, and reconciliation.

Review the deployment and audit evidence with security and internal audit, not only operations.

If a vendor cannot show a failed read and the review path it triggers, the evaluation is still a demo. Ask for the failure mode, not only the clean sample.

Handwritten OCR benchmark On-prem security & architecture Cheque management for banks Chequedb vs Veryfi

Evaluate with your own cheque images

The right choice depends on handwriting mix, capture channels, deployment constraints, workflow controls, integrations, and the evidence your bank requires. Run a scoped pilot before committing.

Scope an evaluation Use the OCR benchmark

Cheque software comparison questions

Is Chequedb a replacement for Azimut?

Not necessarily. Both address cheque processing, but the fit depends on capture channels, handwriting volume, deployment constraints, workflow controls, integrations, and the evidence your bank requires. Run both against the same representative cheque set before deciding.

Which one is better for handwritten cheques?

Chequedb positions handwriting recognition as a core capability and publishes a benchmark protocol for payee, date, CAR, LAR, MICR, abstention, and false accepts. Azimut’s public page advertises OCR and validation; confirm its handwriting scope and accuracy evidence directly.

Does Azimut offer on-premise deployment?

Confirm directly with Azimut. Its public page describes capture from mobile, desktop scanners, and kiosks, with posting to core banking, clearing, and ERP systems. Deployment and data-residency options should be verified in a scoped discussion.

How should we compare pricing?

Ask both vendors to price the same scope: capture channels, cheque volume, handwriting mix, workflow, integrations, deployment, support, and rollout. A per-seat or per-scanner price is not comparable to a flat-rate platform quote without the same scope.

What is the fastest way to evaluate both?

Use 50–100 de-identified cheques from your workflow, including handwritten and degraded images. Ask for per-field results, abstentions, false accepts, review time, and the path a failed field takes. Then compare deployment, audit evidence, and integration effort.

Competitor names and public descriptions are used for identification and comparison only. Confirm current features, deployment options, pricing, and contractual terms with each vendor. Chequedb does not provide legal advice.

API & Integration

Hardware & Deployment