AI Engineering · Vision

Computer Vision
that turns images into data

We connect the sources in scope to models validated for the agreed use case: inspection, quality control, counting or document reading. Results that clear configured thresholds may include marked evidence and confidence; low confidence, capture errors and other exceptions go to human review before a report or any action.

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Illustrative example · facade_scan.mp4

Illustrative facade example with markers for possible cracks and damp

Crack · 92% Damp · 87% Window · 99%

Capabilities

From the agreed source to reviewable data

We first assess representative samples, permissions and source coverage. Only formats and use cases in scope are processed; metrics, thresholds, retention and the review workflow are documented. Not sure where to start? Review our diagnostic tools.

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Building inspection

In material captured within the agreed coverage, the model can flag indications of cracks, damp or other defects for professional review.

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Quality control

We analyse parts captured within the agreed coverage and cadence. Metrics and thresholds define what is flagged; low confidence and capture errors are reviewed before a release decision.

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Counting and tracking

We can estimate counts or paths across included cameras where coverage, permissions and privacy requirements allow; occlusions and lost tracks are recorded as exceptions.

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Document reading

We extract configured fields from compatible documents and apply validation before sending them to a system; incomplete or uncertain readings require review.

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Reports with evidence

When included in scope, a report links available evidence, confidence and history within the agreed retention. Missing evidence or low confidence is flagged as an exception, not asserted as a confirmed result.

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Integration with your systems

We connect compatible sources and systems in scope. Providers, formats, permissions, cadence, retries and owners for exceptions are documented before the pilot.

How it works

From image to report in four steps

You connect the source

We define the cameras, photos, video or batches in scope and verify permissions, coverage, quality and ingestion events before processing.

The model analyzes

We validate the model on a representative sample using agreed metrics. Only the sources and classes in scope are processed, and failures remain visible.

The evidence gets marked

Results that meet thresholds are annotated with available evidence and confidence; uncertain cases or capture failures go to human review.

You get the report

We deliver the configured format, such as PDF, Excel or a compatible API. Only defined events generate alerts; retries, failures and exceptions are logged and assigned.

Use cases

Applications we assess by scope and sample

Construction: visual screening of facades and roofs within agreed coverage Manufacturing: quality control on captured parts and agreed criteria Retail: counting and analysis with defined camera coverage and privacy Logistics: tracking and verification with configured events and exceptions Real estate: before/after condition reports for renovation or rental Insurance: visual support for assessment, with review by the responsible professional Public administration: plate and document extraction with permissions and validation Hospitality: occupancy and room capacity control Agriculture: counting and indication alerts based on agreed thresholds Maintenance: periodic inspection of warehouses, towers and roofs

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Sources, coverage and ingestion events defined

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Metrics, thresholds, review and exceptions documented

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Provider, region, retention, roles and SLA agreed

FAQ

What people ask before starting

What kind of images can you analyze?

We can assess phone photos, IP or drone video, CCTV and stored batches. Feasibility depends on resolution, framing, lighting, cadence, permissions and coverage; a representative sample defines the sources in scope and whether capture must be adapted.

Do I need to install new cameras?

Not necessarily. We test a sample from the available cameras or photos and check quality, coverage, permissions and integration. If it does not meet the agreed criteria, we document which source or capture setup must change before the pilot.

How do I know the detection is reliable?

Reliability is validated on representative data using agreed metrics, thresholds and acceptance criteria; model confidence does not replace that validation. Low confidence, capture errors and other exceptions go to human review before entering a report or triggering a decision.

Where are the images processed and stored?

The proposal documents the processing and hosting provider and region, access roles, retention and deletion, and permitted training use. Exclusion from third-party training and EU-only residency apply only when supported by the agreed provider, plan, contract and configuration; GDPR compliance also depends on the architecture and the controller's obligations.

Does it integrate with what I already use: ERP, CRM, ticketing?

Results can be delivered as PDF, Excel or via API when the format and integration are compatible and in scope. Only configured events and thresholds trigger actions; retries, delivery failures and exceptions are logged and routed to the responsible role.

Computer Visionthat turns images into data