AI Image Data Extraction
Point. Capture. Done. Asset Data Extracted From Any Image in Seconds.
Strev's AI image data extraction uses OCR and intelligent visual processing to read asset labels, delivery documents, invoices, warranty certificates, and equipment datasheets — capturing every relevant data field and creating the complete asset or operational record automatically. No manual typing. No data entry backlog. No gap between the physical document and the digital record that should reflect it.
OCR & ICR — reads printed text and handwriting
Mobile camera or uploaded image — any source
Human review before any record is created or modified
Seconds
Image to complete extracted record
Zero
Manual data entry from photographed documents
Delivery Invoice — Dell Q2 Hardware
Asset Label Scan — Compressor C4
Warranty Certificate — HVAC Unit
Intelligent Visual Data Capture
AI Image Data Extraction That Turns Every Document Photo Into a Structured Operational Record
Strev's AI image data extraction is purpose-built for enterprise asset and operational management — trained to understand what fields matter in asset labels, procurement invoices, warranty certificates, and compliance documents, and to extract those fields accurately from images captured in the field, at point of receipt, or from scanned archive documents.
OCR Image Data Extraction
Strev's OCR image data extraction reads printed text from any image format — labels, datasheets, certificates, invoices, delivery notes, and scanned archive documents. High-accuracy text recognition across different font types, print qualities, and document structures, producing structured field extractions from unstructured image sources.
ICR Handwriting Recognition
Intelligent Character Recognition (ICR) extends extraction beyond printed text to handwritten fields — handwritten serial numbers on asset labels, manual additions to printed certificates, field notes on inspection forms, and handwritten signatures on delivery receipts. Documents with mixed printed and handwritten content handled in a single extraction pass.
AI Metadata Extraction
Human Review Gate
Every image data extraction presents extracted fields for human review before any record is created or modified. Low-confidence extractions are flagged for attention. The user verifies, corrects if necessary, and confirms — the AI extracts, the human validates, the system records. AI speed with human accuracy as the quality gate.
Document Storage
Mobile Camera Capture
Field teams photograph any document or label using the Strev mobile app camera — no scanner hardware required. The image is processed instantly by the AI image data extraction engine, with extracted fields returned for review before the record is created. Asset onboarding and document processing at the point of receipt, delivery, or discovery.
Mobile App
Extraction Capabilities
Every Document Type Strev's AI Image Data Extraction Handles
01
Asset Label & Nameplate Extraction
Point the mobile camera at any equipment nameplate or asset label — Strev's AI image extraction reads serial numbers, model numbers, manufacturer names, manufacturing dates, and specification values. The AI understands common nameplate layouts and knows which label fields are operationally significant for asset management records.
Asset Management
02
Invoice & Purchase Order Extraction
Upload a purchase invoice or delivery note and the AI image data extraction engine reads line items, unit costs, quantities, supplier details, purchase order numbers, and delivery dates — creating individual asset records for each line item or populating procurement records automatically. Multi-item invoices processed in a single image extraction pass.
Vendor Management
03
Warranty Certificate Extraction
Photograph a warranty certificate and Strev extracts the coverage period, covered components, labour and parts terms, vendor contact details, and any claim conditions — populating the asset's warranty record and configuring the renewal alert automatically from the extracted expiry date. Warranty registration that happens at delivery, not weeks later.
Warranty Management
04
Contract & Agreement Extraction
Upload a contract image or scanned agreement and the AI reads vendor, contract value, effective dates, renewal terms, notice period, and key obligations — creating the contract record in Strev's contract lifecycle management module without manual re-entry of the document content.
Contract Management
05
Compliance Document Extraction
Process certification documents, inspection reports, calibration certificates, and compliance declarations — extracting certificate scope, issuing authority, issue date, expiry date, and certification reference. Compliance data captured and monitored from the moment the document is photographed, not filed in a folder and forgotten.
Document Storage
06
Multi-Image Batch Processing
Process multiple images simultaneously — an entire delivery of equipment photographed at goods receipt, a stack of historical documents being digitised, or a folder of scanned archive records being migrated into Strev. Batch image data extraction that scales from a single label to hundreds of documents without changing the process.
Bulk Data Import
The Problem
What Enterprise Operations Lose Every Day to Manual Data Entry From Physical Documents
The gap between physical documents — labels, invoices, certificates, datasheets — and the digital records that should reflect them is bridged by manual data entry. Manual data entry is slow, error-prone, and expensive at enterprise scale. It creates a data entry backlog that delays asset onboarding, produces records that don't accurately reflect the source documents, and consumes hours of operational time that AI image data extraction eliminates.
Asset Onboarding That Takes Days Instead of Minutes
When every new asset requires manual data entry from its delivery documentation — serial number typed from the label, warranty details copied from the certificate, specifications entered from the datasheet — a large procurement batch creates a data entry backlog that can take days to process. During that time, assets sit in a system record that's incomplete, missing the fields that warranty monitoring and maintenance scheduling depend on.
Transcription Errors That Propagate Into Operational Records
Manual transcription of serial numbers, dates, and specification values from physical documents produces error rates that compound across an enterprise asset base. A transposed serial number makes an asset untraceable. An incorrect warranty expiry date means coverage is either monitored past its end date or abandoned before coverage lapses. Errors that are expensive to discover and costly to correct at scale.
Documents That Get Filed Without Being Digitised
The path of least resistance for physical documents that require data entry is to file them without processing them. Warranty certificates get filed in a folder. Delivery notes are archived without creating asset records. Compliance certificates reach their expiry date in a drawer while the system record that should be monitoring them remains empty. The document exists; the data it contains doesn't.
Historical Document Archives That Are Unsearchable
Enterprise operations accumulate years of physical and scanned document archives — maintenance records, inspection certificates, procurement invoices, compliance evidence — that contain valuable operational data locked in image formats. Without OCR image data extraction processing these archives, the information exists but cannot be searched, filtered, reported on, or connected to the asset records it belongs to.
Field Teams Without Document Processing Capability
Field technicians who receive equipment deliveries, discover documentation discrepancies, or need to register new assets in the field have historically faced a choice: record asset details manually by hand or wait until returning to the office to process documents. AI image data extraction on mobile eliminates this choice — the camera is the processing tool, and the record is created in the field.
Data Entry as a Bottleneck to Operational Intelligence
The operational intelligence that Strev generates — predictive maintenance, compliance monitoring, warranty renewal alerts, vendor performance analytics — depends on the quality and completeness of the underlying asset and document data. When that data is incomplete because manual data entry created a backlog, the intelligence built on top of it is correspondingly limited. AI image data extraction removes the data entry bottleneck that limits everything downstream.
90%+
reduction in asset onboarding time when AI image data extraction replaces manual data entry from procurement documentation
Seconds
from photograph or image upload to complete extracted data record — presented for human review before any record is created
Any image
Labels, invoices, certificates, datasheets, inspection reports, contracts — OCR and ICR processing across all document types
How It Works
From Image Capture to Operational Record — How Strev's AI Image Data Extraction Works
Strev's AI image data extraction follows a consistent five-step process regardless of the document type, the image source, or the record being created — capture, process, extract, review, record. The same process that onboards an asset from a delivery note also creates a contract record from a scanned agreement.
Image Capture
Photograph or Upload — Any Image Source, Any Device
An image enters the Strev AI image data extraction system from one of three sources. Mobile camera: a field team member photographs a label, document, or certificate directly from the Strev mobile app using the device's camera. Desktop upload: a user uploads a scanned document, PDF image, or photograph from their desktop interface. Batch import: multiple images or a folder of scanned documents uploaded simultaneously for bulk processing. The image quality requirements are designed for real-world conditions — moderate lighting variation, slight angle, and minor focus imperfection are handled by the processing step rather than requiring re-capture.
Mobile App Image Processing
AI Pre-Processing — Orientation, Enhancement, and Layout Analysis
Before extraction begins, StrevAI pre-processes the captured image: correcting orientation, enhancing contrast and sharpness for OCR accuracy, identifying the document type and layout structure, and locating the text regions within the image. This processing step is what enables accurate extraction from real-world image conditions — photographs taken at an angle, documents with varying print quality, or multi-section layouts where fields appear in non-standard positions. The processing happens automatically, typically in under two seconds, before extraction begins.
StrevAI AI Extraction
OCR and Field Recognition — Structured Data From Unstructured Image Content
With the image pre-processed, StrevAI applies OCR to convert visible text to machine-readable characters, then applies field recognition to identify which text values correspond to which operational data fields. For an asset label, this means recognising that "S/N: XK-7741" is a serial number and "Mfg: 2022-04" is a manufacturing date. For an invoice, it means identifying line items, quantities, unit costs, and supplier details from the invoice structure. The extraction model is trained specifically on asset management document types — it understands the semantic meaning of extracted text in the context of operational records, not just the character sequence.
AI Metadata Extraction Human Review
Extracted Fields Presented for Verification — Low-Confidence Values Flagged
After extraction, Strev presents the extracted field values in a review interface — showing the source image alongside the extracted data with each field's confidence score. High-confidence extractions appear ready to confirm. Low-confidence extractions — where the OCR encountered ambiguous characters, partial text, or unusual formatting — are highlighted for human attention with the problematic region of the source image indicated. The reviewer corrects any flagged values and confirms the complete extraction. This human review gate is what ensures extraction accuracy in the presence of real-world image variation — the AI provides speed, the human provides the quality validation.
Document Storage Record Creation
Confirmed Extractions Create or Update Operational Records — Instantly
When the reviewer confirms the extraction, Strev creates or updates the relevant operational record from the confirmed field values. A confirmed asset extraction creates a complete asset record — all standard and custom fields populated from the extracted data, the source image attached as the asset's documentation. A confirmed warranty extraction creates the warranty record linked to the asset and configures the renewal alert from the extracted expiry date. A confirmed invoice extraction creates individual asset records for each line item. The record is live in the Strev platform immediately — searchable, linkable, and available for all downstream operational processes.
Asset Management Downstream Activation
New Records Connect to Workflows, Alerts, and Operational Intelligence — Immediately
Records created from image data extraction are fully operational from the moment of creation — they participate in all Strev platform capabilities immediately. An asset record created from a label extraction is searchable, QR-linkable, and visible in the asset management dashboard. A warranty record created from a certificate extraction activates the renewal alert workflow. A contract record created from a scanned agreement initiates the approval workflow. Extracted records aren't data entry outputs — they're fully active operational records that trigger the same downstream workflows as manually created records from the moment they're confirmed.
Workflow Automation Turn Every Document Photo Into a Complete Operational Record — Without Typing a Single Field.
Strev's AI image data extraction reads any asset label, invoice, certificate, or document image and creates the complete operational record automatically — with human review to ensure accuracy before any record is created or modified.
OCR & ICR — printed text and handwriting from any image
Mobile camera or desktop upload — any image source
Human review gate — AI speed with human accuracy control
StrevAI
AI-Powered Image Data Extraction — Domain-Specific Intelligence for Asset Management Documents
Generic OCR software reads text. Strev's AI image data extraction understands what the text means in the context of asset management operations — extracting the right fields into the right record types, connecting extracted data to the operational workflows that depend on it, and learning from the corrections your team makes to improve extraction accuracy over time.
Powered by StrevAI
The difference between reading a document and understanding what it means
Generic OCR converts image pixels to characters. StrevAI's image data extraction model goes further — understanding document structure, identifying field semantics in the context of operational records, mapping extracted values to the correct record fields, and flagging confidence levels that guide human review to the values most likely to need attention. OCR as a foundation. Asset management intelligence as the layer on top.
Asset Management Domain Training
StrevAI's extraction model is trained specifically on asset management document patterns — it knows that "S/N:" precedes a serial number, that a date range after "Warranty Period:" defines coverage, that a line item quantity on a purchase order maps to individual asset records. Domain-specific extraction that understands context, not just characters.
AI Metadata Extraction Confidence Scoring and Learning
Every extracted field carries a confidence score — the AI's assessment of extraction certainty for that specific value. Low-confidence fields are flagged for human review. Corrections made during review are used to improve extraction accuracy for similar documents in future passes — the extraction model improves as your organisation processes more documents.
StrevAI Document Type Recognition
StrevAI identifies the type of document being processed from its image — asset label, purchase invoice, warranty certificate, compliance certification, inspection report, or contract document — and applies the appropriate extraction model for that document type. No manual template selection required before processing begins.
Document Storage Natural Language Extracted Data Queries
Once records are created from image extraction, their data is fully queryable via StrevAI's natural language interface — "Show me all assets whose serial numbers were extracted from the Q2 Dell invoice" or "Which warranty records are approaching expiry from certificates photographed this year?" Extracted data fully integrated into the operational intelligence layer.
StrevAI Industry Use Cases
AI Image Data Extraction for Every Industry That Manages Documents and Physical Assets
Every industry that receives physical equipment, processes procurement documents, maintains compliance certifications, or manages operational archives generates documents that contain valuable data locked in image format. Strev's AI image data extraction unlocks that data for operational use.
Manufacturing & Procurement
Invoice and Equipment Label Image Data Extraction for Manufacturing Asset Onboarding and Procurement Processing
Manufacturing and procurement operations receive large equipment deliveries with multiple items per order — each with its own label, datasheet, and warranty certificate. Processing these manually creates the data entry backlog that delays asset activation, maintenance scheduling, and warranty monitoring. Strev's AI image data extraction processes each delivery document and label image at the point of receipt — creating complete asset records for every item in the delivery, with warranty alerts configured from the extracted certificates, before the equipment leaves the goods receiving area.
Delivery invoice extraction — individual asset records per line item from one image
Equipment nameplate and label extraction at goods receipt — mobile camera capture
Warranty certificate extraction — coverage and expiry configured from photographed certificates
Batch processing for large equipment deliveries — multiple items without multiple processes
Asset Onboarding Time
90% faster
AI extraction from delivery docs vs manual entry per asset
Processing Location
Goods receipt
Records created at delivery — no office data entry backlog
Warranty Registration
Immediate
Alerts configured from extracted certificate at point of receipt
IT & MSPs
IT Asset Label and Hardware Documentation Image Data Extraction for ITAM and MSP Fleet Onboarding
IT teams and MSPs onboard new hardware through regular procurement cycles — laptops, servers, networking equipment, and peripherals each arriving with labels, invoices, and support documentation. Manual ITAM record creation for each device is the bottleneck that delays deployment, delays warranty registration, and delays licence assignment. Strev's AI image data extraction creates complete ITAM records from hardware labels and purchase documentation at the point of receipt — device serial number, model, specification, warranty period, and vendor details all extracted without manual entry.
Laptop, server, and device label extraction — serial number, model, and spec captured from photograph
Hardware purchase invoice extraction — individual ITAM records per device from one document
Support contract image extraction — warranty and support terms linked to device records
MSPs: device onboarding across all client environments from one extraction workflow
Device Onboarding
At point of receipt
ITAM record from label photo — no workstation required
Data Entry
Zero per device
Serial, model, and warranty extracted — none typed manually
Fleet Refresh Speed
Significantly faster
Batch invoice processing creates all device records simultaneously
Healthcare & Compliance
Medical Device and Compliance Document Image Data Extraction for Healthcare Asset Registration and Regulatory Compliance
Healthcare organisations receive medical devices with extensive documentation requirements — device labels containing regulatory classification data, calibration certificates with expiry dates, IFU references, and compliance declarations with scope and issuing authority. Processing this documentation manually for every device creates the data entry burden that results in incomplete CMMS records and compliance documents that are filed without their data being captured in the system. Strev's AI image data extraction processes medical device documentation at the point of receipt or discovery — creating complete, compliance-ready asset records from the photograph.
Medical device label extraction — device class, UDI, lot number, and expiry from label photo
Calibration certificate extraction — scope, issue date, and expiry with automatic alert configuration
Compliance declaration extraction — issuing authority and certification scope captured from image
Archive digitisation — historical device records extracted from scanned documentation
Registration Point
Point of receipt
Device records complete before leaving goods receiving
Compliance Data
Captured from cert
Certification scope and expiry extracted — not manually re-entered
Archive Processing
Batch capable
Historical scanned records processed in bulk extraction runs
Facilities & Field Operations
Field Document and Equipment Data Image Extraction for Facilities Management and Operational Archive Digitisation
Facilities teams and field operations encounter documents and equipment labels in environments where returning to a workstation to enter data is impractical — plant rooms, building roofs, basement infrastructure, and remote sites. Strev's AI image data extraction on mobile gives field teams the ability to photograph any equipment label or document and have the data extracted and the record created or updated in Strev before they move to the next item. For facilities teams with historical paper archives, batch image extraction processes existing document folders into searchable, operational Strev records.
Equipment nameplate extraction in the field — mobile camera, no workstation required
Inspection certificate and compliance document extraction from photographs
Historical archive digitisation — paper records extracted into searchable Strev records
Offline capture — images photographed offline, processed when connectivity returns
Processing Location
In the field
Mobile camera extraction anywhere — no workstation trip
Archive Conversion
Batch capable
Paper folders processed into searchable records
Connectivity Required
Not always
Offline capture, processed on reconnect
Connected Extraction
Extracted Data That Flows Into Every Operational Module
Records created from image data extraction are immediately active across every Strev module — triggering workflows, configuring alerts, and populating the analytics platform with the complete data that makes every downstream capability more effective.
Asset Management
Extracted asset records immediately searchable, QR-linkable, and active in the platform
Warranty Management
Warranty data extracted from certificates activates renewal alerts automatically
Contract Management
Contract terms extracted from scanned agreements initiate approval workflows
Operational Alerts
Extracted expiry dates configure monitoring and alert schedules from day one
Reporting & Analytics
Extracted records immediately included in operational reports and analytics
Security & Privacy
Image Processing Security — Your Documents Processed Within Your Security Boundary
Document images submitted for AI data extraction often contain commercially sensitive and legally significant information — pricing, contract terms, personal data, and compliance credentials. Strev's image processing architecture is designed to handle this with the security standards that enterprise and regulated-industry operations require.
Encrypted Image Transmission
All images submitted for extraction are transmitted encrypted in transit — document contents are protected during the transmission and processing steps.
Human Review Before Record Creation
No image extraction creates or modifies any record without explicit human confirmation. The human review gate is not optional — it's the process requirement for every extraction.
Extraction Audit Trail
Every extraction event — image submitted, fields extracted, review completed, record created — logged with user, timestamp, and source image reference. Complete traceability for every record created from image extraction.
Isolated Model Processing
Document images processed within your Strev environment boundary — image content is not used to train shared models or exposed to processing workflows that serve other organisations.
Role-Based Extraction Access
Extraction capability governed by role — control which team members can submit images for extraction and which record types their extractions can create or modify.
Why Switch
Still Entering Asset and Document Data Manually From Physical Records?
Manual data entry from physical documents is the operational tax that every enterprise pays on every piece of equipment it receives, every certificate it files, and every document it processes. AI image data extraction eliminates that tax — replacing the slow, error-prone, field-disconnected manual process with a camera-based extraction workflow that creates complete records in seconds.
| Capability | Manual Entry & Generic OCR Tools | Strev AI Image Data Extraction |
|---|---|---|
| Processing speed | ✗ Minutes per document — hours for large deliveries | ✓ Seconds per image — immediate field extraction and review |
| Field recognition | ✗ Raw text output — no understanding of which field is which | ✓ Domain-specific field mapping — serial number, warranty, cost mapped to correct record fields |
| Handwriting support | ✗ Printed text only — handwritten fields require manual entry | ✓ ICR handwriting recognition — mixed printed and handwritten documents handled |
| Mobile field capture | ✗ Workstation required — field teams photograph and process later | ✓ Mobile camera extraction — records created in the field at point of capture |
| Record creation | ✗ Separate step — extracted text pasted into forms manually | ✓ Automatic record creation after human review confirmation |
| Downstream activation | ✗ Static — extracted data sits in the tool, not the operational platform | ✓ Extracted records immediately active in alerts, workflows, and analytics |
| Batch processing | ✗ Sequential — each document a separate manual process | ✓ Multi-image batch extraction — entire deliveries processed in one workflow |
| Learning from corrections | ✗ Static extraction — same errors recurring across similar documents | ✓ Confidence scoring improves from review corrections over time |
What operations teams gain when they move to Strev's AI image data extraction
✓Asset onboarding time collapses — entire procurement deliveries processed in minutes instead of days of manual data entry
✓Transcription errors are eliminated — extracted values reviewed against the source image, not compared against a handwritten note from memory
✓Field teams process documents where they find them — mobile camera extraction creates records in the field without office follow-up
✓Extracted records are operationally active immediately — warranty alerts, maintenance schedules, and compliance monitoring configured from the moment of confirmation
FAQ
Frequently Asked Questions About AI Image Data Extraction
AI image data extraction combines optical character recognition (OCR) — which converts image pixels to machine-readable text — with intelligent field recognition that understands what extracted text means in the context of specific operational records. Standard OCR software outputs raw text from an image: a string of characters that still requires a human to identify which characters represent the serial number, which the purchase date, and which the warranty period. Strev's AI image data extraction goes further — recognising document structure, identifying field labels and their associated values, mapping extracted values to the correct record fields (serial number to the Serial Number field, warranty expiry to the Warranty End Date field), and presenting a complete pre-populated record for human review rather than a raw text block for manual re-entry. The difference is between reading a document and understanding it — OCR does the former, AI image data extraction does both.
Strev's AI image data extraction processes the full range of documents that enterprise asset and operational management generates: asset labels and equipment nameplates (serial numbers, model numbers, manufacturing dates, specifications); purchase invoices and delivery notes (line items, quantities, unit costs, supplier details, purchase order numbers); warranty certificates (coverage period, covered components, terms and conditions, vendor contact details); compliance and certification documents (certificate scope, issuing authority, issue and expiry dates, certification reference numbers); contract documents (vendor, contract value, effective dates, renewal terms, notice period, key obligations); inspection reports and calibration certificates (inspection scope, findings, calibration readings, next inspection date); and general operational documents such as supplier qualification forms, insurance certificates, and compliance declarations. Both printed and handwritten content is supported through OCR and ICR processing respectively.
Extraction accuracy in Strev depends on image quality, document type, and print legibility — high-quality images of clearly printed standard documents achieve very high field extraction accuracy. For real-world field conditions — photographs taken at slight angles, documents with variable print quality, or images with partial shadows — the accuracy is lower but is managed through the confidence scoring and human review system. Every extracted field carries a confidence score. High-confidence extractions appear ready for confirmation. Low-confidence extractions are highlighted in the review interface with the corresponding region of the source image displayed for reference — so the reviewer can directly compare the extracted value against the source to verify or correct it. This review gate ensures that the records created from image extraction are as accurate as human-reviewed records, regardless of the initial extraction confidence level. Over time, corrections made during review improve the extraction model's accuracy for similar documents in future processing runs.
Yes. Strev's AI image data extraction supports batch processing of scanned document images — making it suitable for digitising historical paper archives and creating operational records from documents that were previously filed without their data being captured in a structured system. Historical maintenance records, inspection certificates, procurement invoices, and compliance documentation can be batch-uploaded as image files or scanned PDFs, processed by the extraction engine, reviewed by the relevant team member, and confirmed into Strev as structured operational records. For organisations migrating from paper-based or legacy systems, this archive digitisation capability accelerates the migration by eliminating the manual re-entry that would otherwise be required to create records from historical documentation. The extracted historical records become immediately searchable, filterable, and linkable to the asset records they belong to once confirmed.
In the Strev mobile app, AI image data extraction is accessed through the camera function within any supported record creation or update flow. A field team member photographing a delivery label opens the asset creation screen, activates the camera extraction mode, photographs the label, and the AI processes the image and returns the extracted fields for review — all within the mobile interface. The review interface on mobile shows the source image alongside the extracted values, with low-confidence fields highlighted for attention. The team member verifies the extraction, corrects any flagged values, and confirms — the asset record is created in Strev immediately. For areas with limited connectivity, images can be captured offline and queued for extraction processing when connectivity returns — the local capture is preserved on the device, and the extraction and record creation happen automatically on reconnect. No scanner hardware is required — the mobile device camera is the capture tool.
Records created from image data extraction are fully integrated into the Strev platform from the moment they're confirmed — they're not output files or extraction results, they're live operational records. An asset record created from a delivery label extraction is immediately searchable in the asset register, assignable to a QR code, linkable to a maintenance schedule, and visible in the operations dashboard. A warranty record created from a photographed certificate immediately configures the renewal alert and begins monitoring from the extracted expiry date. A contract record created from a scanned agreement immediately initiates the configured approval workflow. Operational alerts configured from extracted expiry dates fire through the standard alert delivery channels. The extracted records also populate the reporting and analytics platform — their data is immediately included in utilisation reports, maintenance analytics, and compliance dashboards. Image data extraction is not a standalone data capture tool — it's the intake channel that connects physical documents to the full Strev operational platform.
Document images submitted to Strev's extraction system are processed within the Strev security boundary — images are transmitted encrypted in transit and processed in isolation from other organisations' extraction workflows. The content of your documents is not used to train shared extraction models that other Strev customers benefit from, and image data is not retained beyond the processing and review session in a form that could be accessed outside your Strev environment. The human review gate additionally ensures that no record is created from extracted image content without explicit authorisation from a user with the appropriate permissions — the AI extracts the data, the authorised user confirms it, and the system records it. Every extraction event is logged in the audit trail with user, timestamp, and source image reference. For industries with specific data handling requirements — healthcare (GDPR, patient data adjacent documents), financial services (FCA regulated information), or legal (privilege-sensitive contracts) — the extraction process is compliant with the same data governance standards that apply to all data in the Strev platform.
Resources
Deeper Reading on AI Image Data Extraction and Document Processing Automation
Practical guides for IT managers, operations directors, procurement teams, and compliance leads on eliminating manual data entry from asset onboarding and document management workflows.
Image Data Extraction
How AI Image Data Extraction Eliminates the Asset Onboarding Bottleneck That Delays Every Large Equipment Delivery
Read more
OCR & Intelligent Processing
OCR vs AI Image Data Extraction: Why Generic Text Recognition Is Not the Same as Operational Document Intelligence
Read more
AI Metadata Extraction
AI Metadata Extraction: How Strev Creates Complete Asset Records From Procurement Documents Without Manual Data Entry
Read more
Get Started
Turn Every Document Photo Into a Complete Operational Record — In Seconds, Not Hours.
Join manufacturing, IT, healthcare, facilities, and field operations teams who have replaced manual data entry from physical documents with Strev's AI image data extraction — and eliminated the backlog, the transcription errors, and the disconnect between physical documents and the digital records that should reflect them.
OCR & ICR — printed text and handwriting from any image
Mobile camera or upload — any document, any location
Human review gate — accuracy controlled before record creation
Immediately active — extracted records trigger alerts and workflows