OCR breaks on handwriting, irregular layouts and degraded scans. We build the document-intelligence layer that does the rest — extracting, classifying and validating into schema-mapped records, with field-level confidence, full lineage and a reviewer console for the cases the pipeline isn’t sure about.
We do not ship basic text-matching tools. We build processing engines whose structured outputs are fully auditable by compliance regulators.
Four operational bottlenecks we address continuously. If one of these architectural challenges matches your current data backlog, our collaboration starts with a file ingestion map, not a high-level overview deck.
"Operations is losing days to manual document typing."
Invoices, contracts, claims, compliance packets. The queue is the bottleneck, and the queue costs more every month. We replace the queue with a pipeline that scales to the corpus, not the headcount.
"Critical historical data is locked inside unsearchable records."
The reconciliation problem is a data-extraction problem in disguise. One pipeline, one schema, one audit trail — into your ERP, core-banking or claims system, with field-level confidence on every record.
"Compliance will not sign off on automated extraction models."
Stamps, multi-column layouts, mixed scripts, degraded originals. Actigen handles them at 91%+ accuracy after three months of training — not 60% with a footnote.
"Our documents change templates continuously across partners."
Audit, KYC refresh, ESG disclosure, accreditation evidence. Producing the document in seconds is the difference between passing and failing. We index for retrieval, not just storage.
The production-grade processing stack we deploy to enterprise systems. The raw processing model is a single variable; the long-term value is locked in the system integrations, formatting pipelines, and human-in-the-loop checkpoints.
Automated extraction and semantic processing across complex PDFs, low-contrast microfilms, handwritten records, land deeds, and multi-page corporate agreements. Completely independent of standard template structures.
Bidirectional ingestion pipelines feeding extracted data directly into your CRM, ERP, document vaults, and operational core backends with unalterable audit trails.
Specialised computer vision and text models optimised to read cursive, historical scrawls, and mixed-character formats without flattening specific regional handwriting nuances.
Dynamic structural analysis that accurately maps complex tables, nested text boxes, header variations, and footers across variable multi-page documents.
Continuous metrics monitoring schema alignment, data extraction drift patterns, and human correction trends to optimise overall processing pipeline performance.
Programmatic validation screens built for internal teams to quickly review and verify low-confidence extraction points before data hits down-stream ledgers.
“The extraction engine is a foundational commodity. The document layout mapping, backend business integrations, and auditable human-verification gates are the actual product.”
Talk to an engineerAn active overview of the processing models and pipeline frameworks we deploy into active production—ensuring absolute UK and EU data residency wherever your corporate regulatory frameworks demand data safety.
Advanced layout processing networks, proprietary handwriting recognition modules, and specialised open-weight vision-language models.
Bidirectional, layout-aware pipelines that format unstructured images into validated, schema-compliant JSON structures.
Intuitive, coordinate-matched data validation dashboards designed for internal teams reviewing high-volume documentation.
Hardened system environments deployed directly across your cloud infrastructure or ours, securing strict data isolation boundaries.
Performance returns evaluated exactly how enterprise data directors and operations managers score success—quantified across actual production volumes, following human verification, and bound to upfront data-accuracy goals.
Performance metrics are formally committed to in writing during the initial technical Discovery phase.
Our data automation practice ships reliable enterprise platforms through three connected technical workstreams. Durable document intelligence lives across all three pillars—deeply embedded inside workflow applications, generative at the processing layer, and supported by robust backend data pipelines.
The extraction framework deploys directly within your native application architecture, core records system, or intake queue—not isolated in a separate tab.
Read more / 02Retrieval-anchored, layout-aware, and highly governed ingestion systems. We replace basic OCR scripts with production enterprise software.
Read more / 03Automated feature transformation pipelines, schema evaluation tools, and data drift analytics—the underlying technical plumbing that ensures system durability.
Read moreDocument classes we handle most. Filter by industry to see typical schemas and integration shapes.
Why partner for document automation with an engineering organisation that explicitly builds and supports production data infrastructure.
Every extracted data field we deploy into production explicitly maps back to its original document coordinates. Visual data provenance is turned on directly at the code level.
The core extraction model is simply a modular piece. The long-term value resides within the structural table-parsing, coordinate matching, and core database ingestion workflows—fully schema-mapped and auditable.
We track document processing velocities, field-level data correction frequencies, and net operational cost adjustments—ignoring vanity statistics to protect your project expenditures.
Our teams design specialised pipelines that read centuries of handwritten scripts and fading text types, ensuring accurate historical document extraction where off-the-shelf software fails.
Every single extraction is logged end-to-end: recording the generated value, the source document coordinate, system confidence levels, and human correction changes for complete transparency.
Formally certified across CMMI Level 3, ISO 9001, ISO 27001, and ISO 27701 standards, delivering localised data residency boundaries that align perfectly with regional regulatory requirements.
Engagements where the corpus was hard, the accuracy was measured, and the audit trail was retained. Filter by service or industry to widen the view.
CASE 01
US university research initiative
260 years of fragmented historical maps transformed into a georeferenced spatial database for anthropological and land-use analysis. A prominent US university needed to study the historical evolution of Uxeau, France, across multiple centuries of land ownership, taxation, and agricultural activity. The research depended on digitising and harmonising vintage maps dating back to 1759 — each with different scales, formats, and levels of degradation — into a single spatially accurate GIS environment suitable for comparative analysis. 260+ years of historical mapping digitised and layered. Lambert II precision georeferencing using Esri GIS tools. Multi-era land parcel and feature extraction delivered at scale.
30,000+ CT scans annotated at 98.9% segmentation accuracy for AI-driven radiology models. A US-based MedTech AI company developing radiology models for tumour detection and analysis required clinically precise annotation support to accelerate model training and validation. Existing workflows faced rising costs, limited access to qualified medical annotators and growing compliance pressure around handling sensitive patient imaging data.
40%+ faster foreclosure data processing with 50% higher accuracy across multi-county property records. A US-based mortgage data company managing property intelligence across more than 155 million properties and 3,000+ counties required a scalable operational model for foreclosure data collection and processing. Their existing workflows relied heavily on manual back-office operations, creating delays, inconsistencies and rising operational overhead across fragmented government data sources.
The underlying commercial configuration adjusts to your procurement model; our engineering governance frameworks and data protection standards remain unyielding. Whichever deployment option you prioritise, you receive identical field-level audit trails and system validation workflows.
An integrated, standing engineering pod—comprising machine learning developers, database engineers, backend specialists, and MLOps architects—embedded alongside your group for the lifecycle of the program.
A clearly specified document category or targeted layout classification framework delivered directly to explicit, measurable accuracy and deflection targets.
Expert machine learning specialists and backend developers integrated directly within your internal development sprints—working under your deployment roadmap while enforcing our strict code standards.
Document automation projects routinely break down in production when they are isolated as mere character recognition challenges. True stability requires balancing image preparation, layout structure analysis, and deterministic human validation gates.
Granular document taxonomy mapping, physical and digital data asset screening, and extraction target setting. We baseline exactly what successful performance looks like directly on the page.
Pipeline topology layout, model layer selection, verification interface mapping, and specialised evaluation dataset building. Your absolute compliance posture is locked in on day one.
Agile technical development cycles integrated with an extended shadow-mode operating phase prior to public rollout. The automated system performs silently alongside live human teams before managing traffic autonomously.
Continuous performance tuning loops, automated layout drift tracking, and model optimisation. We ensure your data extraction metrics remain fully auditable and accurate over time.
“An automated document processing system built to parse complex records without granular pixel coordinate tracing is simply a system engineered to obscure data errors.”
Contact us“Eight million specimens, multi-script labels, four centuries of collection. Standard OCR returned 60 percent and a footnote. Actigen returned research-grade accuracy on the corpus, with the lineage on every record. The collection is now searchable for the first time in its history.”Director of Digital Programmes UK national herbarium — IDP — mass digitisation
Not an ambiguous sales pitch. Provide a representative selection of your target documents along with your database profiling parameters, and our engineering architects will deliver a custom extraction strategy within five business days.
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