50%
of B2B invoices worldwide will be processed without manual intervention by 2025.
Gartner, 2024
Intelligent Automation
Documents and emails, handled in the background.
Invoices, contracts, claims, support tickets, inbound emails. Extracted, classified, routed and answered by AI pipelines that combine OCR, LLMs and your business rules. Ops volume becomes a background task.
The work humans hate, done by a system you can audit.
Document processing is the kind of work that scales backwards. Every new customer adds a few more invoices, contracts and emails, and the only way to keep up is to hire more people to read them. We build pipelines that take that work off the team. OCR and LLM extraction on the messy real-world documents you actually receive. Classification, routing and response drafting. Audit trails for every decision. Confidence-based escalation so humans handle the calls that need judgement, and only those.
Where document automation lands first
High-volume document work where the cost of a missed exception is real and the ROI of automation is measurable in months.
Finance & accounting
- Accounts payable and three-way invoice matching
- Expense report classification and policy checking
- Bank statement and reconciliation pipelines
- Vendor onboarding and W-9 / KYB handling
Why Codino
- 10+ years shipping production document systems for regulated industries.
- One team owns the whole stack. Extraction, classification, routing, response, audit trails and human-in-the-loop.
- EU-based with EU data residency and GDPR-compliant delivery by default.
- Evaluation-first. Every pipeline ships with golden datasets, accuracy targets and live monitoring.
- Tool-agnostic. OCR, IDP platforms and LLMs chosen for fit, not for partnership.
- Phased rollouts. Human-review-first until accuracy is proven, then expanding autonomy where the data supports it.
By the numbers
Document processing is the back-office bottleneck, and it is ready to be automated.
The market data is clear. The ROI is measurable. The question is which document workflow goes first.
62%
reduction in invoice processing time after AP automation, from 20.8 days to 7.9 days on average.
IDP Market Report, 2025
€2–4
mature cost per automated invoice versus €12 to €15 manually. Over 80% reduction.
IDP Market Report, 2025
88%
of financial institutions are putting document automation first on their 2025 digital roadmap.
IDP Market Report, 2025
Calculate the impact
Plug in your document volume, see the savings.
A rough but honest estimate of what document automation could save you. Drag the sliders to your situation. The math and assumptions are listed below.
Monthly savings
€54,000
Annual savings
€648,000
FTE-equivalents freed
7.2
Assumptions and methodology
- Mature automated cost per document set at €3, mid-point of the €2 to €4 industry benchmark.
- "Automatable share" is the fraction of documents the pipeline can handle straight-through. The rest go to human review.
- FTE-equivalent uses a fully-loaded cost of €7,500 per FTE-month (≈ €90k annually).
- Excludes one-time delivery cost, model/OCR cost and integration effort. These are scoped per engagement.
- Savings are gross. We help you measure the realised net during phased rollout.
What you get
A complete document automation stack: extraction, classification, response, audit and the operational discipline to run it safely.
Extraction that handles messy real-world documents
OCR plus LLM-based field extraction on the documents you actually receive: non-standard layouts, handwritten notes, scanned PDFs, photographs. Not just clean templates.
Classification and routing tuned to your process
Custom taxonomies, confidence-based routing and escalation paths. Each document type lands where it should. The ones the system is not sure about land with a human.
Email and inbox automation
Inbox intake, intent classification, drafted responses and human review queues. Triage at the volume your team cannot physically handle.
Audit trails for every decision
Source document, extracted fields, classification reasoning, escalation path, human override. All logged for every action. Compliance reviews pass the first time.
Integration with the systems you already use
ERPs (SAP, NetSuite, Oracle), CRMs, ticketing, document management, accounting. The pipeline output lands in the system your team is already in.
Evaluation harness that catches drift
Golden datasets, accuracy targets per document type, live monitoring. When extraction quality moves, you know before users do.
Selected work
Recent projects where document and email pipelines replaced hours of manual processing.
ZipZero
ZipZero lets users collect funds from their purchases to pay monthly household bills. Backed by AI that scans receipts and recognises products, it lets users earn from their own shopping data.
View Case Study
Insights
Web-based tool that simplifies and accelerates airline research and analysis activities. Additionally, its risk tracking capabilities simplify risk management, auditing and collaboration in order to find additional value for the airline.
View Case StudyWhat Clients say about us
Marcin Walaszczyk
CTO
"The expertise of the leaders, coupled with the diverse skill sets of their teams, truly sets them apart. Their vast experience across a myriad of projects ensures that they can adeptly handle virtually any project you envision. Furthermore, their deep involvement in the process is palpable; it's as if they seamlessly integrate and become an intrinsic part of your in-house development team."
ZipZero
How we deliver
From document audit to a running pipeline. Usually in weeks, phased so each phase ships measurable value.
- 01
Discover
Document workshop with your team. Sample documents, current process, error rates, exception types. We pick the highest-ROI workflow to start with.
- 02
Prototype
Narrow pipeline wired all the way through on real documents. Extraction, classification, routing, human-review queue. You see the accuracy numbers on your own data, not a vendor benchmark.
- 03
Productionise
Audit trails, evaluation harness, integration with your downstream systems, monitoring and alerting. The pipeline runs unattended on a slice of real traffic.
- 04
Scale
Roll out to more document types and workflows. Ingest human corrections into the evaluation loop. Expand autonomy as accuracy data accumulates.
What changes when documents handle themselves
Talk about your documentsYour ops team stops drowning in paperwork
Routine documents flow through the pipeline. Your people only see the ones that actually need judgement.
Processing time drops from days to minutes
Inbound document SLAs improve measurably. Customers and partners feel the difference, not just the back office.
Costs scale flat, not linear with volume
You can double the customer base without doubling the document-processing team.
Audit and compliance pass without firefighting
Every decision is logged, traceable and explainable. Reviews stop being a six-week project.
How we engage
Our way of working with you
What we actually do on every engagement, regardless of stack, model or scope.
- 01Map the real documents
We start with your documents, not our toolkit.
Workshop with your team to map document types, volumes, exception patterns and downstream systems. You leave with a prioritised backlog ranked by ROI.
- 02Define "good" upfront
Accuracy targets are measurable before we build.
We agree on per-field and per-document-type accuracy targets, escalation thresholds and cost-per-document budgets. So at handoff we can prove value, not argue about it.
- 03Work alongside
Senior engineers in your team, not a separate squad.
We work next to your ops people, your domain experts and your engineers. The pipeline reflects the process they actually run.
- 04Ship a narrow pipeline fast
First workflow to production in 3 to 5 weeks.
First workflow wired all the way through on real documents. You see whether the value is real before scaling.
- 05Plan our exit from day one
Your team owns it the day we leave.
Documentation, runbooks, evaluation harness and monitoring. Built so analysts and engineers can run the pipeline without us.
Document automation, explained
Most of them are, and that is where traditional OCR/template approaches fail. We use LLM-based field extraction that handles layout variation, handwritten notes and scanned PDFs. Accuracy targets are tuned per document type, not assumed across the board.
Depends on the document type. For clean structured invoices, 95 to 99% straight-through. For messy non-standard documents, lower extraction confidence routes to human review. The pipeline is honest about uncertainty rather than guessing.
Yes, and we recommend it, especially early. Confidence-based routing means humans handle low-confidence cases and edge cases. Autonomy expands over time as the evaluation data supports it.
We pick what fits the engagement. Hyperscience, Rossum, Docsumo, AWS Textract, Google Document AI, Azure Form Recognizer, plus custom LLM pipelines on Claude, GPT and open-source models. Cost, accuracy and integration drive the choice.
EU-region deployments by default, lineage and audit trails for every decision, PII detection and redaction where required, GDPR-compliant data handling. Reviews pass the first time.
Let's Talk About Your Project
Get In Touch
Maciej Roman|CEO & Co-founder