Compliance officers manually review KYC packages for dozens of clients: checking all required documents, expiry dates, and regulatory compliance.
Incoming emails, requests, and documents from clients all land with one manager, who sorts, forwards, and prioritizes them manually.
The logistics team processes dozens of waybills a day: data is transferred from paper into the system manually, and errors are only caught during reconciliation, not right away.
An accountant spends 2–3 hours a day manually entering invoice data into the system: vendor, amount, date, details.
A lawyer or manager checks every contract by hand: whether required clauses are present, whether prohibited terms are absent, whether amounts match across documents.
You want to automate document processing but aren't sure whether your document volume and quality are sufficient, or how to handle edge cases.
The system runs on a Human-in-the-Loop principle. If AI recognizes a document with high confidence, it's processed automatically. If the confidence falls below a set threshold, the document goes into a queue for human review. This data feeds back into training, so recognition accuracy improves over time.
Before implementation, we recommend an AI Feasibility Sprint to test on your real documents whether your data quality can reach the accuracy you need.
Supports all document formats: PDF, JPEG, PNG, DOCX, email. For low-quality scans and photos, OCR preprocessing (AWS Textract or Google Document AI) converts the image to text before AI processing. OCR quality affects the accuracy of the result, so assessing scan quality is part of the initial audit.
An LLM extracts structured data (GPT-4o, Claude, or an open-source model if your data can't leave your servers due to compliance or GDPR requirements).
Validation rules: automatically check formats, dates, and amounts, plus cross-check related documents. Every result comes with a confidence score showing how certain the system is.
Processed results → Spiro, your CRM, or any other system via API or webhook. Documents below the accuracy threshold → a Human-in-the-Loop queue instead of automatic acceptance.
Important: connecting to standard systems via REST API is included in the base scope. Custom integration with SAP or a specific legacy system is quoted separately after an audit of your systems. The complexity of such integrations can vary significantly depending on version and configuration.
Exception queue: when AI isn't confident in a result, the document goes to a person for a final decision. Feedback loop: human corrections are logged and used to improve the system's accuracy. Analytics by exception type shows which documents most often need review, and where improvements are needed.
Included in Setup: AI Feasibility Sprint, pipeline configuration for your documents, training on your archive, integration with standard systems via API, Human-in-the-Loop queue setup, team training.
Not included in Setup, quoted separately: custom integration with SAP or a legacy system, infrastructure costs (cloud model access or deploying an open-source model on your server).
Includes: accuracy monitoring and degradation detection, prompt updates when document formats or regulatory requirements change, incident response and recovery, monthly metrics report, iterative improvement based on Human-in-the-Loop feedback.
Why Ongoing matters: document formats and regulatory requirements change, and new model versions come out. Without support, system accuracy gradually declines.
Situation: a compliance officer manually reviewed KYC packages for 50+ new clients a week, spending 20–40 minutes per package.
What we did: AI checks a package in seconds and generates a report listing present, missing, expired, and non-compliant documents. A person only reviews exceptions.
Result: review time per package dropped to 3 minutes. The number of clients processed per business day doubled.
Situation: an accountant spent 2.5 hours a day entering data from 80–120 incoming invoices in various formats into the system. Errors were only caught at payment time.
What we did: PDF and scanned invoices → OCR → LLM-based structured field extraction → validation → entry into the accounting system. Invoices with uncertain fields go into a human review queue.
Result: 20 minutes reviewing exceptions instead of 2.5 hours of manual entry. Over 95% accuracy on standard formats and 88% on non-standard ones. Errors in invoice data dropped by 94%.
Situation: over 150 incoming letters and documents a day were sorted and forwarded manually; urgent requests had no priority over standard ones.
What we did: automatic document type detection with routing to the responsible lawyer. Court documents get priority and a notification, standard ones go straight into the ticketing system.
Result: 3 hours of manual sorting dropped to 15 minutes reviewing complex cases.
There's a Human-in-the-Loop architecture built in for exactly this: documents where the AI's confidence score falls below a set threshold automatically go into a queue for human review, instead of being processed autonomously. The confidence threshold is adjusted to your needs: conservative means more reviews, aggressive means fewer reviews and higher system autonomy.
