Light integrates AI throughout the platform to automate financial workflows, reduce manual data entry, and provide intelligent insights. This article explains where AI is applied and what capabilities...
Last updated Jul 22, 2026 · 5 min read
Light leverages a combination of its own proprietary fine-tuned models and advanced third-party foundation models (OpenAI GPT, Google Gemini, and Anthropic Claude via AWS Bedrock) through a unified GenAI framework that provides intelligent automation across multiple financial processes.
When you upload invoices, bills, receipts, or other financial documents, Light's AI-powered parsing engine automatically extracts key financial data. The system uses optical character recognition (OCR) combined with large language models to read document content, even from images and PDFs. Extracted data includes vendor names, invoice amounts, dates, line items, account codes, and tax information.
This eliminates manual typing and reduces the risk of data entry errors. All extracted values remain fully editable, so you can review and correct them before anything is posted.
Mobile receipt uploads are processed by Light's AI engine to automatically capture spending details. The system extracts the vendor name, transaction amount, currency, purchase date, and line items. Light's AI then assigns appropriate expense categories and tax codes based on the merchant type and spending pattern.
The AI parsing module uses GenAI function handlers to extract structured data from uploaded invoices. Beyond basic amounts and dates, the system can identify multiple line items within a single invoice, extract custom fields, and recognize payment terms. This is especially useful for processing bulk invoices from regular vendors.
Light validates and enriches financial data as it enters the system. Uploaded documents that are not valid bills are detected by AI before they enter your payables workflow, and duplicate bills are automatically flagged based on vendor and document number. AI is also used to enrich vendor and bank details from parsed documents.
The bank reconciliation engine is powered partly by AI. Light's AI parses bank transaction descriptions into structured metadata (such as invoice references and counterparty details), and matching rules then use that metadata — together with amounts, dates, and references — to suggest matches. You can also create reconciliation automation rules by describing them in plain language, and AI converts your description into structured rule conditions.
Through Light's Slack integration, you can ask natural language questions about your finances using @Light. Ask questions like "What did we spend on travel last month?" or "Show me payables by vendor." Light processes your question using AI and returns relevant reports and data without requiring you to navigate the UI.
Good to know: The Slack integration processes your questions using the same AI models that power other Light features, maintaining the same security and privacy standards across the platform.
Light uses a modular AI architecture:
The platform routes each request to the model configured by Light for that specific use case, whether that is one of Light's proprietary models or a third-party foundation model.
Light processes financial documents using AI in a secure manner:
Light's AI features run on a combination of models. Light uses data to fine-tune its own proprietary models and LLMs, improving the accuracy of AI-assisted features over time — these fine-tuned models are owned and operated by Light. For some features, Light additionally uses foundation models provided by third-party services (OpenAI, Google, and Anthropic via AWS Bedrock), which remain the property of those providers. Light owns its fine-tuned models along with the application-level logic, integrations, orchestration, and product features built around the platform.
Customer data is never used by third parties for model training or fine-tuning. Data sent to third-party AI providers is used only for runtime inference to deliver AI-assisted features — those providers do not train or fine-tune their models on your data.
Runtime AI processing may use customer-provided data such as:
This data is used to deliver AI-assisted product features such as parsing, prefilling, and summarization, and to fine-tune Light's proprietary models. It is never used by third parties for training or fine-tuning.
Light uses AI in a supporting, non-decision-making capacity. AI outputs are advisory only and do not produce legally or financially binding outcomes without human review. All AI-generated suggestions — from extracted invoice fields to reconciliation matches — are subject to human review and override before they take effect.
Light has assessed its AI functionality as Limited Risk under the EU AI Act. Light's use cases are assistive and non-high-risk, emphasizing transparency, human oversight, security, and accountability.
Light's approach to AI aligns with the OECD AI Principles, emphasizing:
To put it in practical terms, here are some common tasks where AI makes a measurable difference:
Tip: Start by letting AI handle the high-volume, repetitive tasks (receipt processing, invoice data entry) and then expand to reconciliation and reporting as your team gets comfortable with the suggestions.
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