Do I need a proper data warehouse before I can start?

No. The useful starting point is structured data from the documents you already handle every day — invoices, receipts, contracts — landing in whatever database or accounting system you already run. A warehouse is an optimization you add when volume justifies it, not a prerequisite for getting value.

What is RAG, and what does it have to do with OCR?

RAG (retrieval-augmented generation) is the standard technique for making an AI assistant answer from your data: before responding, the system retrieves relevant records from your own store and feeds them to the model as context. Retrieval only works on machine-readable data — which means documents must have gone through OCR and extraction first. No extraction, nothing to retrieve; nothing to retrieve, RAG degrades back to guessing.

How does extracted data get into my internal systems?

Through a REST API and signed webhooks — your systems receive structured fields per document and can verify each delivery's signature. For accounting specifically, Inferio also syncs directly to freee and MoneyForward via OAuth, and for Japanese compliance it validates invoice T-numbers against the NTA public API, handles the 8%/10% tax split, and supports the 7-year retention required by the Electronic Bookkeeping Law.

Which document type should I start with?

The one you have the most of — for most companies that's invoices or receipts. High volume means the extraction pays for itself fastest and the resulting dataset becomes useful soonest; contracts and other lower-volume documents can join the pipeline once the core loop is running.

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AI strategy

Why AI is only as accurate as your data (and why OCR comes first)

July 8, 2026 · 6 min read

An AI model is only accurate about your business when it can see your business's data. A general-purpose model knows nothing about your vendors, your contracts, or last month's spend — that knowledge lives in your invoices, receipts, and agreements, most of it locked in paper and PDFs. Building the data foundation that makes AI useful therefore starts with one unglamorous step: extracting structured data from documents. That step is OCR.

Why does AI answer wrong about your own business?

A large language model (LLM) — the kind of AI behind every modern assistant — can only work with two sources of information: what was in its training data, and what you put in front of it as context. Your company's internal records are in neither. Ask an assistant "how much did we spend with vendor X last month?" and it has exactly two options: admit it doesn't know, or make something up. Both look like "AI being inaccurate," but the model isn't broken — it was never given the data.

The old data-engineering rule "garbage in, garbage out" has a stricter modern sibling: no data in, no answer out. If last month's vendor invoices exist only as a stack of paper in a filing cabinet, or as scanned PDFs in a shared folder, no amount of model quality fixes the answer. The bottleneck of AI accuracy in a business setting is almost never the model — it's whether the business's own facts exist anywhere a machine can read them.

What does a "business data foundation" actually mean?

It doesn't mean a multi-year data-lake project. For most companies, a working data foundation starts with something much smaller: structured data — information broken into named, typed fields that software can query. An invoice as a photo is a dead artifact; the same invoice as a database row with vendor, issue date, line items, tax rate, and total is a fact your systems can use.

Once document data is structured, everything downstream becomes possible: analytics can sum spend by vendor and by quarter, reconciliation can match invoices against payments, and an AI assistant can retrieve the exact rows it needs to answer a question truthfully. None of those consumers can work from a folder of image files. Structured data is the difference between "we have the documents somewhere" and "we can answer questions about our business."

Why is OCR the first step of every AI strategy?

Most of a business's operational knowledge is not in databases — it arrives as documents: paper invoices, scanned PDFs, phone photos of receipts, signed contracts. An AI system cannot read a filing cabinet. Before any analytics layer, retrieval system, or assistant can use that knowledge, someone or something has to convert documents into data. That conversion step is OCR — and in a modern pipeline it's AI OCR, where a vision-language model reads the document in context and returns named fields rather than a wall of raw text.

This is why OCR sits at the base of the stack rather than being a side feature: it is the ingestion layer. Skip it, and every AI layer above it is starved — the assistant has no facts to retrieve, the dashboard has no numbers to chart, the reconciliation job has nothing to match. Companies that treat document extraction as step one of their AI strategy end up with an accumulating asset; companies that jump straight to "add an AI chatbot" end up with a fluent interface to an empty room.

How does the data-to-AI loop work in practice?

The working loop has four stages. First, documents come in — JPG, PNG, or WebP images, and multi-page PDFs or TIFFs. Second, an extraction engine turns each one into structured fields, attaching a confidence score to every field; anything below a review threshold (Inferio's default is 0.75) routes to a human reviewer in a correction UI, so low-confidence values are fixed by a person instead of silently entering your records. Third, the clean result lands in your data store — via REST API and signed webhooks, or synced directly into accounting tools like freee and MoneyForward. Fourth, that store feeds the consumers: reports, reconciliation, and AI assistants that can finally answer questions about your company with your company's numbers.

The loop compounds. With three months of structured invoices you can answer "what did we spend last month?" With two years, you can answer "which vendors raised prices above inflation?" and "what does our Q4 spend spike look like across years?" Every document processed makes the next question answerable — which is the practical meaning of a data foundation: not a big-bang project, but an asset that grows one document at a time.

Quick answers

Do I need a proper data warehouse before I can start?: No. The useful starting point is structured data from the documents you already handle every day — invoices, receipts, contracts — landing in whatever database or accounting system you already run. A warehouse is an optimization you add when volume justifies it, not a prerequisite for getting value.
What is RAG, and what does it have to do with OCR?: RAG (retrieval-augmented generation) is the standard technique for making an AI assistant answer from your data: before responding, the system retrieves relevant records from your own store and feeds them to the model as context. Retrieval only works on machine-readable data — which means documents must have gone through OCR and extraction first. No extraction, nothing to retrieve; nothing to retrieve, RAG degrades back to guessing.
How does extracted data get into my internal systems?: Through a REST API and signed webhooks — your systems receive structured fields per document and can verify each delivery's signature. For accounting specifically, Inferio also syncs directly to freee and MoneyForward via OAuth, and for Japanese compliance it validates invoice T-numbers against the NTA public API, handles the 8%/10% tax split, and supports the 7-year retention required by the Electronic Bookkeeping Law.
Which document type should I start with?: The one you have the most of — for most companies that's invoices or receipts. High volume means the extraction pays for itself fastest and the resulting dataset becomes useful soonest; contracts and other lower-volume documents can join the pipeline once the core loop is running.

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