---
title: kintone and AI — TechJapan LLC
source: https://www.tech-japan.jp/services/kintone/
updated: 2026-09-11
facts: https://www.tech-japan.jp/facts.json
---
# kintone already holds the records. The paper around it does not.

Building kintone apps is the easy half. The half that decides whether anything improves is what happens at the edges: the paper that still gets retyped, the free-text fields nobody can query, and the four apps a person moves data between by hand every morning.

## Four attachment points, in order of payback

*Figure: Where AI attaches to kintone. Paper enters through AI-OCR instead of being retyped. A language model reads free-text fields. An agent moves records between apps on a schedule. Search runs over the kintone data as a knowledge base. kintone itself remains the system of record.*

```text
                  +================================+
  paper  --OCR--> |                                |
                  |      kintone       | --> reports
  email  --LLM--> |   still the system             |
                  |   of record        | --> alerts 
  other  --API--> |                                |
   systems        +====================+            
                      ^                            |
                      |          v                  
                   agent      search                
                   moves      over your own         
                   records    data, as a            
                   nightly    knowledge base        
```

### 1. Paper enters without being retyped

AI-OCR reads delivery notes, inspection sheets, order forms and timecards straight into kintone records. The measurable effect is not the typing time; it is the transcription errors that stop happening, and the fact that the record now exists on the day of the event rather than at month end.

### 2. Free text becomes queryable

Remarks fields hold most of what a business actually knows and none of what it can search. A language model can classify, extract and normalize those fields into structured columns — which is when the data becomes reportable rather than merely stored.

### 3. An agent does the morning routine

The person who copies yesterday’s records between three apps, checks for missing entries and emails a summary is executing a program. Writing it down as one makes it repeatable and auditable, and gives that person their first hour back.

### 4. Search over your own data

Once kintone holds the records, it is also a knowledge base: past cases, decisions, and the reasons behind them. Retrieval over that is subject to everything on the [ontology page](https://www.tech-japan.jp/services/ontology/) — if your terminology is inconsistent, this is the step where you find out.

## What we do

- AI-OCR so paper reaches kintone without retyping
- Generative AI and LLM integration against kintone records
- Data analytics and prediction on the accumulated data
- Agent automation across apps
- Internal knowledge search over kintone data
- External system and API integration
- Standard kintone work: process design, app development, customization, and enablement for the people who will run it

|  |  |
| --- | --- |
| Already on kintone | AI capability can be added incrementally, app by app, without a migration |
| Not on kintone yet | We start from the process review and the adoption decision, including whether kintone is the right answer at all |
| Price | Quoted per scope, from JPY 30,000, tax excluded. One simple app has a fixed price from JPY 50,000 (see the [Japanese menu page](https://www.tech-japan.jp/ja/small-dev/)). First consultation free |

> kintone stays the system of record throughout. Nothing here asks you to move your data somewhere we control.