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Onigiri Tech

Artificial Intelligence

AI that does useful work inside real processes.

Assistants, document intelligence, search and automation that connect to your data and systems — with evaluation, guardrails and humans in the loop where it matters.

Overview

AI & Intelligent Systems

Most AI value comes from narrow, well-defined tasks: reading documents, finding the right information, classifying requests, drafting responses. Not from a general chatbot bolted onto a website.

We identify where AI genuinely reduces work, integrate it with your existing systems and permissions, and measure whether it performs well enough to trust.

  • Generative AI
  • AI Assistants
  • Document Intelligence
  • Intelligent Search
  • NLP
  • AI Automation

Problems we solve

Sound familiar?

  • Knowledge that's hard to find

    Policies, contracts and past work spread across drives, emails and systems nobody can search well.

  • Manual document processing

    People reading invoices, forms and applications to type data into another system.

  • AI pilots that never ship

    Impressive demos that stall because of accuracy, security or integration concerns.

  • High-volume repetitive decisions

    Triage, classification and routing done manually because rules alone can't handle it.

Capabilities

What we build.

  • 01

    Generative AI integration

    LLM features inside your products — drafting, summarizing, extracting and transforming content.

  • 02

    AI assistants

    Assistants grounded in your documents and data, respecting user permissions.

  • 03

    Document intelligence

    Extracting structured data from invoices, forms, contracts and reports, with review workflows.

  • 04

    Intelligent search

    Semantic and hybrid search across internal knowledge, with source citations.

  • 05

    NLP & classification

    Categorizing tickets, emails and requests; detecting intent, sentiment and entities.

  • 06

    Evaluation & guardrails

    Test sets, accuracy tracking, fallbacks and human review for decisions that matter.

Common use cases

Where it usually starts.

  • Internal knowledge assistant

    Staff ask questions and get answers with links to the source policy or document.

  • Invoice & form extraction

    Documents read automatically, validated, and passed to finance or operations systems.

  • Request triage

    Incoming emails and tickets classified and routed to the right team.

  • Report & summary drafting

    First drafts generated from structured data for people to review and finalize.

Process

How we approach artificial intelligence.

  1. 01

    Find the right task

    Identify where AI reduces real effort and how success will be measured.

  2. 02

    Prototype with real data

    A focused proof of concept evaluated against examples from your organization.

  3. 03

    Integrate & guard

    Connect to systems and permissions; add review steps, logging and fallbacks.

  4. 04

    Monitor & improve

    Track quality over time and refine prompts, models and data as usage grows.

Relevant technologies

Chosen per project, not by habit.

  • Python
  • LLM APIs
  • Open-weight models
  • Vector databases
  • PostgreSQL + pgvector
  • FastAPI
  • OCR engines
  • TypeScript

Related work

Artificial Intelligence / Automation

Document Intelligence Workspace

An AI workspace that extracts structured data from incoming documents and lets staff search internal knowledge with cited answers.

  • Field extraction
  • Validation rules
  • Human review
  • Semantic search
View Case Study

Workflow Automation

Internal Requests & Approvals Suite

A single place for purchase, leave, IT and facilities requests — each routed through the right approval chain automatically.

  • Dynamic forms
  • Rule-based routing
  • Delegation
  • SLA tracking
View Case Study

FAQ

Questions we hear often.

Is our data used to train public models?

We design integrations so your data is not used for model training, choosing providers and deployment options — including self-hosted models — that meet your requirements.

How accurate will it be?

We measure it. Every AI feature is evaluated on real examples before launch, and we design review steps for cases where confidence is low.

Do we need a lot of data?

Often not. Many document and language tasks work well with modern models and a modest set of examples for evaluation.

Can AI be added to our existing software?

Yes. Most of our AI work adds capabilities to existing systems through APIs rather than replacing them.

Complex problem? Good.

Have a software problem worth solving?

Whether you're starting with an idea, replacing an existing system, or trying to automate an operation that has become too complicated, let's talk.