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人工知能

生成AIアプリケーション

大規模言語モデルやマルチモーダルモデルを活用し、チームが日常的に使うツールの中でコンテンツの作成、要約、変換、解説を行う、本番運用のアプリケーションを構築します。

概要

A generative AI demo can be assembled quickly. A generative AI application that serves real users, sensitive data and edge cases takes engineering: authentication, context management, streaming interfaces, cost controls, content safety and an evaluation loop that tells you whether a change made things better or worse.

We design and build these applications end to end, from the user experience to the model gateway, and integrate them with the systems of record that give generated output its context and its value.

ビジネスでの活用領域

01
Drafting and content operations
Generate first drafts of proposals, product descriptions, reports or correspondence from structured data and approved source material, with reviewers editing and approving before anything is published or sent.
02
Summarization and synthesis
Condense long case files, meeting transcripts, research and support histories into role-specific briefs, with citations back to source passages so readers can verify what the model asserts.
03
Copilots inside business applications
Embed assistants directly into CRM, ERP, ticketing or internal portals so users can query records, generate updates and complete routine tasks without switching tools or copying data between them.
04
Multimodal content understanding
Combine text, images and audio to describe products, transcribe and analyze calls, or interpret diagrams and screenshots, using multimodal models from providers such as Google, OpenAI and Anthropic.

エンジニアリングアプローチ

01
Design around the user's job
Map the workflow the application supports, the decisions users make and where a model adds value, then design interfaces that make generated content easy to inspect, edit and reject.
02
Engineer context and prompts
Assemble the right context for each request from documents, records and user state, using versioned prompt templates, structured output schemas and explicit instructions for uncertain cases.
03
Build the application platform
Implement a model gateway with routing, caching, rate limiting and cost tracking, together with authentication, authorization and audit logging consistent with your security standards.
04
Evaluate before and after release
Combine automated checks, model-graded scoring and expert review on representative cases, and run regression evaluations in CI whenever prompts, models or retrieval logic change.
05
Operate and improve
Instrument every request with tracing and user feedback signals, monitor quality, latency and spend, and feed observed failures back into the test set and the next release.

技術とプラットフォーム

Models and APIs

OpenAI
Anthropic Claude
Google Gemini
Amazon Bedrock
Azure OpenAI
Mistral

Application frameworks

TypeScript
Next.js
Python
FastAPI
Vercel AI SDK
LangChain

Safety and evaluation

Guardrails AI
NVIDIA NeMo Guardrails
promptfoo
Langfuse
Microsoft Presidio

Infrastructure

Kubernetes
PostgreSQL
Redis
OpenTelemetry
AWS
Microsoft Azure
Google Cloud

記載の技術名は各社の商標であり、提携関係や推奨を意味するものではありません。

提供する成果物

Production generative AI application integrated with your systems
Model gateway with routing, cost tracking and usage controls
Versioned prompt library and structured output schemas
Evaluation suite with regression tests in CI/CD
Content safety, PII handling and audit logging controls
Operational runbook and observability dashboards

経営層からのよくある質問

How do you deal with hallucinations?

We reduce them rather than claim to eliminate them. Grounding responses in retrieved source material, constraining output formats, instructing the model to state uncertainty and showing citations all help. Evaluation measures how often errors occur, and interfaces are designed so that people review generated content wherever an error would matter.

Will our data be used to train the provider's models?

Enterprise API terms from major providers generally exclude customer data from training by default, but terms differ and change over time. We review the current terms for each provider you consider, configure data retention settings, and can route sensitive workloads to models hosted inside your own environment.

Can we switch model providers later?

Yes, if the architecture allows it. We place a gateway and abstraction layer between the application and the model, keep prompts and evaluation sets as provider-neutral as practical, and use the evaluation suite to verify quality before any switch. Some prompt tuning is usually needed when changing models.

iQmindset

事業のために動く前に、まずその事業を学ぶAIグロースオペレーティングシステム。

エンタープライズAIトランスフォーメーション

点在するAIの試行を、事業を動かす統制されたシステムへ。

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