企業はどのようにAI SaaS製品を構築しているか

Structured output generation inside real UIs

Some of the most impactful AI features don’t generate content — they generate structure you can build on.

Excalidraw AI is a perfect example. You describe the flow you want — “a user signs up, verifies email, and hits the dashboard” — and the AI writes the Mermaid.js code to match. The diagram appears instantly, fully editable inside the app. You’re not starting from scratch — you’re getting a smart, structured base that fits the use case.

This isn’t a static graphic. It’s code that thinks, turned into a visual workflow you can manipulate.

Other tools are exploring this too — like Uizard, which turns prompts into UI layouts, and Retool, where AI configures frontends and backend queries based on user goals.

In all these cases, the LLM isn’t just helping the user move faster — it’s producing outputs in the native language of the product.

Decision-support agents built into the workflow

Most SaaS tools assume the user knows what to do next. AI is changing that.

Now, we’re seeing embedded agents that can read the current state of a project, issue, or document — and propose the next action.

In Linear, AI summarizes bugs and issues, then suggests prioritization based on severity, frequency, or blocker status. It’s not just summarizing tickets — it’s interpreting urgency and nudging the team toward action taking upon a role of a vertical AI agent that is essentially acting as a bridge between departments.

Asana AI is doing something similar with project data. It spots stuck tasks, misaligned owners, or schedule drift — and quietly proposes updates to rebalance the work.

This type of agent doesn’t generate content. It reads signals inside the system—task progress, assignments, inputs—and makes small, helpful moves that shift the direction of the work.

AI-native onboarding that adapts to the user

Most onboarding flows are static — a few guided clicks, maybe a checklist. But LLMs are making it possible to start with what the user wants and build around that.

In Coda, onboarding feels more like a conversation. You describe what you’re trying to do — plan a team offsite, manage client deliverables, track habits — and the AI builds out a workspace scaffold to get you going. Tables, buttons, formulas — already in place.

Guidde takes a different approach: it uses product metadata and AI to auto-generate in-app walkthroughs based on your input. You say what kind of guide you need, and it creates the flow — no manual capture needed.

What used to be a tour is now a head start.

You show up with intent. The product responds to structure.

From structured output to adaptive onboarding, every use case we’ve covered relies on infrastructure that can handle natural language, context, memory, and dynamic outputs. Some of these tools work behind the scenes. Others are embedded directly into the product stack.

Let’s look at the most important platforms powering AI-native SaaS right now — the ones that help you build agents, manage RAG pipelines, structure inputs, and plug LLMs into real workflows.

1.Botpress

Botpress is what you reach for when you're building agents that need to do more than just answer questions. It’s made for teams who want real control over how AI behaves — combining logic, memory, action flows, and multichannel deployment in one place.

You can connect it to any backend, pass context across turns, handle API calls, and trigger real outcomes — all from inside the same conversation. It's especially strong in situations where chat needs to drive behavior, not just offer responses. Whether it’s onboarding users, scheduling visits, handling internal ops, or routing support, Botpress makes it feel seamless.

The platform also supports web, platforms such as WhatsApp and Telegram, and custom SDKs out of the box — so your agent goes where your users already are.

主な特徴

• Full control over logic, memory, and API actions
• Built-in tools for testing, analytics, and versioning
• Multichannel support (web, WhatsApp, Slack, custom)
• Easy handoff to live agents, fallback flows, and custom UI widgets

価格設定：

• Free Plan: $0/month with $5 AI credit included
• Plus: $89/month — includes live agent handoff and analytics
• Team: $495/month — adds role management, SSO, collaboration
• Enterprise: Custom pricing for high-scale or compliance-heavy teams

2. LangChain

LangChain is the backbone for many AI features that don’t look like chat at all — planning agents, internal copilots, analytics explainers, you name it. It’s flexible, modular and gives developers a clear way to connect LLMs to tools, APIs, and memory.

That flexibility comes with some tradeoffs. LangChain is very SDK-centric — most of the orchestration and debugging happen deep in Python or JavaScript. They’ve introduced a no-code builder called LangFlow, but it’s still early and lacks the polish or stability of the core SDK experience.

Still, if you need full control over how your agent thinks, plans, and acts — this is the tool most people reach for.

主な特徴

• Agent framework with support for tool use, planning, and memory
• Native support for OpenAI functions, RAG pipelines, vector search
• Modular design for chaining workflows and reasoning steps
• Works with most APIs, vector DBs, and document loaders

価格設定：

• LangChain OSS: Free and open source
• LangSmith (debugging + monitoring): Currently free; usage-based pricing coming soon

3. Pinecone

Pinecone is the vector database that shows up in nearly every production RAG system — and for good reason. It’s fast, scalable, and lets you store and retrieve high-dimensional data with minimal setup. Whether you're indexing support tickets, internal docs, or structured knowledge, Pinecone makes it easy to get relevant context into your LLM workflows.

The newly released Pinecone Assistant makes this even easier. It handles chunking, embedding, and retrieval behind the scenes so teams can build data-aware agents and search features without needing to manage infrastructure.

It’s rarely the only thing in your stack — but when fast, filtered retrieval matters, Pinecone is the one most teams reach for. Connect it to LangChain or Cohere, and you’ve got a reliable foundation for any RAG-based assistant.

主な特徴

• Fast, production-ready vector search
• Pinecone Assistant (2025) abstracts retrieval complexity
• Metadata filters, multi-tenant indexing, hybrid scoring
• Managed infra — no hosting or tuning required

価格設定：

• Starter: Free up to 5M vectors
• Standard: Usage-based, elastic scaling
• Enterprise: Dedicated capacity and support

4. Cohere

Cohere started as the go-to for fast, high-quality embeddings — and it still dominates that space. But over the past year, it’s evolved into a broader platform that powers retrieval-augmented generation (RAG) thanks to tools like its Rerank API and hosted Command R models.

The Rerank API is where Cohere stands out. It lets you reorder search results based on how well they match a query — so instead of passing 20 raw chunks to your LLM, you send 3 that matter. The result: faster responses, lower token usage, and sharper answers that feel intentional.

You also get multilingual support, long-context awareness, and an optional hosted stack that handles embeddings, search, and rerank in one place — no fine-tuning required.

Cohere shines when you need to improve what your model sees — not change how it reasons. Pair its Rerank API with a good vector store like Pinecone and a smart orchestrator like LangChain, and you’ll get shorter, more accurate, and more explainable answers.

主な特徴

• Rerank v3.5 for sharper, context-aware answer selection
• Hosted RAG stack with low-latency APIs
• Works well with Pinecone, LangChain, and LlamaIndex

価格設定：

• Embeddings: Free up to 100k queries/month
• Rerank: Usage-based (contact for pricing)

5.ラマインデックス

LlamaIndex is built around a specific idea: your AI is only as good as the data you give it. And if you're pulling that data from PDFs, wikis, databases, or spreadsheets, LlamaIndex is how you get it ready for retrieval — with structure, metadata, and smart routing.

Unlike Pinecone, which handles vector search, or Cohere, which reranks relevance, LlamaIndex focuses on the pipeline that feeds the model. It chunks and indexes your sources, keeps track of document metadata, and routes queries based on structure and intent — not just keywords or embeddings.

It’s especially useful for teams building AI products that rely on domain-specific content — product manuals, customer data, engineering logs — where context matters and generic retrieval breaks down.

LlamaIndex overlaps with LangChain in some areas, but it’s more focused on data prep and indexing, not agent planning or tool use.

主な特徴

• Indexing pipelines for structured and unstructured data
• Smart query routing and source tracking
• Works with Pinecone, Chroma, or local memory stores
• Pairs best with agents that need high-trust internal data access

価格設定：

• Open Source: Free (MIT)

6. Vercel AI

Vercel AI SDK is for teams who want AI to feel like part of the product — not just a chatbot dropped into the corner. It helps you build responsive, chat-like interfaces inside your app using React, Svelte, or Next.js — with full support for streaming responses, memory, and calling external tools.

It’s built by the same team behind Next.js, which shows in how well it handles frontend state and UX. The latest version also adds support for MCP (Model Context Protocol) — an upcoming standard for structuring model inputs, tool usage, and grounding sources. That means cleaner APIs, easier customization, and better control over what your assistant does.

You don’t build agents here — but if you already have one, this is how you turn it into a polished product experience. The SDK fits cleanly into any front-end stack, and its support for MCP, tool use, and streaming makes it ideal for AI interfaces that need to feel native.

主な特徴

• Add AI interfaces directly into React or Svelte apps
• Streaming, chat history, tool support, and grounding
• Supports MCP for structured, controllable model behavior
• Built by the creators of Next.js — optimized for frontend UX

価格設定：

• Open source SDK: Free
• Vercel hosting: Usage-based (compute + bandwidth)

7. Make

Make is like duct tape for SaaS products — especially in the early days of integrating AI. It’s a visual automation platform that lets you stitch together apps, trigger workflows, and even plug in AI models without writing much code.

It truly excels in providing product teams the ability to prototype AI behavior without requiring a complete backend or orchestration layer. Need to trigger a support follow-up when a user provides negative feedback in a chat? Use Make. Want to summarize that message with OpenAI and log it in your Hubspot CRM? Also, use Make.

It’s not built for complex planning agents or deep tool use, but for tasks where you just need to connect A to B to C, it’s fast, flexible, and friendly. This is especially useful when your product isn’t AI-first but you want to embed some intelligence behind the scenes.

主な特徴

• Visual builder with hundreds of prebuilt app integrations
• Easy to trigger actions from AI inputs (e.g. GPT summaries → email/send/CRM)
• Built-in OpenAI module, plus HTTP and webhook support
• Great for team ops, feedback loops, and lightweight automation
  
  

価格設定：

• Free: 1,000 ops/month, 2 active scenarios
• Core: $9/month — for small teams and light use
• Pro: $16/month — adds more ops, scheduling, and error handling
• Enterprise: Custom — for teams running mission-critical flows

1. Make AI part of the product, not just an add-on

AI should support your core experience, not sit on the sidelines. If it feels like a disconnected feature — like a chat window floating in the corner — it won’t get used.

Instead, integrate AI into the workflows people already rely on. In Linear, AI supports issue tracking and prioritization. In Coda, it builds tables and logic around the user’s goals. These features don’t feel separate — they’re part of how the product works.

Start by identifying where users get stuck or where work slows down. Use AI to smooth those moments, not just to impress.

2. Build around intent, not just input

LLMs work best when they understand why someone is doing something — not just what they typed. That means your product should capture user intent early and design flows around it.

This is what makes tools like Notion AI or Duolingo Max feel useful. They don’t just respond — they shape their responses based on context and goals. That only works if you structure your UX to guide and learn from the user’s intent, not just their words.

Ask: What is the user trying to accomplish? Then, build from that.

3. Give users visibility and control

AI should support decisions, not make them in a black box. Users should understand what the model is doing, where it got its information, and how to adjust its behavior.

Good AI interfaces explain why they suggested something. They let users retry, edit, or explore alternatives. This helps users build confidence and prevents over-reliance on automation.

Expose data sources, show prompt logic when it makes sense, and always leave room for manual overrides.

4. Prepare for edge cases and failure

LLMs won’t always behave the way you expect. They can miss context, produce vague outputs, or misinterpret instructions. Your product should be ready for that.

Add guardrails. Use confidence scores to route uncertain responses. Allow graceful fallbacks to other large language models or human support. And most importantly, track how users interact with the AI so you can learn where it helps — and where it needs work.

AI should improve your product, not make it unpredictable.

5. Start with one strong use case and expand gradually

You don’t need to make your whole product AI-driven from day one. The most successful teams start small — one feature, one workflow — and improve it until users rely on it every day.

That might be onboarding, document search, analytics summaries, or task automation. Focus on one area where AI can reduce friction or increase speed, and make it work well before scaling up.

Strong, reliable features build trust. Once your users depend on them, expanding to other use cases becomes much easier.