Introduction
For the first time in decades of building products, I’m seeing a shift that feels bigger than mobile or cloud.
AI-native architecture isn’t “AI added into the app” it’s the app shaped around AI from day one.
In this new world:
- Rails is no longer the main intelligence layer
- Rails becomes the orchestrator
- The AI systems do the thinking
- The Rails app enforces structure, rules, and grounding
And honestly? Rails has never felt more relevant than in 2025.
In this post, I’m breaking down exactly what an AI-native Rails architecture looks like today, why it matters, and how to build it with real, founder-level examples from practical product work.
1. AI-Native Rails vs. AI-Powered Rails
Many apps today use AI like this:
User enters text → you send it to OpenAI → you show the result
That’s not AI-native.
That’s “LLM glued onto a CRUD app.”
AI-native means:
- Your DB supports vector search
- Your UI expects streaming
- Your workflows assume LLM latency
- Your logic expects probabilistic answers
- Your system orchestrates multi-step reasoning
- Your workers coordinate long-running tasks
- Your app is built around contextual knowledge, not just forms
A 2025 AI-native Rails stack looks like this:
- Rails 7/8
- Hotwire (Turbo + Stimulus)
- Sidekiq or Solid Queue
- Postgres with PgVector
- OpenAI, Anthropic, or Groq APIs
- Langchain.rb for tooling and structure
- ActionCable for token-by-token streaming
- Comprehensive logging and observability
This is the difference between a toy and a business.
2. Rails as the AI Orchestrator
AI-native architecture can be summarized in one sentence:
Rails handles the constraints, AI handles the uncertainty.
Rails does:
- validation
- data retrieval
- vector search
- chain orchestration
- rule enforcement
- tool routing
- background workflows
- streaming to UI
- cost tracking
The AI does:
- reasoning
- summarization
- problem-solving
- planning
- generating drafts
- interpreting ambiguous input
In an AI-native system:
Rails is the conductor. The AI is the orchestra.
3. Real Example: AI Customer Support for Ecommerce
Most ecommerce AI support systems are fragile:
- they hallucinate answers
- they guess policies
- they misquote data
- they forget context
An AI-native Rails solution works very differently.
Step 1: User submits a question
A Turbo Frame or Turbo Stream posts to:
POST /support_queriesRails saves:
- user
- question
- metadata
Step 2: Rails triggers two workers
(1) EmbeddingJob
– Create embeddings via OpenAI
– Save vector into PgVector column
(2) AnswerGenerationJob
– Perform similarity search on:
- product catalog
- order history
- return policies
- previous chats
- FAQ rules
– Pass retrieved context into LLM
– Validate JSON output
– Store reasoning steps (optional)
Step 3: Stream the answer
ActionCable + Turbo Streams push tokens as they arrive.
broadcast_append_to "support_chat_#{id}"Code language: JavaScript (javascript)The user sees the answer appear live, like a human typing.
Why this architecture matters for founders
- Accuracy skyrockets with grounding
- Cost drops because vector search reduces tokens
- Hallucinations fall due to enforced structure
- You can audit the exact context used
- UX improves dramatically with streaming
- Support cost decreases 50–70% in real deployments
This isn’t AI chat inside Rails.
This is AI replacing Tier-1 support, with Rails as the backbone of the system.
4. Example: Founder Tools for Strategy, Decks, and Roadmaps
Imagine building a platform where founders upload:
- pitch decks
- PDFs
- investor emails
- spreadsheets
- competitor research
- user feedback
- product specs
Old SaaS approach:
You let GPT speculate.
AI-native approach:
You let GPT reason using real company documents.
How it works
Step 1: Upload documents
Rails converts PDFs → text → chunks → embeddings.
Step 2: Store a knowledge graph
PgVector stores embeddings.
Metadata connects insights.
Step 3: Rails defines structure
Rails enforces:
- schemas
- output formats
- business rules
- agent constraints
- allowed tools
- validation filters
Step 4: Langchain.rb orchestrates the reasoning
But Rails sets the boundaries.
The AI stays inside the rails (pun intended).
Step 5: Turbo Streams show ongoing progress
Founders see:
- “Extracting insights…”
- “Analyzing competitors…”
- “Summarizing risks…”
- “Drafting roadmap…”
This builds trust and increases perceived value.
5. Technical Breakdown: What You Need to Build
Below is the exact architecture I recommend.
1. Rails + Hotwire Frontend
Turbo Streams = real-time AI experience.
- Streams for token output
- Frames for async updates
- No need for React overhead
2. PgVector for AI Memory
Install extension + migration.
Example schema:
create_table :documents do |t|
t.text :content
t.vector :embedding, limit: 1536
t.timestamps
end
Code language: JavaScript (javascript)Vectors become queryable like any column.
3. Sidekiq or Solid Queue for AI Orchestration
LLM calls must never run in controllers.
Recommended jobs:
- EmbeddingJob
- ChunkingJob
- RetrievalJob
- LLMQueryJob
- GroundedAnswerJob
- AgentWorkflowJob
4. AI Services Layer
Lightweight Ruby service objects.
Embedding example:
class Embeddings::Create
def call(text)
OpenAI::Client.new.embeddings(
model: "text-embedding-3-large",
input: text
)["data"][0]["embedding"]
end
endCode language: CSS (css)5. Retrieval Layer
Document.order(Arel.sql("embedding <-> '#{embedding}' ASC")).limit(5)Code language: HTML, XML (xml)Grounding prevents hallucinations and cuts costs.
6. Streaming with ActionCable
Token streaming UX looks magical and retains users.
7. Observability Layer (Non-Optional)
Track:
- prompts
- model
- cost
- context chunks
- errors
- retries
- latency
AI systems break differently than traditional code.
Logging is survival.
6. How To Start Building This (Exact Steps)
Here’s the fast-track setup:
Step 1: Enable PgVector
Install and migrate.
Step 2: Build an Embedding Service
Clean, testable, pure Ruby.
Step 3: Add Worker Pipeline
One worker per step.
No logic inside controllers.
Step 4: Create Retrieval Functions
Structured context retrieval before every LLM call.
Step 5: Build Token Streaming
Turbo Streams + ActionCable.
Step 6: Add Prompt Templates & A/B Testing
Prompt engineering is your new growth lever.
7. Why Rails Wins the AI Era
AI products are:
- async
- slow
- streaming-heavy
- stateful
- data-driven
- orchestration heavy
- context dependent
Rails was made for this style of work.
Python builds models.
Rails builds businesses.
We are entering an era where:
Rails becomes the best framework in the world for shipping AI-powered products fast.
And I’m betting on it again like I did 15 years ago but with even more conviction.
Closing Thoughts
Your product is no longer a set of forms.
In the AI era, your product is:
- memory
- context
- retrieval
- reasoning
- workflows
- streaming interfaces
- orchestration
Rails is the perfect orchestrator for all of it.
