The AI Landscape Explained — Models, Agents, and RAG • Rithik's Blog.

The AI Landscape Explained — Models, Agents, RAG, Context Windows, and the Companies Shaping It All

If you open X, LinkedIn, YouTube, or any tech newsletter, it feels like the world is drowning in AI terms.

You keep hearing things like:

LLMs
multimodal models
RAG
agents
wrappers
open weights
local models
tool calling
reasoning models
context windows
embeddings
inference
fine-tuning
distillation

Most people pick these words up in fragments.

They hear that DeepSeek is cheap, Perplexity is like AI search, Claude is strong for coding, Gemini has long context, Llama is open, Ollama runs models locally, RAG reduces hallucinations, and agents automate workflows.

But they never get one clean map of how all of this fits together.

This article is that map.

The goal is simple: after reading this, you should understand what AI actually looks like in practice today, what kinds of AI products exist, what the major companies are building, what category each company belongs to, and how the entire stack connects.

The AI world is changing fast, but the structure underneath it is becoming clearer.

At the center of it are foundation models.

Around them are layers of products, infrastructure, workflows, and business models.

First: What People Mean When They Say “AI” Today

When most people say AI today, they are usually talking about generative AI.

That means systems that can create or transform content from prompts.

In practice, this usually includes several different model families:

Language models that generate and reason over text
Multimodal models that handle text, images, audio, video, and files
Speech models for text-to-speech, speech-to-text, and voice interaction
Image models for generation and editing
Video models for generation and editing
Embedding and retrieval models for semantic search and memory systems
Agent systems that combine models with tools, memory, and planning to complete tasks

That is the first important mindset shift.

Modern AI is not one thing.

It is an ecosystem.

The Core of Everything: Foundation Models

The single most important concept in the modern AI industry is the foundation model.

A foundation model is a large model trained on broad data so it can later be adapted to many different tasks.

Instead of building one model for summarization, one for translation, one for coding, and one for question answering, companies now build a broad base model and then align, fine-tune, or prompt it for many use cases.

That is why the same model family can:

chat
summarize documents
write code
analyze images
call tools
reason through tasks step by step

When people say OpenAI, Anthropic, Google, Meta, DeepSeek, Mistral, Cohere, xAI, Qwen, or Microsoft Phi, they are usually talking about companies that either build foundation models directly or distribute them in a major way.

The companies that train powerful foundation models sit at one of the most important layers in the stack.

A huge part of the rest of the ecosystem builds on top of them.

The AI Stack: The Cleanest Way to Understand the Industry

The easiest way to understand the market is to imagine a stack.

At the bottom is compute and infrastructure.

This includes:

GPUs
chips
cloud systems
model serving infrastructure

This is where companies like NVIDIA, AWS, Google Cloud, and Microsoft Azure matter.

Above that is the foundation model layer.

This is where the core models are trained.

Above that is the platform layer.

This includes things like:

APIs
inference engines
vector databases
orchestration tools
agent frameworks
evaluation systems
local runtimes

This is where tools like Hugging Face, Ollama, LM Studio, vLLM, and LangChain fit.

Above that is the application layer.

This is where people interact with actual products:

chat assistants
coding copilots
AI search tools
image tools
voice systems
workflow products

This is where names like ChatGPT, Claude, Gemini, Perplexity, Grok, Runway, and ElevenLabs show up.

Above that is the business and workflow layer.

These are the companies packaging AI into vertical solutions for areas like:

finance
healthcare
law
customer support
recruiting
operations
sales

This is why people get confused.

A single company can exist in multiple layers at once.

For example:

OpenAI is both a model company and an application company
Google is a model company, platform company, and cloud company
Perplexity is mainly an application company, but also touches platform routing and APIs
Meta is a model company more than a dominant consumer app company
NVIDIA is infrastructure-first but increasingly part of the model ecosystem too

The Major AI Categories and the Companies in Each

Frontier Closed-Model Companies

These are the companies most associated with top-end proprietary models and large commercial ecosystems.

OpenAI

OpenAI is one of the central frontier labs.

It spans:

flagship reasoning models
multimodal models
voice systems
image generation
developer APIs
consumer chat products

It is one of the clearest examples of a company that is both a model lab and a consumer AI platform.

Anthropic

Anthropic is another core frontier lab through the Claude family.

Claude is especially associated with:

coding
long context
enterprise use
careful behavior
document-heavy tasks

Anthropic is best thought of as a foundation-model company with strong enterprise and developer positioning.

Google

Google’s frontier AI position comes through Gemini and DeepMind.

Google is especially strong in:

multimodal systems
long-context models
product integration
search and cloud distribution
enterprise deployment

Google is important because it sits across multiple powerful layers of the stack at once.

xAI

xAI is the newer frontier player built around Grok.

It is increasingly associated with:

reasoning models
search-connected behavior
multimodal capabilities
tool use
large context systems

These companies are best understood as foundation model labs with developer and consumer products.

Open-Weight and Semi-Open Model Leaders

These companies matter because their models can often be downloaded, self-hosted, or deployed more flexibly.

Mistral AI

Mistral is one of the most important companies in the open and deployable model ecosystem.

Its strength comes from offering a broad family across:

large models
small models
coding models
multimodal models
enterprise-friendly deployments

Mistral is especially important because it positions itself across:

cloud
edge
VPC
on-prem

DeepSeek

DeepSeek became globally important because of its strong performance-per-cost profile.

It is much closer to the model layer than many people realize.

DeepSeek should be thought of primarily as a foundation model company, especially in the open-weight conversation.

Qwen

Qwen, from Alibaba, is another major family spanning:

language models
multimodal models
audio-capable systems
tool-using systems
agent-friendly systems

Qwen matters because it is one of the strongest broad model families outside the most talked-about Western labs.

Microsoft Phi

Phi represents the importance of small language models.

Not every useful AI system needs a giant frontier model.

Smaller models matter for:

edge environments
efficient deployment
lower latency
lower cost
practical embedded use cases

NVIDIA Nemotron

Nemotron sits in an interesting middle zone.

NVIDIA is primarily infrastructure-first, but Nemotron shows how the company also participates in the model ecosystem through specialized model, recipe, and deployment offerings.

These companies are best thought of as model producers whose models can often live outside their own apps.

Search-Native and Answer-Engine AI Companies

Perplexity

Perplexity belongs primarily in the AI search / answer engine category.

That distinction matters.

Perplexity is not mainly known for being the original trainer of the world’s most dominant general-purpose base model family.

It is known for building a product layer around:

search grounding
answer generation
citations
routing
APIs
retrieval
agentic querying

So Perplexity is better understood as an answer engine and search-grounded AI platform than as a classic foundation-model origin lab.

Enterprise Language and Retrieval-Focused AI

Cohere

Cohere is especially strong in enterprise use cases.

It is closely associated with:

retrieval-heavy applications
multilingual systems
RAG
business language tasks
tool-oriented enterprise workflows

Cohere sits somewhere between a foundation-model company and an enterprise AI platform company.

Image Generation Leaders

Image generation is its own segment, even though the line between text and multimodal systems is getting blurrier.

Stability AI

Stability AI is one of the most important names in open image generation through Stable Diffusion.

It matters because it helped make image model deployment far more accessible.

Black Forest Labs

Black Forest Labs became highly visible through FLUX.

It is one of the strongest names in image generation and editing, especially in the open and deployable image-model ecosystem.

Other Labs in Image

Major labs like OpenAI, Google, xAI, and creative platforms like Runway also participate in image generation.

But Stability AI and Black Forest Labs are more directly associated with image-model identity itself.

Video Generation Leaders

Runway

Runway is one of the clearest names in AI video.

It is strongly associated with:

generative video
creative workflows
scene consistency
developer-facing APIs
media production tooling

Video generation is still an evolving segment, but Runway remains one of the most visible category leaders.

Voice and Conversational Audio Leaders

ElevenLabs

ElevenLabs is one of the clearest leaders in synthetic voice.

It is associated with:

text-to-speech
expressive voice generation
multilingual voice systems
cloned voices
conversational voice experiences
voice agents

ElevenLabs is not just a voice tool.

It is becoming part of the broader voice-agent platform conversation.

Local AI and Open Deployment Ecosystem

This category matters much more than beginners often realize.

Hugging Face

Hugging Face is the central open ecosystem hub for:

model distribution
datasets
demos
experimentation
community sharing

It is one of the most foundational companies in the open AI ecosystem.

Ollama

Ollama made local model usage dramatically easier.

It matters because it lowers the barrier to running models on your own machine.

LM Studio

LM Studio is another major local AI runtime and desktop environment.

It is part of the same broader movement toward more accessible local AI.

vLLM

vLLM is one of the most important open-source serving engines for efficient inference.

It is more infrastructure than consumer app.

But it matters enormously in deployment.

These companies are not usually “AI assistants” in the consumer sense.

They are part of the developer, deployment, and distribution ecosystem.

Closed Models vs Open Models vs Open Weights

One of the biggest sources of confusion in AI is the meaning of the word open.

A closed model is controlled by the company that built it.

You usually access it through:

an API
a web app
a commercial platform

Examples usually include companies like:

OpenAI
Anthropic
much of Gemini access

An open-weight model is one whose weights are available for download or deployment under some license.

Examples often include:

Llama
many Mistral releases
DeepSeek releases
Qwen releases
FLUX variants
Nemotron models

This distinction matters because open-weight is often more accurate than casually saying open source.

Some companies release weights without releasing every part of the training pipeline.

Practically, the tradeoff looks like this:

Closed models often lead on frontier capability
Open-weight models often win on control, privacy, flexibility, and self-hosting

What Is an LLM?

An LLM, or large language model, is a model trained to predict and generate text token by token.

That sounds simple.

But it turns out to be powerful enough to support tasks like:

summarization
translation
coding
question answering
structured extraction
conversational reasoning

That said, the market is no longer just about text-only LLMs.

It is increasingly about multimodal systems that combine:

text
images
audio
files
tool use

That is why many leading systems now sit somewhere between classic language models and broader multimodal platforms.

What Is a Context Window?

A context window is the amount of text or tokens a model can process in one interaction.

In simple terms, it is how much the model can “see” at once.

A larger context window helps with tasks like:

long conversations
big codebases
large PDFs
repositories
multi-file analysis
contract review
research summarization

But there is an important practical truth here.

A larger context window does not automatically mean better performance on everything placed inside it.

Long context helps, but you still need:

relevance filtering
retrieval
chunking
prompt design
context engineering

That is why teams increasingly think about context as a system-design problem, not just a model-spec number.

What Are Tokens?

Tokens are the units that models process.

They are not exactly the same as words.

A token might be:

a short word
part of a word
punctuation
a number
a symbol

That is why model pricing, rate limits, and context windows are measured in tokens, not pages.

So when a model says it has a huge context window, that does not mean it can read that many words directly.

It means it can process roughly that many tokens.

What Is RAG?

RAG stands for retrieval-augmented generation.

This is one of the most important practical ideas in the AI world.

The basic idea is simple:

Instead of asking the model to answer from its own internal memory alone, you first retrieve relevant information from external sources and then pass that into the model.

That way, the answer is grounded in actual data.

This is why RAG matters so much for real business systems.

A base model may know a lot about the world in general.

But it does not automatically know your:

private PDFs
internal SOPs
contracts
deal memos
policy documents
Slack threads
database records

RAG connects the model to your actual data.

A typical RAG pipeline looks like this:

ingest documents
split them into chunks
convert them into embeddings
store them in a vector database or search index
retrieve the most relevant chunks
send those chunks to the model
generate a grounded response

That is why RAG sits at the center of so many enterprise AI products.

What Are Embeddings?

Embeddings are vector representations of content.

They turn text, images, or other information into numerical forms that can be compared mathematically.

That is what makes things like this possible:

semantic search
clustering
recommendation systems
similarity matching
retrieval in RAG systems

If language models are the generation and reasoning engine, embeddings are often the memory indexing and retrieval layer.

What Are Agents?

An AI agent is a system that uses AI to pursue a goal and take actions on behalf of a user.

This is where a major distinction matters:

A chatbot answers.

An agent acts.

A chatbot usually responds one turn at a time.

An agent can:

break down a task
choose tools
search for information
run code
call APIs
access files
update records
continue across steps
decide what to do next

Examples of agents include:

a coding agent that reads a repo, edits files, runs tests, and fixes bugs
a research agent that searches sources and writes a report
a support agent that checks accounts and resolves tickets
a recruiting agent that finds profiles and drafts outreach

That is why the industry is moving so strongly toward agentic systems.

Agents vs Workflows

People often confuse agents and workflows, but they are not the same thing.

A workflow follows predefined steps.

An agent can decide its own steps dynamically.

A workflow is best when the path is already known.

For example:

read a form
classify it
route it
send an email

An agent is better when the path is uncertain.

For example:

investigate an issue
inspect records
read previous messages
search policy documents
determine what happened
resolve it

Workflows are structured.

Agents are adaptive.

Most real production systems use both.

Usually, the strongest systems are workflows with agentic parts inside them, not uncontrolled “full agent everywhere” systems.

What Is Tool Calling?

Tool calling means the model can invoke external functions or APIs instead of only producing text.

This is one of the key shifts that turns AI from a text generator into an action layer.

Examples of tools include:

web search
calculator
database query
code execution
email sending
calendar lookup
CRM updates
image generation
file access

Without tools, a model can describe what should happen.

With tools, it can actually participate in making it happen.

What Are Wrappers?

A wrapper is an AI product built mainly on top of someone else’s base model.

It usually adds a new interface, workflow, or niche use case without owning the deepest model layer.

People often use the word dismissively.

That is too simplistic.

A wrapper can still become a strong business if it adds real value through:

better UX
domain-specific prompting
RAG over private data
workflow automation
integrations
collaboration
compliance
memory
operational structure

A useful distinction is:

thin wrapper — mostly just a UI over another model
thick wrapper — adds meaningful workflow, retrieval, tooling, memory, or domain value

Many AI startups sit somewhere on this spectrum.

What Is Inference?

Inference is the act of using a trained model to generate outputs.

Training creates the model.

Inference runs it.

This distinction matters a lot economically.

Many companies are not training giant frontier models from scratch.

Instead, they are optimizing things like:

serving
routing
batching
caching
throughput
latency
deployment cost

That is why inference infrastructure matters so much.

AI is not only about intelligence.

It is also about the cost of turning that intelligence into a useful product.

What Is Fine-Tuning? What Is Distillation?

Fine-tuning means taking a pretrained model and training it further on a narrower dataset or behavioral objective.

The goal is to make it better for a particular domain, style, or task.

Distillation usually means using a stronger model or stronger training setup to transfer useful behavior into a smaller model.

Why does this matter?

Because businesses often want:

lower cost
faster speed
more control
smaller deployment footprints
reliable enough performance

This is why smaller model families continue to matter.

Not every problem needs the biggest possible model.

Multimodal AI: The Industry Is Moving Beyond Text

A major shift in AI is that the leading products are no longer only text systems.

The direction is increasingly multimodal.

That means handling combinations of:

text
images
audio
video
files
tools

This is why the future AI product is not just one chat box.

It is a broader interface over multiple model types and multiple action layers.

Local Models: What They Are and Why They Matter

A local model is a model you run on your own device or infrastructure instead of through a remote API.

People want local models for several reasons:

privacy
control
offline access
lower marginal cost after setup
reduced dependence on vendors
custom deployment flexibility

But local models also come with tradeoffs:

hardware limits
memory limits
slower performance on consumer devices
model size constraints
operational complexity

That is why the local ecosystem matters so much.

Tools like Ollama, LM Studio, Hugging Face, and vLLM reduce the friction.

The modern AI world is increasingly shaped by the tension between local control and cloud convenience.

Small Models vs Frontier Models

Bigger is not always better.

A frontier model is usually best for:

open-ended tasks
ambiguous tasks
multi-step reasoning
high-stakes work
broad multimodal capability

A small model is often better for:

narrow tasks
repetitive operations
latency-sensitive systems
private deployments
lower-cost applications

A smart AI architecture often mixes model sizes together.

For example:

small model for routing
embedding model for retrieval
medium model for classification
frontier model for final reasoning
speech model for voice
image model for visual generation

That is why no single company dominates every layer in exactly the same way.

Advanced Models vs Legacy Models

People also use the words advanced and legacy loosely.

A practical way to think about them is this:

Advanced or current models are the actively supported, strategically important, higher-performing models in a family
Legacy models are older versions still kept alive for compatibility, pricing, or migration reasons

Legacy does not always mean useless.

Older models can still be valuable for:

cheaper inference
stable integrations
narrow workflows
fallback systems
internal business tools

But when people want best-in-class capability, they usually reach for the latest flagship or latest recommended balanced model.

Which Company Belongs to Which Segment?

Here is the clean mental map.

Foundation / Frontier Closed Models

OpenAI
Anthropic
Google
xAI

Open-Weight Foundation Models

Meta Llama
Mistral
DeepSeek
Qwen
NVIDIA Nemotron
Microsoft Phi

Search / Answer Engine AI

Perplexity

Enterprise Retrieval / Language Infrastructure

Cohere

Image Generation

Stability AI
Black Forest Labs

Video Generation

Runway

Voice Generation and Voice Agents

ElevenLabs

Open Model Hub / Distribution

Hugging Face

Local Model Runtimes

Ollama
LM Studio

Inference / Serving Infrastructure

vLLM
NVIDIA ecosystems
cloud AI platforms

This distinction alone clears up a lot of confusion.

Why Some Companies Feel More Powerful Than Others

The market rewards leverage.

A company that controls the foundation model layer controls a lot of downstream power:

performance
pricing
features
model behavior
ecosystem gravity
developer demand
product direction

A company that mainly wraps another model is more dependent on the upstream model provider.

That does not mean it cannot win.

It just means the moat has to come from somewhere else:

distribution
UX
workflow quality
vertical integration
speed
data
trust
compliance
brand

That is why infrastructure companies, foundation-model labs, and distribution hubs often feel especially powerful.

They control the rails.

The Real Meaning of AI Workflows

An AI workflow is a structured system in which the model is only one part of a larger process.

For example, a document workflow may do something like this:

ingest a PDF
parse it
chunk it
embed it
retrieve relevant sections
pass those sections to a model
extract fields
write results to a database
trigger a follow-up action
route uncertain cases to human review

That entire chain is the workflow.

The model is one component inside it.

This is why businesses often overestimate “the model” and underestimate system design.

In production, strong AI outcomes come from good architecture as much as from raw model quality.

Why RAG, Agents, and Workflows Are the Three Most Important Practical Ideas

If foundation models were the first major phase of the generative AI wave, the next phase is about making them useful in real systems.

That next phase is defined by three practical ideas:

RAG solves the private-data and freshness problem
Workflows solve the reliability and operations problem
Agents solve the autonomy and task-execution problem

That trio explains a huge share of modern AI product building.

So What Is Perplexity, Really?

Perplexity is best understood as a search-grounded AI answer engine and platform.

It is not primarily the canonical origin of the world’s leading general-purpose base model family.

Its identity is much more tied to:

search
citations
retrieval
answer synthesis
model routing
developer APIs
agentic information access

So Perplexity belongs mainly in the AI search / answer engine / routing layer.

So What Is DeepSeek, Really?

DeepSeek is a foundation model company.

It became especially notable because of:

strong open model releases
aggressive performance-per-cost positioning
broad attention from developers
deployable models
API compatibility patterns familiar to developers

So DeepSeek belongs much closer to the open-weight foundation model category than to the search-engine or wrapper layer.

So What Is “Foundational” in AI?

When people say foundational, they usually mean one of two things.

The first meaning is foundation models themselves.

The second meaning is the foundational layers of the stack that many others build on top of.

By that logic:

OpenAI, Anthropic, Google, Meta, DeepSeek, Mistral, Qwen, xAI are foundational at the model layer
NVIDIA and major cloud providers are foundational at the compute and infrastructure layer
Hugging Face, vLLM, Ollama, and LM Studio are foundational in deployment and distribution ecosystems

That distinction is useful because it separates core model creators from the supporting rails that make the ecosystem function.

The Direction the Industry Is Moving

The industry is moving toward a few clear patterns.

First, models are becoming more multimodal.

Second, products are becoming more agentic.

Third, enterprise adoption increasingly depends on:

retrieval
governance
security
reliability
workflow structure

Fourth, the open-weight ecosystem is getting stronger.

Fifth, local deployment remains strategically important even while cloud APIs still dominate frontier performance.

The future is not one chatbot doing everything.

It is a layered system of:

models
retrieval
tools
agents
infrastructure
orchestration
cloud systems
local systems

The Simplest Summary of the Whole AI World

If all of this had to be compressed into one clean idea, it would be this:

The modern AI world has four centerpieces.

Models are the brains
RAG and retrieval are the memory bridge to real data
Tools and agents are the action layer
Workflows and infrastructure are what make it useful in the real world

And the companies fit around those roles:

OpenAI, Anthropic, Google, xAI — frontier proprietary brains
Meta, Mistral, DeepSeek, Qwen, Phi, Nemotron — open or deployable model families
Perplexity — search-grounded answer engine
Cohere — enterprise language and retrieval strength
Stability AI and Black Forest Labs — image generation
Runway — video generation
ElevenLabs — voice generation and voice agents
Hugging Face — model distribution hub
Ollama and LM Studio — local runtime layer
vLLM and NVIDIA ecosystems — efficient deployment and serving infrastructure

Final Takeaway

If you understand these distinctions, you already understand more than most people casually talking about AI online.

You understand that:

a chatbot is not the same as an agent
a big context window is not the same as good retrieval
a wrapper is not automatically useless
a foundation model company is different from an application company
local AI is different from cloud AI
open models and closed models solve different problems
the best AI systems are not just “a model” but a full architecture

That is the real state of AI today.

It is not one industry.

It is a stack.

And once you see the stack clearly, a lot of the noise starts to disappear.