1k under 1k: B2B AI Products You Can Try Today
We analyzed 1,000+ B2B AI products with fewer than 1,000 employees on LinkedIn.The companies below represent accessible solutions you can implement today.
Selecting the top b2b AI Product
B2B AI Products | Employees (LinkedIn) | Focus Area |
|---|---|---|
Abacus.AI | 101-250 | Enterprise AI Platform |
Adept AI | 101-250 | AI Agent |
AssemblyAI | 101-250 | Speech AI |
C3.ai | 501-1000 | Enterprise AI Platform |
Character.AI | 101-250 | Conversational AI |
Clarifai | 101-250 | Computer Vision AI |
Clari | 501-1000 | Revenue Operations AI |
Cohere | 251-500 | LLM Platform |
Cognition AI | 101-250 | AI Developer/Coding Agent |
Copy.ai | 101-250 | Marketing Copy AI |
Sorting by alphabetical order. For access to our complete database of 1,000+ AI companies, please reach out to us.
Key findings:
• Infrastructure and horizontal applications are commoditizing fast – prices drop
• Vertical applications maintain pricing power through domain expertise
• Most value concentrates in orchestration tools and industry-specific solutions
We organized vendors into 5 layers based on function:
Layer 1: Infrastructure & Foundation Models
This layer provides the models, compute resources, and hosting infrastructure that power everything else. Foundation models deliver language understanding and reasoning capabilities.
Cloud platforms offer GPU access without managing hardware. Model hosting services let you run AI via an API without setting up infrastructure.
Layer 2: Development & Operations
Building and maintaining AI systems requires tracking experiments, labeling data, monitoring production performance, and ensuring safety.
Teams use these tools to move from prototype to production reliably. Without proper monitoring and governance, you can’t diagnose failures or ensure that models comply with compliance rules.
These platforms handle the operational complexity, so data science teams focus on improving models rather than managing infrastructure.
Layer 3: Data & Search Infrastructure
AI applications need fast access to relevant information. Vector databases store embeddings – numerical representations of text, images, or data – and retrieve the most similar items in milliseconds. This layer powers everything from semantic search to RAG systems.
Layer 4: Agent Orchestration & Tools
Orchestration frameworks manage prompt engineering, data flow, and tool integration for LLMs. Without these, you manually design prompts, parse outputs, and trigger API calls. Orchestration automates multi-agent coordination, memory management, and RAG integration.
Layer 5: Applications (Horizontal & Vertical)
Applications sit at the user-facing layer where AI systems interact directly with end users. We divide these into horizontal applications (work across all industries) and vertical applications (solve industry-specific problems with deep domain expertise).
- Horizontal applications offer broad utility but face intense competition and commoditization.
- Vertical applications require specialized knowledge and longer sales cycles but build stronger moats through industry expertise.
Horizontal Applications
These tools work across industries and solve common business problems. Competition is fierce, and differentiation comes from execution speed and integration depth rather than unique capabilities.
Vertical Applications
These applications solve industry-specific problems and require deep domain expertise. They build stronger competitive moats through specialized knowledge, regulatory compliance, and workflows that horizontal tools can’t easily replicate.
Further Reading:
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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