North Cohere’s AI Agents: Disruption in Action - The AI Podcast

Summary

Podcast Summary: The AI Podcast – "North Cohere’s AI Agents: Disruption in Action"

Release Date: August 10, 2025
Host: The AI Podcast

1. Introduction to Cohere’s North Platform

In this episode, the host delves into Cohere’s latest offering, the North platform, an AI agent solution tailored for enterprise use with a strong emphasis on security. The host expresses renewed enthusiasm for Cohere, highlighting recent updates that have enhanced the platform's competitiveness in the AI landscape.

Alex (00:00):
"Cohere has just released a new platform for creating, basically, AI agents for enterprise, and they have a huge focus on security."

2. Background and Branding

Cohere, a Canadian AI company, has introduced North, aptly named to reflect its Canadian roots. The host appreciates the clever branding choice, associating it with geographic identity.

Alex (00:00):
"Their new AI agent platform is called North. And, you know, of course they're up north, so I think that's some pretty good Canadian branding for you."

3. Features and Capabilities of North

The North platform is designed to automate various enterprise workflows by assigning specific tasks to AI agents. Unlike the early vision of autonomous agents acting like employees, North focuses on task-oriented automation, allowing organizations to delegate individual responsibilities to the AI.

Alex (00:10):
"Less and more it's going to be basically giving it tasks to accomplish... You figured out how to write the perfect newsletter and send it and the whole workflow that goes into that."

Key features include:

Chat and Search: Facilitates customer support queries, meeting transcript summarization, marketing copy creation, and information retrieval from internal and external sources.
Asset Creation: Generates tables, documents, slideshows, and conducts market research.
Integrations: Connects seamlessly with existing workplace tools such as Gmail, Slack, Salesforce, and Outlook, as well as industry-specific applications via MCP model context protocol servers.

Alex (00:40):
"North can run on an organization's on premise infrastructure, their hybrid cloud, their VPCs or air gapped environments."

4. Security and Privacy Emphasis

A standout aspect of North is its robust security framework, addressing a primary concern in AI deployment—data privacy. Cohere ensures that sensitive enterprise data remains secure by enabling private deployments on clients' own servers and firewalls, avoiding exposure through external APIs.

Nick Frost, Co-founder of Cohere (05:30):
"LLMs are only as good as the data they have access to. If we want LLMs to be as useful as possible, they have to have access to useful data and that means they need to be deployed in the customer's environment."

Security features include:

Granular Access Control
Agent Autonomy Policies
Continuous Red Teaming
Third-Party Security Tests

These measures ensure compliance with international standards such as GDPR, SOC2, and ISO 27001.

Alex (06:10):
"We can deploy literally on a GPU in a closet that they might have somewhere."

5. Enterprise Focus and Strategic Partnerships

Cohere’s North is strategically positioned for enterprise and white-collar sectors, collaborating with major institutions like RBC Bank, Dell, LG, Ensemble Health Partners, and Palantir. This focus on large-scale enterprises underlines their commitment to providing tailored AI solutions that integrate deeply with corporate infrastructures.

Alex (07:00):
"We've already piloted North with a bunch of customers... So there's some big players that are using this."

Additionally, Cohere’s acquisition of Auto Grid in May, a Vancouver-based company specializing in automating high-level market research, signifies their drive to enhance North’s capabilities and maintain a competitive edge.

Alex (09:00):
"Cohere actually acquired a company back in May called Auto Grid... They develop enterprise tools for automating high-level market research."

6. Comparison with Other AI Platforms

While North replicates many functionalities found in other AI agent platforms, its unique selling proposition lies in its enterprise-centric approach and stringent security protocols. However, the host notes that Cohere's language model may not yet rival leaders like OpenAI in standalone performance.

Alex (11:30):
"I don't think it's competitive as a standalone product... They're not competing head on with OpenAI and a lot of other players trying to have like the best model."

He speculates on the potential challenges Cohere might face, especially with the emergence of open models from competitors that could be adapted similarly to North’s toolset without requiring proprietary model training.

7. Future Outlook and Criticisms

The host remains cautiously optimistic about Cohere's trajectory. While praising their strategic focus and tool integrations, he emphasizes that Cohere may need to continue investing in their language model to ascend as a top-tier AI platform.

Alex (12:45):
"Cohere is going to have to spend some more investment to make sure their model is up to par and is kind of top notch."

Moreover, he highlights the importance of Cohere’s approach in addressing specific enterprise needs through partnerships and toolsets, which has significantly contributed to their substantial $5.5 billion valuation despite not leading in standalone AI model performance.

Alex (14:00):
"They're making some good moves, but I don't think it's the number one company at the moment."

8. Conclusion

The episode concludes with a reflection on Cohere’s ability to leverage strategic tool development and partnerships to achieve significant market valuation, even without being the top player in AI model performance. The host encourages listeners to explore Cohere’s offerings through his startup, AI Box AI, which provides access to multiple AI models, including Cohere’s.

Alex (15:30):
"I hope you learned something new about basically how you can take a model, take a, take a tool that might not be number one and still get an incredible $5 billion valuation out of it."

Key Takeaways:

Cohere’s North is a promising AI agent platform focused on secure, enterprise-level deployments.
Security and Privacy are paramount, with robust measures ensuring data remains within corporate environments.
Strategic Acquisitions and Partnerships bolster Cohere’s position in the market.
Competitive Edge lies in tailored enterprise solutions rather than leading standalone AI model performance.
Future Prospects depend on continued investment in model development and expanding tool integrations.

For those interested in exploring Cohere’s North platform and other AI models, the host recommends checking out AI Box AI, offering access to over 40 AI models for a subscription of $20. [Link provided in the podcast description.]

Notable Quotes:

Alex on Security: (06:10)
"We can deploy literally on a GPU in a closet that they might have somewhere."
Nick Frost, Cohere Co-founder: (05:30)
"LLMs are only as good as the data they have access to. If we want LLMs to be as useful as possible, they have to have access to useful data and that means they need to be deployed in the customer's environment."
Alex on Future Competition: (12:45)
"Cohere is going to have to spend some more investment to make sure their model is up to par and is kind of top notch."

This comprehensive exploration of Cohere’s North platform highlights its innovative approach to enterprise AI agents, merging functionality with stringent security to cater to large organizations' specific needs. While not yet leading in AI model performance, Cohere's strategic focus positions it as a significant player in the enterprise AI domain.

Transcript

Alex (0:00)

Cohere has just released a new platform for creating, basically, AI agents for enterprise, and they have a huge focus on security. They're doing a couple really interesting new things here. I got to say, Cohere is one of those platforms that I don't talk about a ton. It felt like their LLM kind of lagged for a while. They've made some updates and brought themselves up to be a little bit more competitive now. And I think that their new agent platform is really interesting and basically pushing them back to the front, so I'm excited to cover them again. I think I will also say I have a soft spot for them because they are a Canadian company, and I am originally Canadian. I live in America now, so always, always happy to cover a Canadian company. One funny thing I'll say is I do think their branding is pretty funny. Their new AI agent platform is called North. And, you know, of course they're. They're up north, so I think that's some pretty good Canadian branding for you. All right, before we get into this, I wanted to mention, if you want to try different models from Cohere or any of the other top AI companies, we. I have a startup called AI Box AI, and basically I have the top 40 AI models, all for a $20 subscription. You get access to everything on one platform, and we have a whole bunch of models from Cohere. We have their R model and their R Plus model. We also have Microsoft Meta, Quinn, Nvidia, Cohere, Anthropic, OpenAI. Like, we have everybody else as well. A bunch of cool audio and a bunch of image models. But if you want to try out what's going on with Cohere or any other AI tool, any other AI platform, I'd recommend go checking it out. There's a link in the description to AI Box AI. Yeah, would love to hear what you have to say. It's currently in beta, and we're getting some good reviews and good feedback. We're constantly working on it, so we'd love to hear what you have to say about it. All right, let's get into what is going on with CO here. So the first thing I want to say is, obviously, these AI Agent, these tool platforms, the idea is to automate your workflows, automate things that you're doing. Um, that's basically what they're doing. I think there's, like, this concept with agents that at some point we're gonna be like, hey, you're a marketing agent. Like, make my. You know, make my sales, go Crazy. And you, you know, the agent runs away and pulls off a miraculous miracle. I think less and less this is where people are, are believing these agents are going to be. And more and more it's going to be basically giving it tasks to accomplish. You figured out how to, you know, write the perfect newsletter and send it and the whole workflow that goes into that. And, and so you get the, you basically train the agent, make this automation that can go do that task for you. It's kind of interesting because there's two concepts with agents. One is that they're like an employee. I think right now we're like, we'll get there, I think but right now we're not there. I think right now we're at the A task. So if an employee does 10 different tasks, you would take one of those tasks and give it to an agent. That's kind of what we're calling agents now. I think in the future it's going to be the agents like an employee that can do all your things you train it to do. But in any case, I digress. What I will say is basically one of of the big problems with agents that companies have is that they are not very secure. You're giving it your private data and people don't really know where that all leads or goes. There's been a whole bunch of, you know, leaks, things like that. And so Cohere is basically trying to address that concern and keep their, their customers data private. Who are they working with? Are we working with big banks, RBC bank and a bunch of other like financial places and, and those kind of places. A lot of enterprise and white collar. So basically they are promising to enable private deployment so that enterprises and governments can keep their customers data safe and behind their own firewalls. Right. So this isn't a deployment where you're accessing their API. This is going to be on your own servers, your own firewalls protected. And that's kind of what Coheres basically. I think it's the angle they're taking so that they can stay competitive in the market. This is what they said. This is the co founder, Nick Frost of Frost. That's the greatest last name of a Canadian. I love that he's a co founder of cohere. He said LLMs are only as good as the data they have access to. If we want LLMs to be as useful as possible, they have to have access to useful data and that means they need to be deployed in the customer's environment. He's kind of Referencing the fact that if these companies want this to be really useful, you need to deploy them on your own servers, give them access to all of your company data, and then when you ask IT questions, it's actually looking at your data to give you results and responses. And it's very customized and it's stuff that you can't obviously get publicly on something like ChatGPT. So instead of using enterprise cloud platforms like Azure AWS, you can actually install north on your organization's private infrastructure so that it never sees or interacts with your customer's data. North can run on an organization's on premise infrastructure, their hybrid cloud, their VPCs or air gapped environments. So this is pretty cool. He said, quote, we can deploy literally on a GPU in a closet that they might have somewhere. Basically, they claim that they also include security protocols like granular access control agent autonomy policies, continuous red teaming and third party security tests. So basically it meets international compliance, GDPR, SOC2, ISO 27001. I'm not sure what country that is required by and I'm sure some people are yelling at me that it's like a super common thing I just wasn't aware of. But in any case, this is pretty exciting so far. CO here by the way, is a background on the company. They've raised about 970 million. Most recently they had a $5.5 billion valuation. So they're growing, they're still cruising along. They said that they already piloted north with a bunch of customers. So rbc, Dell, lg, Ensemble, Health Partners and also Palantir. So there's some big players that are using this. It is a cool tool. It basically mirrors many AI agent platforms right out of the box. Its main feature is chat and search, which basically lets you get answers to your customer support queries. You can summarize meeting transcripts, you can write marketing copy, you can access information from internal resources and the web. So these are things that we have seen other places. Um, but they're also basically trying to make this more focused on the enterprise and what they're doing in house and not giving it like access to, you know, they're not running this on their own server, you're running this on your personal company server. Um, it also has citations and quote unquote reasoning chain of thought. So an employee could audit or verify the output that is actually there. Right. It will show the steps that it went through to get an answer. So if you're saying like, hey, you know which customers in our database had the most problematic Transactions. It would give you a list and then you can go look through how it actually came to that conclusion. Right. So I think this is really interesting. The chat and search function are basically powered by Cohere. They have their own LLM model. We should go try on AI box if you're curious on how that works and what it looks like Command. They have a bunch of generative AI models. Compass is its multimodal search tech stack that they've created. It's so funny. I love this Compass North. This is great. This is a very, very Canadian. So this is what they said about it. They said it goes beyond just Q and A and gets into doing work for you. North has a bunch of assets, a bunch of asset creation. It can make tables, it can make documents, it can make slideshows, a bunch of market research or it can do a bunch of market research. I think it is worth noting that Cohere actually acquired a company back in May called Auto Grid. So this was a Vancouver based company and basically they develop enterprise tools for automating high level market research. So I think this is kind of cool that they went and acquired a company and basically used that to kind of push themselves forward and become more competitive for their customers. Like a lot of AI agent platforms, it can connect to existing workplace tools. So things like Gmail, Slack, Salesforce, Outlook, all of those, it can integrate with all of them. It also integrates with MCP model context protocol servers to basically access a bunch of industry specific or in house applications that a company might have. And this is a final quote from their co founder and CEO Mr. Frost himself from Canada. He said as you build confidence by chatting to the model, there's like a smooth transition that happens between using this as an augmentation to using it as an automation. All right. Will you completely automate everything in your life with this platform? I will say probably not. For now. I, I think this is probably a really useful tool especially for companies. I don't think it's competitive as a standalone product. This is my, my critical. After giving them their praises for their clever names and being Canadian. But I don't think this is actually a super competitive product as a standalone product. And I think this is evidenced by the fact that they're not competing head on with OpenAI and a lot of other players trying to have like the best model. They're basically trying to say, look, we have a really decent model and we have a whole bunch of really great tools for enterprise to use. Is this a bad approach? Not at all. I think this is a great approach. They're solving a problem that people have. I just don't think it's like it's not something that everyone has to use or try because it's probably not number one. Now, is it good at certain tasks, better at maybe tone or better at style or better at doing different things? Yeah, I think it is. But I also think it's probably getting most of its lift and usage. Is most of its valuation based on the software tools that have been basically built around their LLM. And I think it's. It almost calls into question, unless they're about to train and release like some new insane LLM, they're not number one. And so it calls into question, in my opinion, when you have OpenAI dropping like an open model like they just dropped yesterday, it almost calls into question like what if any company went and took that open model, ran it themselves and built all the tools that cohere has and competed with them without having to train their own model? It's kind of interesting. So I think Coyote is going to have to spend some more investment to make sure their model is up to par and is kind of top notch. We I haven't seen any big announcements from them since, since their last model, which was a while back, so I'll be curious to see where they go. They are an interesting company. I think they're, they're making some good moves, but I don't think it's the number one company at the moment. Thank you so much for tuning into the podcast. I hope you learned something new about basically how you can take a model, take a, take a tool that might not be number one and still get an incredible $5 billion valuation out of it. It's basically with making partnerships and making tools that address specific user needs. Thanks so much for tuning in. Make sure to leave a rating or review wherever you get your podcast. If this was useful to you. And go check out AI Box AI Box AI. There's a link in the description. You can try out Cohere for yourself and 40 other AI models. That should save you a ton of money. It's 20 bucks. All right, you have a great day and I'll catch you in the next episode.