[ai_fry_ur_brain]

Why would businesses do that when they can pay a fraction of a ML departments salary to a company like fin?

This is the same reasoning people use to say SaaS is dead, but it makes no sense. Rolling things yourself is often 10x more costly and not worth it, even with agents you need to pay 5-10 guys 150k-250k a year to build and train your own agent, why not pay fin 250k flat and not deal with any of it? Same goes with basically all other software that has nothing to do with your core product.

SaaS is alive and well and will continue to be.

[aurareturn]

  This is the same reasoning people use to say SaaS is dead, but it makes no sense. Rolling things yourself is often 10x more costly and not worth it, even with agents you need to pay 5-10 guys 150k-250k a year to build and train your own agent, why not pay fin 250k flat and not deal with any of it? Same goes with basically all other software that has nothing to do with your core product.

I built an AI support agent in one week. It hooks into our knowledge base, app API, runs tests, and then finally sends a Yes|No|Other option to a real human to send back to the customer. It was surprisingly easy to build. The hardest part was the knowledge of how to help the customer, which Fin can't do for me anyway.

I see absolutely zero value in something like Fin. There is no model training needed. It's all context. Anyone who is training a Qwen model for their customer support is doing it wrong. Paying Fin $250k flat does nothing since it isn't going to actually know how to solve problems. The real challenge is the knowledge and context engineering and Fin doesn't help there. The technical stuff is really easy to build.

[DoingSomeThings]

"Paying Fin $250k flat does nothing since it isn't going to actually know how to solve problems. The real challenge is the knowledge and context engineering and Fin doesn't help there"

You misunderstand the model. Fin does not have flat fee. They charge exclusively for resolutions. That's the entire value prop.

Correct that knowledge and context engineering are the key. Fin DOES help here. They have an entire backend suite to help you build out areas where Fin is failing. It shows you questions it couldn't resolve, looks at the answers your human team gave, and suggests updates to help articles to

You're correct this could all be build by a skilled engineer, but that's not the point. It's built for non-techincal users to use and implement. A person who rose through the support ranks and shows some technical competency can learn the system without any software knowledge.

[aurareturn]

The bulk of the work is context engineering which is done outside of Fin. Once you do the context engineering, it's very easy to duplicate Fin's features. Seriously. Just try it.

You don't need a fancy editor for "if this then do this". A simple text document is all you need. And if you do need a fancy editor, it's extremely easy to build it in 2026. Maybe 1-2 days.

I'm not a SaaS believer anymore.

[DoingSomeThings]

Maybe you've done this yourself. I'm honestly jealous if solving customer support was as easy as your describe.

In my case, I've spent the past 12 months running implementations at multiple companies. I've engaged directly with smart engineering teams to assist. It was not that easy.

What you outlined might work for a simple ecom business. It probably does 95% of the job for a simple case where you're delivering information. But it will fail the second it needs to take action or deliver personalized information based on client's account data.

That leads to the exact issue people here complain about... an LLM that doesn't actually answer the question, can't solve the problem, and is worse than talking to a human

[aurareturn]

  But it will fail the second it needs to take action or deliver personalized information based on client's account data.

And why would Fin be better here? It's very easy to give your agent context on the customer.

In 2026, every time I've tried to build a custom tool to replace a SaaS, I've succeeded. The biggest problem with SaaS is that they build a one size fits all. When you build a custom tool, you control everything from data to UI and it works for your business.

[christoff12]

Many, many people do not have the capacity to build and maintain custom solutions (whether in-house skills, or simply bandwidth) and therefore outsource to vendors.

It's an incredibly common aspect of business. Enterprise level contracts often include the sort of white glove service to help fill in these sort of gaps. On simpler plans, having the tooling provided frees up just enough capacity to handle the exceptions to keep the process running smoothly (since one doesn't have to build and run simultaneously).

Sometimes people want to minimize the hassle with stuff. It's why car washes and oil change places and coffee shops exist.

[vanuatu]

the bulk of the context engineering for users of these ai support platforms is done in the platform

and the amount of context needed to automate f500 is non trivial, plus you usually cant use reasoning because latency would blow up and you get escalated on

if this was so easy as you claim theres many millions for you to be made selling it to enterprises, but you wont

[aurareturn]

F500 is exactly the kind of scale where I fully expect support agents to be developed in house. They'll try Fin. Then one day, a single dev inside the company demos a custom agent that outperforms Fin and cost almost nothing.

[vanuatu]

tech forward f500 is possible (but.. even anthropic used fin)

most f500s are going to outsource. i think sierra is already at 40% of the f50? and each of those deals they have to compete against teams of inhouse engineers and convince execs to buy instead of build

the reality is agents at scale is a hard problem and most f500 engineers are not equipped to solve it

[ai_fry_ur_brain]

Have fun sinking 100s of hours into improving and maintaing that. Someone who sells software for real estate probably doesnt want to get into building software for customer service agents. This makes no sense. There should be urgency to make the product your selling better, not wasting time building support tools. Just pay for it.

[rtpg]

> I see absolutely zero value in something like Fin.

Y'all really over indexing on the "AI"-ness of intercom. Intercom is a chat box and help pages. Those are nice to have and nice to not have to build yourself (you can build your own help pages... great, now you're managing content and have to have an admin to update the docs etc!)

People here really forgetting the notion of "core competency".

[whstl]

Rolling something yourself was a waste of time when SaaS was cheap and competitive.

Not they’re all getting incredibly expensive, even the last few startups I worked at were paying hundreds of thousands of euros for services that were total garbage.

Do I really need a crappy 20k/yr app to help me with my 1:1s? Do I really need a 100k/yr clicks counter that requires two devs to keep running and still heavily miscounts the clicks? Do I need another crappy app to manage my translation JSON files?

[spwa4]

> I see absolutely zero value in something like Fin.

The value, of course, is that there is a website with a chatbox that some MBA can type in "never give any refunds anymore for any reason", and it just updates the AI support agent and sends an automated "I deserve a promotion and a raise" to their boss.

[aurareturn]

Yes. I agree. When I look at Fin's home page and marketing, I think to myself that this stuff can mostly just be text documentation given to an LLM. It's a tool built for MBAs but most of the work is done by a software engineer to give Fin that context in the first place.

So all Fin is is a UI on top of the context engineering done by a software engineer who integrated with Fin. It's extremely easy to duplicate Fin's UI and get rid of the $250k fee.

[spwa4]

Well, it's a few weeks of work, systems integration, and you'll need an SRE too if you're hoping to run it on any scale. But yes, I agree.

[aurareturn]

You'll need an SRE for Fin too. How else can Fin get access to your customer's data?

It takes the same amount of time to build a custom agent as an agent on Fin. All Fin does is provide a fancy UI for non-technical people to create rules.

They can create the same rules in plain text. If they want a fancy UI like Fin to do it, just build one in a day.

[spwa4]

They call it rules? Because of course one of the defining properties of LLMs is that they can decide to deviate, reinterpret, or ... rules. Which makes them more like guidelines, or so the meme goes.

[jmuguy]

Well specifically with just the AI agent/customer support product I think businesses would do well to handle this themselves rather than hoping a one size fits all solution from Intercom would serve them. Not just from a bespoke AI solution but also on cost. The other aspects of Intercom's product, the little chat bubble, CRM, can be had for much much less from dozens of competitors.

I think they mostly benefit from time in market and name recognition. The AI angle was a good bet to make when they made it, but is increasingly less of a differentiator.

I don't think SaaS is dead - but I think for a product like Intercom, that is very expensive, they get eaten alive by smaller SaaS + in-house AI agent.

[runako]

The problem is that Fin prices at $0.99 per outcome. Only for companies with tremendous support volume would it even begin to make sense to build in-house.

There's a wide swath of companies that do < (say) 20,000 cases monthly where the economics will never make sense. And a company finds Fin successful as it grows to 20k/mo, why would it decide to take on the headache as it grows to the 50k/mo? or whatever level where the economics could feasibly make in-house work?

[aurareturn]

  The problem is that Fin prices at $0.99 per outcome. Only for companies with tremendous support volume would it even begin to make sense to build in-house.

$0.99 could be the profit margin of small ecommerce businesses too so it might not make sense for small businesses.

[runako]

Let's say the small e-commerce business does 500 of these outcomes per month. ~$500 all-in cost at Fin.

I'm curious how you would calculate the other side of the ledger, the in-house approach. Assume the e-commerce business does not employ any AI/ML experts or programmers or anyone whose workday has ever been interrupted by a Github outage (this is the normal case for most businesses, not an artificial handicap). I'm curious how you would structure things to make an in-house more efficient than the $500/mo all-in.

[mchusma]

You are right. These outcomes also skew heavily towards the easy stuff for LLMs to get. So tickets that take a human 1 min to respond to now cost you $0.99 ($60+/hour) and you are stuck only doing the hard tickets.

[sdd2]

Man you are delusional.

You clearly have never ran a business and don't understand the dynamics of the make vs buy decision.

[aurareturn]

The dynamics have changed completely in 2026.

[y2244]

Saas is cheap for a small company but once you want large volume or per seat pricing for a large amount of users it gets expensive.

But rolling out your own allows you to then scale at minimal incremental cost