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Comment by DoingSomeThings | Hacker News Reader

"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.

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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.

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

  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.

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.

For the last 15 years, I'm that person in the company who always said "let's not build it ourselves".

In the last 6 months, I'm that person in the company who always said "let's just build it in a few days".

I agree with you 100%. Fin and products like this simply do NOT solve the hard part of providing support in 2026. Basically, the hard parts are (1) coming up with the tools for agents to use, like searching for data, making updates, etc. (2) reviewing the logs of actual usage and adjusting prompts, docs, tools based on the real feedback. (3) tuning human escalation procedures.

This process is an ongoing effort, with an upfront engineering commitment which depends entirely on the product, but can be months of work. But if you have your own backend, I would argue this hard works is made HARDER by implementing something like Salesforce/Fin, because you have to now pipe a bunch of data and structure over to them, which is a pain.

LLM models capable of doing this are a commodity, the UI for customers and support teams is pretty trivial, the database/backend is trivial.

Outside of some cases, if you have your own app, and you have a given support volume, build your own.

Agreed.

A recent example is that we replaced our support ticket system with an in-house built one. The new system lives in the same database as our app. Every ticket now has real live customer data. You can't get this kind of integration easily using a 3rd party tool.

It was surprisingly easy to build. Just took 2-3 days for us. Massive improvement in productivity. Took about $100 in tokens to build. Maybe an hour of maintenance work per week.

This larger company took 48 hours: https://tradecore.com/resources/blog/we-replaced-zendesk-in-...

Doing this would've be seen as crazy in 2023. But in 2026, it's often an advantage. Better, more integrated, cheaper, faster.

I'm happy to buy if it's something I know we don't have the expertise to build. For example, you'll never catch me saying we need to build our own database. But for something like Fin, I know exactly how to build it for my company and I think I can build a better agent with better context, faster iteration, cheaper, and more tailored to my company's business.

I hear you -- you and other teams are capable of building internal versions that just work.

I'm equally excited -- I've spent much of my career building janky internal versions of popular SaaS out of necessity since we didn't have the budget to buy. To be able to do a better job with less effort is enticing.

But this is: a) a step-change that hasn't had a full year to bake; we should all anticipate the pain associated in the medium term after a few iterations, inevitable feature add-ons, etc. b) beside the point.

Yes, many teams can also build internally, but it doesn't change the fact that others find value in outsourcing. Just because it's much easier, or rather __because__ it's easier to stand stuff up, it's imperative we prioritize what gets built.

If [Anthropic](https://fin.ai/customers/anthropic) themselves are willing to vouch for the value-add, I think it's silly to suggest that teams with budget and higher priorities should trade the time and focus to roll their own.

"Anthropic is using it so it must be valuable". I think I've heard this a lot before but with different companies.

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

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.

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

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.

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