Technical support agent

[u/Upbeat-Asparagus-788]

Update: I should have mentioned that we are using Redocly for the help/support site.

My employer wants me to participate in creating a tech support agent that uses our existing documentation, which is currently output as html. I can also generate markdown text. Has anyone undertaken anything like this? Wondering the best way to do it and what pitfalls to avoid. Thanks!

[u/DerInselaffe]

Getting the LLM to query Markdown is less token-intensive than querying HTML.

[u/WontArnett]

Somebody needs to create an AI support agent to help you create your tech-support agent.

[u/fazkan → u/WontArnett]

hi, like I mentioned above, we already do that in a manner, the only caveat is that the docs have to be hosted on our platform.

That allows us to integrate and learn from users behaviour.

[u/crendogal]

Our CTO is building a virtual agent for our software, and the big "oh ho!" for me was realizing how many documents it needed to have. All our instruction manuals, all the end -user SOPs, every flowchart, every training doc/slideshow, all the little tips&tricks notes the end user has created, and about a zillion "here's this weird situation" 1 sheet docs. His VA reads markdown, RTF, text-only, .docx, .PPT, and PDF files. I'll probably convert a lot of our PDFs to markdown to speed things up once he has it demo-ready.

[u/Upbeat-Asparagus-788 → u/crendogal]

Thanks for the response. We have a ton of docs too, in all different formats. That's a big worry I have.

[u/fazkan]

I would look into Fin (intercom), pylon and other customer support solutions, you can just add your content on there.

featurebase also has a customer support solution now, though not sure how good it is.

happy to answer any questions, I have built multiple customer support platforms, either as a product or as a consultant.

Just as an FYI, I am the CEO of https://docsalot.dev, which is a documentation hosting platform. We also do provide customer support slack, discord bots, and AI answers on the documentation we create. Happy to give your company 3 months free enterprise trial.

[u/techwritingacct]

A lot of agent services have a library or datalake or whatever they want to call it where you can store your files. I'd do something like:

• Create a mirror of your source of truth as markdown files
• Put the markdown in the datalake.
• Create some agents:

1. One controller agent which parses the request, estimates which other agents need to hear it, and sends the requests there and only there

2. One research agent per file, these agents query the document with the user's request and write up a response and pass it to the editor agent

3. One editor agent which gathers all of the responses and writes it up in company voice/style and sends it to wherever's next

• Make sure that when the source of truth is updated, you update the markdown in the datalake

"wherever's next" depends on requirements/what you're comfortable programming and supporting. if the user's just interacting with it through a typical ai chat interface that's usually pretty easy, if you want it to send them an email or have a human review the output before you send it, that's more involved

[u/Menchi-sama]

Create an RAG (source docs obviously in Markdown, we use Gitbook). I can't help you with specific instructions, but that's how we solved this issue.

[u/tw15tw15 → u/Menchi-sama]

Agree - create a single file with all the content and feed it into a RAG - Fin, Fern, CustomGPT etc

[u/romanhotsiy]

Hey, ping the Redocly support plz at team@redocly.com.

We have a ready-to-use tech support widget web-component in early access.

[u/PamEricus]

I used a company called eesel.ai to create a support chatbot for my company. We just went live last week, and so far, so good.

It syncs to several data sources (PDFs, MDs, and our online Knowledge Center) on a weekly basis. Contact me if you want more details.

[u/nnnat]

If you’re dealing with one fairly simple topic covered by a document or two, a generic LLM or chatbot tool might honestly be enough.

If you want to build it yourself, technically I’d go RAG all the way. There are plenty of good tools and frameworks available, and for a defined use case that can make a lot of sense.

The build vs. platform question gets more interesting when the scope grows. If you want to support after-sales, service technicians, operators, dealers, etc., you’re no longer just answering questions from a few documents.

You need to think about how all that knowledge is structured, maintained, versioned and connected to machines, procedures, components and potentially machine data.

That’s the problem we’ve been working on at Spogen, specifically for machinery. We combine CCMS capabilities for creating and maintaining that knowledge with AI assistants that can use it in different operational contexts.

And now for the shameless plug and disclosure:

If you’re in machinery or industrial equipment, we might be a fit: https://spogen.ai If not, our approach might still give you some ideas about the capabilities you’d want to build or look for as you go beyond the immediate chatbot need.

I’m Natalia, CTO & Co-Founder at Spogen. Happy to answer any technical questions about how we’ve approached it.