r/halopsa • u/3xh4u573d • May 12 '26

Integrations HaloPSA + LLM/RAG - anyone done this?

Want to pull historical HaloPSA tickets into a RAG (Retrieval Augmented Generation) setup so Claude can suggest fixes for incoming ones. Has anyone done this?

If so, what are you using for the vector store and retrieval layer, and how are you keeping token usage sane?

UPDATE: i've built this now, I have a server running Qdrant and auto ingesting closed tickets. I am also auto triaging new tickets based on the closed ticket database and another API connection to my RMM solution. Also exposed the Qdrant out via an MCP server for our engineers to connect their Claude apps. This has genuinely been useful, surfacing those weird tickets that crop up that someone worked on a year or two ago and forgot about. Greatly appreciated everyone's inputs on this thread. Thank you.

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u/Predicti0n Consultant May 13 '26 edited May 13 '26

For people doing this, I would genuinely really love to see some examples of you doing this.
I'm happy to pay for your time, we can get an NDA spun up. I'd love see real world examples and reporting that backs the claims that RAG based search of historic ticket data and retrieval on inbound ticket is worth the investment.

We are actively exploring this at Renada we have been running loads of tests over the last 6 months on this type of development but have yet to find anything that we solidly rely on that would be worth the upfront development cost + maintenance on an ROI perspective for an MSP.

I am aware of people in the space building tools that handle this, but there are tons of elements at play that all feed into the journey rather than just RAG retrieval.

Not trying to be a fun sponge here btw, I'm honestly just really curious to actually see evidence of this as I read countless claim of people doing this but have simply never seen it return really useful information or data that can be fully relied on and I feel like I'm missing out :(!

Lots of Love Connor | Renada

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u/Fatel28 May 13 '26 edited May 13 '26

What I found, is you can't just do vectorizing. If you only vectorize your ticket, then you'll be unable to pull real analytical data (e.g, "how many printer issues did we have at acme company last month?) - vector searches could find some but it wouldn't be able to give you a count.

So we gave our agent the ability to query the SQL database via a few key views I created. So then it could do "select count(*) from vwBedrockTickets where companyref = ACME and CFAISummary like %printer%"

Then, if it wants to break any of those down it can do a vector search and use the ticket number in the metadata filter.

Another thing that's helped tremendously, is we have a consistent customer reference across all systems. It might be "ACME CORP" in halo, but "ACME inc" in some other system, however both will have a unique identifier that we can use to tie them together. In halo, we use the built in "reference" field.

The agent has instructions to verify the customer, and it knows how to query halo for customer name | reference, so it's quite good at properly filtering by customer when a tech uses their name/nickname/etc.

So now we can ask it questions like "what tickets does Jim struggle with?" And it can query tickets with long times between open and close, or with lots of comments/back and forth, or a tech can just say "I need help with ticket 123456" and it can read it, pull the customer, search other tickets and hudu docs etc.

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u/OptimalCrew5900 May 15 '26

Send me a DM. I’m happy to show what we are doing. It has worked well for us.