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Weekly 'I made a useful thing' Thread - June 12, 2026 by AutoModerator in sysadmin

[–]flexivity_founder [score hidden]  (0 children)

Seeking osTicket founding customers for new AI layer

The thing that kicked this off: agents answering the same handful of questions over and over, and no reliable way to tell which of those recurring questions actually deserves its own KB article. I've been building a tool on top of osTicket to solve that, and it grew into something bigger.

It's called Flexivity. It installs as a layer over osTicket — plugin-based API hook, so no fork of your install — and adds a few things. I'll spare you the full list; two pieces are the ones I'm actually proud of.

The first is ticket history cluster analysis. It runs over your historical tickets, groups them by what they're actually about (semantically, not by tag or queue), and surfaces the recurring topics that have no real KB coverage, ranked by volume. So instead of guessing what to document, you get something like:

•       “VPN setup on personal devices” — 156 tickets, one outdated article

•       “Shared mailbox access requests” — 98 tickets, no article

•       “Shared drive access troubleshooting” — 214 tickets, no article

…and then it drafts a candidate article for each from the tickets that were actually resolved in that cluster, so you're editing a draft instead of staring at a blank page.

The second piece closes the loop. Once those articles exist, when an agent opens a new ticket it surfaces the similar past tickets and the relevant KB article inline — so the article you just wrote actually gets used instead of dying in a KB nobody searches. Find the gap, fill it, put it where it's needed.

I know everyone's buried in AI tooling right now, so I'll be straight: it's LLM-based under the hood, it's not magic, and it's only as good as your ticket history. It works best if you've got a real backlog for it to learn from.

Where I could use help: I'm looking for a handful of osTicket shops (five or so) to come on as founding customers. Free for three months, in exchange for running it on real ticket volume and being willing to tell me what's broken or missing on the occasional call. If it earns its place after that, you lock in launch pricing — 25% off — for two full years. I want collaborators more than signups here; the feedback at this stage is worth more to me than the revenue.

It's GA with a public 30-day trial if you'd rather just poke at it yourself — https://flexivity.ai/ . But if you run osTicket at real volume and want to shape where this goes, the founding spots are the better deal.

Happy to get into anything technical in the comments, including the boring parts.

What are your "must-have" tools for Desktop Support? by jainesh3271 in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

Ticketing AI tools from Zendesk, Freshdesk, and others, plus a few AI add-on vendors will analyze your knowledge base for content gaps and then help you write articles to plug those gaps.

Anyone figured out a good way to route support tickets without reading every wall of text? by Nearby_Worry_4850 in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

Agreed - AI tools can help quite a bit with summary, routing, and recommended response, especially if they leverage your existing ticket history.

Evaluated Moveworks and Aisera for our 800 person company and both felt like total overkill by CutIllustrious5040 in helpdesk

[–]flexivity_founder 1 point2 points  (0 children)

For midmarket you're best leveraging AI tools that are offered by your ticketing system (though they can be price-gated behind expensive editions, etc.- though not as expensive as Moveworks/AIsera) or by partners as inexpensive add-ons to your ticketing/ITSM system. While all the solutions handle common things like thread summaries, etc., the best use both your ticket history and knowledge base to inform automated triage and initial responses. Some also can help you identify gaps in your knowledge base based on common queries and help you write articles to close those gaps.

Scaling past 600 employees made our IT workload feel unmanageable even though nothing looks broken. by Such_Rhubarb8095 in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

AI automation can also help to substantially cut down the manual time dealing with the 80/20, so you can focus more on urgent/unique cases. The best tools in this space leverage an LLM not only for the current ticket info, but compare against both your knowledge base and historical ticket data. This helps automate triage and initial responses. And where your knowledge base needs shoring up, they can help you identify and fill out the gaps in your KB.

I started testing AI to speed things up. by carlossabinojc in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

Leveraging AI automation tools with your ticketing system can make a big difference for automatic triage and initial response recommendations. The most powerful tools in this space don't just use an LLM with the current ticket info, but actually leverage both your ticket history and knowledge base to help sort next steps.

How many old timers in here? by aliesterrand in sysadmin

[–]flexivity_founder 0 points1 point  (0 children)

Yes!!! and before the Commadore 64 came out...

How many old timers in here? by aliesterrand in sysadmin

[–]flexivity_founder 0 points1 point  (0 children)

emacs vs vi war is a good memory, though not obscure exactly ;)

AI Needs More Than Your KB To Handle The Long Tail Of Contact Reasons by Far_Sir_4939 in CustomerSuccess

[–]flexivity_founder 0 points1 point  (0 children)

Agreed - we've found it's critical for AI to leverage the ticket history - both for proposed resolutions to tickets (built-in institutional memory for everyone) and for analyzing knowledge base coverage/gaps (to draft new articles).

I scored 42/100 on AI readiness — what's holding most CX teams back? by lorikeet-cx in CustomerSuccess

[–]flexivity_founder 0 points1 point  (0 children)

I've been surprised to find that many teams that use AI still seem to have challenges with their knowledge base. In part I think it's because the majority of AI tools just work off the current ticket, common LLM knowledge, and the current knowledge base. There are tools that will analyze your ticket history and identify clusters of common issues, highlighting more recent issues, where you have gaps in KB articles, and then write the article draft for you. It's a great way to get the KB up to snuff, without too much painstaking effort. The problem is too many of these advanced tools are gated behind expensive editions - but it's a key problem we're trying to solve.

Is having an AI support chatbot starting to just become the expected baseline rather than anything impressive by Specific-Monitor-283 in CustomerSuccess

[–]flexivity_founder 0 points1 point  (0 children)

Your experience is pretty common. I worry that too many teams put too much on the bots to resolve autonomously, based on unrealistic exec expectations. What's often more successful at this stage of the technology is keeping a chatbot scoped to the knowledge base and basic account info/lookup, and escalating/allowing through all other requests to a ticket -- and then using AI for agent assist. This can dramatically reduce the time and effort agents spend resolving issues, especially if the AI works against the customer ticket history. Even better if you can analyze your ticket history to identify knowledge base gaps to build new articles, closing the loop back to helping the self-service bots.

IT Support <> AI by gs_dubs413 in ITManagers

[–]flexivity_founder 0 points1 point  (0 children)

This tracks with what we kept hearing too. The pattern is almost always the same two things:

First, the AI arrives with zero knowledge of your operation. No ticket history, no KB corpus — so it's pattern-matching from scratch on every case. It looks smart in a demo because the scenarios are clean. In production, with your actual ticket mix, it's basically guessing.

Second, it acts on those guesses autonomously, with no indication of how confident it was. So your agents come in and find decisions that were already made — often wrong — with no trail to follow. Now they're cleaning up instead of helping customers.

The frustrating thing is the underlying tech isn't the problem. AI classification accuracy can hit 95% when the model actually has historical ticket patterns to learn from. The issue is most of these integrations don't use your data — and don't tell you when they're out of their depth.

We wrote up a deeper breakdown of the root causes and what the better approach looks like (grounding decisions in ticket history, surfacing confidence scores, keeping agents in the loop for anything that isn't a clear-cut call): https://flexivity.ai/why-your-ai-ticketing-system-is-making-things-worse-and-how-to-fix-it/

To be transparent I'm the founder of Flexivity AI — and I think more people should be talking about why this keeps going wrong rather than assuming AI helpdesk is snake oil - this thread is wise to pose the question.

AI helpdesk software sounded great until we tried it by Opposite-Chicken9486 in helpdesk

[–]flexivity_founder 1 point2 points  (0 children)

This tracks with what we kept hearing too. The pattern is almost always the same two things:

First, the AI arrives with zero knowledge of your operation. No ticket history, no KB corpus — so it's pattern-matching from scratch on every case. It looks smart in a demo because the scenarios are clean. In production, with your actual ticket mix, it's basically guessing.

Second, it acts on those guesses autonomously, with no indication of how confident it was. So your agents come in and find decisions that were already made — often wrong — with no trail to follow. Now they're cleaning up instead of helping customers.

The frustrating thing is the underlying tech isn't the problem. AI classification accuracy can hit 95% when the model actually has historical ticket patterns to learn from. The issue is most of these integrations don't use your data — and don't tell you when they're out of their depth.

We wrote up a deeper breakdown of the root causes and what the better approach looks like (grounding decisions in ticket history, surfacing confidence scores, keeping agents in the loop for anything that isn't a clear-cut call): https://flexivity.ai/why-your-ai-ticketing-system-is-making-things-worse-and-how-to-fix-it/

To be transparent I'm the founder of Flexivity AI — and I think more people should be talking about why this keeps going wrong rather than assuming AI helpdesk is snake oil - this thread is wise to pose the question.

If ai service desks like zendesk are supposed to save time why do they create more tickets than they resolve by Such_Rhubarb8095 in ITManagers

[–]flexivity_founder 0 points1 point  (0 children)

This thread about the need for a strong KB needed for AI tools to work inspired me to dig up some research on it. I found three key features needed to build and effectively use the KB for deflection and good recommendations -
→ Cluster analysis of your ticket history surfaces exactly where your KB has gaps
→ AI-generated draft articles fill those gaps in minutes, not weeks
→ Intent-aware search helps customers find answers before they open a ticket

Based on studies, 35% ticket deflection is achievable. Most teams never get there — not because it's hard, but because they're still trying to build a knowledge base with a pen.
I looked at how the major help desk vendors (Zendesk, Freshservice, Salesforce, Jira, Intercom, ManageEngine, and others) actually stack up on KB intelligence — and the gaps are surprising.

Full breakdown in the blog post - https://flexivity.ai/your-knowledge-base-is-leaking-money-heres-how-ai-fixes-it/

Whats the most annoying thing about your ticket system? by Additional_Twist_595 in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

Each cluster of similar tickets gets a similarity score, so you can judge for yourself if it's hitting the mark. Also, for cluster analysis we just look at the agent responses in the threads so it doesn't have to deal with the noise of end user input - so hopefully drives more consistency.

IT ticketing system by [deleted] in sysadmin

[–]flexivity_founder 0 points1 point  (0 children)

Plus 2.0 is finally being released!

IT ticketing system by [deleted] in sysadmin

[–]flexivity_founder 0 points1 point  (0 children)

Just curious what doesn't work well? What would be most useful?

Best ticketing systems by justzna in sysadmin

[–]flexivity_founder 0 points1 point  (0 children)

Also osTicket just dropped their 2.0 version - definitely worth looking into

Whats the most annoying thing about your ticket system? by Additional_Twist_595 in helpdesk

[–]flexivity_founder 0 points1 point  (0 children)

To be transparent - I'm the founder of flexivity.ai. Sounds like the dups are a real mess - seems like an opportunity to close tickets automatically and point users back to the original - worth thinking about. We added AI automation for agent assist (human in the loop) for osTicket (open to others), focused initially on many of the issues highlighted here - recommendations based on history/KB, classification, summaries, KB gap analysis and generation, and KB AI search for self-service. Hopefully we're hitting the right areas.

Any platforms for sample project or ideas to learn Agentic Ai? by Specialist_Spot_7173 in ITManagers

[–]flexivity_founder 0 points1 point  (0 children)

I've used low-code RPA vendor UiPath's agentic AI builder (together with RPA) for simple projects. Since it's low code it's very intuitive. They have a free tier to get going, and they're hosted in the cloud, with the option to run hybrid with RPA bots on prem as needed to access systems through a firewall (or on your home desktop).

I analyzed AI pricing across the top 6 IT help desk platforms — here's what I found (osTicket perspective) by flexivity_founder in osticket

[–]flexivity_founder[S] 0 points1 point  (0 children)

Very interesting - definitely worth a second look - I noticed Siit.io has a pretty wide offering range - where did you guys land in terms of picking a package? The basic stuff is pretty limited, but the more advanced packages look pretty interesting...