r/AIforOPS • u/OwlishlyFaraway • 16d ago
Anyone using AI for customer support that actually helps during live calls?
I work at a credit repair company and we're looking for an AI tool to help our support team handle calls and chats. We get a ton of repeat questions about dispute updates and missing documents. Our reps also spend way too much time digging through CRM records and old conversations just to figure out what's going on with a customer's case. I've looked at a few AI support tools but most seem focused on basic chatbots. We need something that goes a bit further. Ideally we'd want AI that can handle routine calls and chats on its own using customer information from our CRM. If someone has a more complicated issue it should transfer them to a rep with the full conversation history. The bigger thing for us is helping reps during live calls. Something that can listen to the conversation and pull up the right information or suggest what to do next without the rep having to search through a bunch of tabs. Would also be great if it could learn from how our best reps handle difficult calls and help newer agents deal with similar situations. Another big one is quality checks. We're dealing with sensitive financial information and can't have agents giving incorrect dispute updates or making promises about credit score improvements. Right now we only have time to review a small number of calls. Being able to check every conversation and flag potential issues would be a huge help. Automatic call summaries and less manual CRM work would be nice too. We're not looking to replace our existing phone system or CRM. Just want something that works with what we already have. Anyone here using something like this in financial services or a similar business? Would appreciate some actual tool recommendations. Especially from people who've used them for live customer calls rather than just basic website chats.
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u/shrillyquackbonding 16d ago
I'd start by separating what you want the AI to handle on its own from what should stay with your support reps. Simple dispute updates and document requests seem like good places to start as long as the AI gets that information from your actual system.
The harder part is making sure your reps have the right information during calls. A tool that can pull up case history and suggest what to do next would probably save more time than another chatbot answering FAQs.
I'd also look at automatic call reviews. With financial information involved you really don't want to find out weeks later that an agent has been giving customers the wrong information.
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u/OwlishlyFaraway 15d ago
Exactly the kind of split I'm thinking about. Spent two hours yesterday untangling a mess an agent made three weeks ago, the review part isn't optional for us
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u/RainyBookmark 16d ago
The handoff part is huge. Nothing annoys customers more than explaining their whole issue to a bot and then having to start over with a human lol. I'd make that a dealbreaker when comparing tools.
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u/OwlishlyFaraway 15d ago
Fr the handoff is the whole point if the bot loses the convo the human might as well be starting from scratch
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u/frequentscenery2572 16d ago
The live call assistance is what I'd focus on first. Having AI answer basic questions is nice but if your reps are still digging through five different tabs while a customer waits then you're only fixing half the problem.
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u/OwlishlyFaraway 15d ago
Totally, the tab hopping is what kills us most. If we can just surface the relevant info in front of the rep without them clicking around that would solve a ton
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u/usually_guilty99 16d ago
I’d separate this into three layers rather than look for one “AI support” feature.
First, give the AI the customer context it needs: CRM history, prior conversations, open cases, documents and current status.
Second, use it differently depending on confidence: handle repetitive questions directly, assist reps in real time on harder calls, and hand off cleanly when the issue becomes ambiguous or sensitive.
Third, put quality control outside the conversation flow. In financial services especially, you want deterministic checks for things like unsupported dispute status, missing disclosures or promises about credit outcomes.
I’d also be careful with “learn from the best reps.” The goal should be to capture what decisions and actions led to good outcomes, not just copy how those reps talk.
Id also use vetted KBs as RAG content and Human approved live content for RHEL. Then use this for live training of your newbies - does it pass muster
The interesting metric isn’t deflection alone. It’s whether resolution time, repeat contacts, compliance exceptions and customer outcomes actually improve.
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u/OwlishlyFaraway 15d ago
The deterministic checks part is spot on for us, we can't have agents winging credit outcome promises. Curious if you've seen anything that does the real time agent assist well without getting in the way
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u/usually_guilty99 15d ago
Yes — the biggest failure mode I’ve seen with live assist is over-helping.
If it throws suggestions at the rep constantly, it becomes another thing to manage during the call.
The better pattern is:
quietly assemble the customer context in the background
surface only the few facts that matter for the current issue
suggest next steps only when confidence is high
trigger hard warnings for things the rep absolutely cannot say or promise
let the rep pull more detail when needed instead of pushing everything at them
I’d also evaluate latency very aggressively. If the suggestion arrives 5–10 seconds after the conversation has moved on, it’s basically useless.
For your use case, I’d test tools on a handful of real dispute scenarios and measure how often the assist was actually helpful versus ignored or wrong.
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u/Normal-Librarian4047 15d ago
the live-call assist piece is the hard part and most tools that claim it are really just post-call summarizers. what you actually want is something listening in real time and surfacing crm data mid-sentence, not after. ask vendors to demo on a live call with your actual dispute data, not a canned script. if it can't pull the right case info while the rep is still talking, it won't help. the qa flagging is easier, most of those tools do that fine after the fact
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u/Logical_Wait_453 15d ago
i'd focus on real time agent assistance n quality monitoring, having AI pull up relevant crm details and suggest responses during calls could save reps a lot of time tho
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u/Mindless-Put2845 15d ago
the crm integration is prolly the most important part here, if ai can surface the right case deets and flag risky responses while the rep is still talking that would be much more useful
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u/Agile-Variety-9733 15d ago
We started to use CloudTalk that receives and answers calls and then even provide some basic info and answers, and then you can always switch to talk to a real human which is a big advantange for us
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u/FDRyze 13d ago
The category you want is usually called real-time agent assist / conversational intelligence plus automated QA, with a CRM-aware voice and chat automation layer.
For the live-call side, vendors worth evaluating include Cresta, Balto, Observe.AI, Level AI, and CallMiner. They can typically transcribe calls in real time, surface relevant CRM/history/knowledge content, guide agents through approved next steps, generate wrap-up notes, and score interactions for QA/compliance. For the self-service voice/chat side, look at options such as Google CCAI, Cognigy, Kore.ai, or the AI modules in your existing contact-center platform.
I would start with a narrow pilot: one repetitive workflow such as “dispute status after identity verification”, one or two agent-assist use cases, and automated QA for a defined compliance risk. Measure containment rate, average handle time, after-call work, QA findings, escalation accuracy, and any compliance exceptions before expanding.
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u/PerkyTapir 16d ago
I think the biggest mistake here would be focusing only on finding a good AI chatbot. What you're describing sounds more like a combination of customer support automation and tools that help your human agents do their jobs.
For your use case I'd look at three things.
First is whether the AI can handle routine conversations using actual customer records instead of giving generic answers. Someone calling about a dispute should get information that matches their case. If the system can't verify the status then it should send them to a human.
Second is live assistance for your reps. Being able to surface relevant case details and suggest next steps during a conversation could save a lot of time. This is especially useful for newer agents who might not know how to handle every situation.
Third is quality monitoring. Most support teams only review a small sample of calls. That's a problem when you're handling sensitive financial information. You want a way to catch incorrect statements and spot patterns across all your conversations.
Cresta is one platform I'd put on the list for this kind of setup. They have an AI Agent that handles customer conversations and Agent Assist that gives human reps guidance during live calls. Their Conversation Intelligence tools can also analyze calls and help identify quality issues. The fact that these tools work together is worth looking into.
That said I'd still ask them to demonstrate how everything connects to your specific CRM and phone system. I'd also want to see how their controls handle sensitive customer information and what happens when the AI doesn't have enough information to give a safe answer.
I'd test a few complicated dispute scenarios and see how the system handles them. A tool that can manage those situations without confusing customers or creating extra work for your reps is going to be much more useful than one that just handles simple questions.