Blogpost
Jan 22, 2026
Replacing BPO with Artificial Intelligence Contact Center: Architecture for a 5-Person Call Center Team
"We have 5 in-house contact center people and 20 offshore. We want to keep the 5", a client told me a month ago. I asked them what was wrong with the offshore team - and I was expecting the usual language barriers, accents, and time zone issues - but no.
What they actually gave me was a laundry list of service quality problems decreasing customer satisfaction:
Tickets that get closed without actually resolving the issue
Wrong macros being sent because the agents were working too fast and not taking the time to read properly
Responses that didn't even match the query
An overall tone with customers that was just plain wrong - especially when those customers were frustrated
This isn't a talent or training issue, by the way - it's a system problem. When BPO is run on a pay-per-resolution model, with handle time being the key metric, what do you get? Agents who skim, that's what.
When you own the contact center - you own the customer data, all customer conversations, and keep your best customer service reps happy.
What 20 Offshore Agents Actually Do (Or Don't)
Before we even think about replacing them, we need to break down the work they're doing:
Offshore deals with all three levels, but is rubbish at all of them:
Tier 0: Slower than a lookup - transcription errors. Why on earth are we sending human beings to do this kind of work?
Tier 1: They know the policies, but can't apply them consistently. This is the "wrong macro" problem - the right tool for the wrong situation.
Tier 2: They don't have the authority to do anything, so they just escalate or stall. And of course the in-house team has to clean up after them.
Your five in-house support people? Brilliant at Tier 2. They've got context, they can make calls, and they're wasted on "where's my order?". So how AI agents should inrease human agent productivity?
The Future Proof Architecture - How An Ideal System Would Work
In modern contact centers we use AI to handle Tier 0 and Tier 1. Not because it's cheaper - it's consistent. It reads the whole message, applies the right policy, and doesn't close tickets half way through because there's no incentive structure pushing it to.
When it gets a request that requires some real human judgment, it escalates with all the context it's got: the transcript, the customer history, and the reason for escalation. Think of it like small call routing or automatic call distribution, but only when needed. And, if needed, AI analyzes customer history, real-time sentiment, and intent for pairing callers with appropriate agents.
The five in-house people do two main things:
Sort out the tough cases - the things that actually need a human brain to sort out.
Make the system better - review AI outputs, flag up failures, and fine tune the agents so they get better and better.
This is the bit that most people get totally wrong. Your experts aren't just handling the overflow - they're the feedback loop. Every edge case they catch is a guardrail. Every wrong response they flag makes the AI prompts better. The AI gets better because your best people are teaching it.
BPO doesn't do this. You train the agents once, and then quality drifts. Then you retrain them.
With AI, improvement is continuous - and the people doing quality control are the same ones handling the tough cases - so they get to see what really matters.
What You Own
With offshore BPO, you get to own nothing - a few playbooks in their system, some recordings on their servers. Switch providers, start over.
With proprietary voice AI, you're renting. 15 cents a minute for the privilege of using their software. Can't inspect, can't modify, can't self host. They learn from your data.
With open-core infrastructure, you get to own it. Pipecat for orchestration, LangGraph for agent logic, your own database for conversations. Swap LLM providers without rewriting the whole system. Run it in your cloud - where you've got control.
The 20 offshore agents you used to have? Never yours. The benefit of contact center AI that replaces them? You can own it.
Why It Costs Less
The consistency & quality argument is stronger, yet I'll deep dive in the costs benchmarks in a separate article. For now just assume the following scenario:
Nominal Rate: $10.00 per hour.
Total Cost per 8-Hour Shift: $80.00.
Shrinkage (15%): 1.2 hours lost. Remaining Available Time: 6.8 hours.
Occupancy Target (75%): The agent is active for 75% of the remaining 6.8 hours.
Productive Hours: $6.8 \times 0.75 = 5.1$hours.
Productive Minutes: $5.1 \times 60 = 306 minutes.
Effective Cost = $80/306 minutes = $0.26 per minute
And only if the call volume is stable, which is not true - you pay more for imperfect workforce management efficiency
Add team leads (1 for every 15 agents), QA analyst, trainers, operations managers, you name it - it effectively goes more than $0.5 per minute.
Voice stack economics 101
Telephony (the commodity layer)
Speech-To-Text & Text-to-Speech (where you pick from Deepgram, Cartesia, Elevenlabs)
LLM (that you optimize with great AI Engineering)
Platform orchestration
Without SaaS margins it should add up to ~$0.04 – $0.1 per minute.
The Point
BPO solves volume by throwing more people at it. Cheaper labour, same process.
The problem is the process. Scripts, macros, handle time metrics - built in because you can't trust 20 remote agents to make good judgment calls. AI doesn't need all that. It's got the policies in context. It composes responses instead of just pulling from a template. Latency? Milliseconds, not minutes.
So... to sum up - AI-powered contact centers can resolve basic customer issues while allowing human agents to focus on more complex interactions. Five people who handle the tough stuff, a bit of AI platform to handle the rest, and a feedback loop that makes it all better over time.
SaaS solves many of these problems - but charge premium overheads and you can't control your data.
If you want to build the control plane for inhouse contact center ai - let's talk!
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