Every BPO now claims to be AI-first. Very few can answer the only question that matters: what exactly runs in production, on whose calls, today?
This post is our attempt to answer that question for our own operation, in enough detail that you can use it as a benchmark when you evaluate anyone, including us.
Our operating model splits every workflow into two parts. AI leads up to 90 percent of it: routing, retrieval, documentation, scoring, follow-ups, the repetitive substrate of customer operations. Human talent owns the remaining 10 percent, which is where the hard things live: a customer on the edge of cancelling, a collections conversation that needs dignity, a complaint that could become a regulatory issue.
The percentages are less important than the principle. Automation should remove work from people, and the people who remain should be doing work that genuinely needs them. A contact centre where agents copy-paste between five systems has failed that test regardless of how many AI logos are in the sales deck.
Full-stack CCaaS across channels. Aurexion, our CX and knowledge management platform, carries voice, chat, email and social in one queue with skill-based routing, IVR self-service and API integrations into whatever CRM or ERP the client already runs. It is cloud-hosted, quick to stand up, and carries a 96.5 percent uptime SLA with failover.
Audit AI on 100 percent of interactions. A human QA team samples 2 to 5 percent of calls, days after they happen. Our audit layer scores every single interaction in one pass: sentiment, intent, script and compliance adherence. Full coverage means the outlier call that would have triggered a complaint gets flagged the same day, not discovered in next month’s dispute. It also removes most of the cost of a traditional QA function, roughly 80 percent in our deployments.
Agent assist during the conversation. Live retrieval from the knowledge base, next-step nudges, and automatic after-call documentation. The measurable effects are shorter handle times and much faster ramp for new agents, because the system carries the product knowledge a six-month veteran would otherwise hold in their head.
Outbound with compliance built in. Vaani, our automated dialer, manages pacing, retry logic and calling-window compliance rather than leaving those to agent discretion. For field operations, FleeTrack handles task assignment, live location and attendance for on-ground staff.
The unglamorous middle. Robotic process automation and intelligent document processing clear the back-office work that surrounds every contact centre: form processing, data entry between systems, email triage. This is rarely demo material and it is frequently where the largest savings sit.
We run transformations in four phases rather than switching everything on at once.
First, assess and map: AI-driven process mining goes through the actual workflows and finds where the automation opportunities and failure points are, while consultants set priorities and success metrics with the client. Second, automate and deploy: bots, document processing and RPA take over routine work end to end, with humans designing the business logic and the governance guardrails. Third, augment and scale: agent assist, live nudges and knowledge retrieval switch on for the conversations that still reach people. Fourth, optimise and govern: models get retrained as accuracy drifts, and senior QA audits the outcomes. That last phase never ends, and any vendor who does not talk about model drift has not run this in production for long.
If you are deciding how to get these capabilities, you have three honest options.
Building in house gives you control and takes 12 to 24 months, a data engineering team, and ongoing model operations. It makes sense for very large operations where CX is the product itself.
Buying software (a CCaaS platform plus point AI tools) is faster, but you still have to integrate the stack, run the operation and manage the agents. Tool sprawl is the usual failure mode: five products, five dashboards, no owner.
Outsourcing to an AI-led operator gets you the stack and the operation together, with one throat to choke and pricing that can be tied to outcomes. The trade-off is dependence on the partner, which is why exit terms and data ownership belong in the contract from day one.
There is no universally right answer, but there is a common wrong one: buying tools without deciding who owns the outcome.
Ask what percentage of interactions are QA-scored today, on a named client. Ask for the before-and-after handle time on an account where agent assist went live. Ask how many of their production deployments use their own platforms versus reselling someone else’s. Ask what happened the last time a model’s accuracy drifted and who caught it. And ask to sit with an agent for thirty minutes and watch the tools in use. That last one is the fastest tell there is: floors do not lie.
EOSGlobe spent its first decade as a traditional outsourcer and its second becoming something else: an AI transformation partner that happens to run operations, rather than an operations vendor that happens to mention AI. The 90/10 model is not a marketing line for us; it is how our floors are built, on our own stack (Aurexion, Vaani, FleeTrack, plus RPA and document processing through eDAS, our digital arm), with a 14,000 plus person team across ten delivery centres serving banking, insurance, healthcare, e-commerce and quick commerce. We are happy to be evaluated against every question in the section above, live, on the floor.
If your contact centre is due for this conversation, write to enquiry@eosglobe.com and ask for a working demo on your own call recordings rather than ours.