AI QA and agent assist for BPOs, delivered under your own name. Your clients now read about support AI that runs without an outsourced team. We put a senior engineer into one of your client programs to build AI QA scoring and agent assist against your rubric, so you can show the results before the client asks.
Bring your QA rubric and one queue's calibration scores.
Published results in BPO firms
A contact center that scaled AI QA with an implementation partner, and two results your clients are reading.
Empower, which takes about 10 million calls a year, increased the share of calls that get a quality review 20-fold with AI scoring.
Seven months from experiment to production. Accenture's engineers wrote prompts from Empower's scoring criteria, tested them with its quality analysts, integrated the existing stack, and ran change management.
Decagon deflects nearly 90% of Tremendous's reward-recipient support, with CSAT of 92% against 86% for its outsourced team.
About 30 days to roll out chat and email. The company is moving from roughly 18 outsourced agents to a smaller internal team.
Klarna estimated its AI assistant did the work of 700 full-time agents in its first month.
Fifteen months later the CEO said quality had suffered and Klarna would hire more human agents. The hand-off between AI and people decides whether savings hold.
Figures as reported by the vendor or customer at the linked source. They are not OpenNash results.
Where our engineer comes in
Empower got 20 times the QA coverage, and it took seven months and Accenture's engineers working with its quality analysts. Tremendous is moving support from a BPO to an internal team with AI. A BPO that runs both the AI and the people has a strong answer to both stories. Our engineer builds that answer in one client program, with access scoped to that client and a seat in your weekly calibration.
| Decision | What we look at | Outcome |
|---|---|---|
| Which client program first | QA sample rate, wrap-up time, handle time, and the client's renewal date. | A program where results will show before renewal. |
| Client's platform or your own stack | The AI already in the client's stack, its cost, and what it does to your margin. | Use the client's platform where they insist. Build your own where you want to own the service. |
| How client data stays separate | Credentials, retrieval indexes, logs, and model accounts per client. | Cross-client access tests that must fail before launch, reviewed with your security team. |
| What to sell | Which pieces can be priced as a service line under your brand. | You own what we build and can resell it. |
Why forward-deployed engineers
Built for their largest accounts, deploying their own models. OpenAI's deployment company launched with more than $4B. ↗
Configure their own product, priced per seat or per resolution.
A small senior team, ex-Palantir, Meta, and Databricks. Any model, any vendor. Free pilot, then a flat monthly fee.
Even OpenAI closes the gap with engineers in the room. Source: OpenAI, What FDE means at OpenAI, Oct 2026.
The first eight weeks
Written down with your team in week 1. The pilot is free, and it has to pass these before anything goes further.
Example acceptance tests, not results from a customer deployment. Yours are written with your team.
Systems we work in
Rules from day one
FAQ
Yes. You own what we build, and the workflows and dashboards can run under your brand.
It changes their work. QA analysts move from sampling to coaching, and agents spend less time on wrap-up. Headcount decisions stay with you.
We work with it. The QA, hand-off, and reporting around it are usually where a BPO adds the most.
Why you need one
A senior engineer joins your team on a laptop you issue, tests the options on your data, and puts one BPO firms workflow into production in 4 to 8 weeks. It starts with a free 14-day pilot judged against test cases you agree in writing.
After launch, we run and improve the workflow on a flat monthly fee, or your team takes it over. The code, tests, and runbook are yours either way.
Bring your QA rubric and one queue's calibration scores.