Their AI handles everything. Ours was raised on retail.
A general-purpose model knows everything about geopolitics and nothing about your brand. Gladly's cluster trained on retail conversations, retail intents, and brand language — so it resolves the customer in front of it.
Iconic brands running on Gladly's AI cluster
A dozen models still don't know your customer.
The industry brag is model count — fifteen models wired into a constellation. But a constellation is a trick of distance; those stars only look connected. A cluster is the opposite: same cloud, real gravity, moving as one.
Trained on retail. Built to resolve it.
Not general web text. Not tickets scraped from every industry. A decade of retail conversations and retail intents. When training matches the job, resolution stops being a guess.
“Where's my order” shows up a dozen ways. A sizing question is usually a return waiting to happen. Gladly reads intent the way your best agent does.
Out of the box, Gladly resolves retail intents at a higher rate than any general-purpose model. Same reason every time: the training matches the task.
Every conversation adds signal. The cluster learns your brand, your customers, and your edge cases. A general model carries no memory of your business. This one does.
The right model for every moment. Automatically.
Every platform runs multiple models now — table stakes. The difference is what does the routing: a cluster that understands retail, or a general system guessing at it.
Order status runs on a fast model. A furious complaint runs on a heavier one that can reason through it. Routing happens in the moment, and the customer never feels the seam.
Tune for speed, cost, or accuracy by intent type. No model configuration, no provider names to manage — the orchestration layer handles the plumbing.
You see every decision. You tune every outcome.
“Trust us, our engineers handle it” is not an architecture. Every model decision is scored, checked, and open to inspection.
Automated QA checks tone, voice, policy, and hallucinations on every answer — in parallel, so quality costs no latency.
Low confidence escalates on its own. High confidence leaves an audit trail. Nothing ships from a black box.
Tell Gladly what a good resolution looks like. The evaluation layer works out how to get there as models shift underneath.
Always current. Never a migration.
Frontier today is baseline in a year. The point of a cluster is that you get the upgrades without ever running an upgrade project.
When a stronger model ships, Gladly evaluates it against your benchmarks before it touches a live conversation. The upgrade reaches you as better numbers.
The model layer is decoupled from the product layer. No retraining, no migration, no downtime. When the frontier moves, Gladly moves with it.
One retail brand, one cluster, and no model migrations.
1,300 customer conversations per month across email, chat, SMS & phone
4 team members supporting customers through fertility, pregnancy & postpartum
4.9/5 CSAT — driven by a relationship-first approach
Metropolis
“AI is only ever as good as the context it can access. Our Gladly AI performs well because it's operating inside a conversation-based architecture — the history, our integrations, all in one place. We didn't have a scaling problem. We had a data-architecture problem.”
Kimberly Spence
Manager, CX Tools & Enablement, Metropolis
Why are retail brands choosing Gladly's AI engine?
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faster time-to-performance vs. training a model from scratch
p99 latency across the cluster, voice included
automated resolution on retail intent out of the box
average CSAT lift vs. pre-Gladly baseline
reduction in cost per resolved conversation
Where Gladly Wins
Why are brands winning with Gladly?
The proof is in the numbers - and the brands behind them are ones you already know.
How Condé Nast unified and accelerated revenue for 18 iconic brands with one CX platform.
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Up within hours. Smarter every day.
Most AI rollouts take six months to ship and plateau six weeks after. Gladly inverts both. See for yourself.