NuPlay runs AI agents for enterprises in production that improve after every run. Deploy agents for retail, customer service, consumer goods etc., across voice, chat, and email, connected to your existing systems. NuPlay is built as one platform, has 5 layers which build, run and improve the agents: NuPro, NuStack, NuContext, Nupulse and NuLoop.
Hi Product Hunt,
I'm hunting NuPlay today.
Most enterprise AI projects follow the same arc: a promising pilot, a launch, and then slow decay. Policies change, products change, customers ask new questions, and the agent stays exactly as it was shipped. NuPlay is built around the opposite idea. It runs enterprise workflows in production and improves them after every run,
with the agents, the systems they operate and the context they draw on in one platform.
The platform comes together in five parts:
NuPro is where you build the agents. You set behaviour, personality, speech, call handling, privacy and post-call analytics, then simulate the agent before you deploy it. Agents work across voice, chat, email and messaging, in support, sales, HR, vendor operations and marketing.
NuStack makes your systems agent-ready. Connect Salesforce, SAP, Snowflake, Zendesk or ServiceNow, map the business objects (customers, orders, invoices, shipments), and design the end-to-end workflow on a canvas: intent, lookup, business rules, response.
NuContext gives every agent the same picture of the business. It maps your sources, pipelines, tables, metrics and policies into one governed context graph, with catalog and lineage, so agents answer from your data and inside your rules.
NuLoop is the part I find most interesting. It scores every run, surfaces the real problem (for example, one in three callers hanging up before they hear the offer), diagnoses the cause, drafts a fix and tests it against past runs. Nothing ships until someone on your team reviews and approves it.
NuPulse shows what is happening across every agent: call volume, connection rates, handling time. You can ask its assistant to list the worst-performing calls or run a cause analysis instead of digging through dashboards.
If you run high-volume support, sales or operations workflows and have watched an AI pilot stall after launch, this is worth a look. The NuPlay team is here all day to answer questions.
self-improving seems really interesting! can you tell me if it can work with our existing systems?
Nuplay has already established as a conversational platform handling voice/text/email agents at scale - this would be the foray also into non-conversational agents and will close the enterprise auto pilot for our customers. Look out for deep capabilities coming on this platform!
Spent the last few months working on NuLoop - Self Improving Layer for AI Agents. Building agents isn’t the hard part anymore. The real challenge is, how do you maintain and improve them at scale? New cases every day, the same mistakes popping up again and again. There’s no way to keep up with that manually. That’s what NuLoop solves. Agents that learn from their own conversations, heal what’s breaking and keep optimising themselves over time towards their business goal.
I’m on the NuPlay team, working on AI transformation projects with enterprises. Most of our work starts in the same place: a process that still runs on spreadsheets, email threads and people copying data between systems. We don’t bolt a chatbot on top. We rethink the process from scratch, work out what can be fully automated and where AI fits, and keep humans on the decisions that need judgment.
The goal is a 10x jump in productivity for the people doing the work, not a 10% saving. NuPlay is what makes that stick: the agents run inside the actual systems (CRM, ERP, ticketing) and get better after every run, so the gains don’t fade once the project ends.
Curious: which manual process in your company do you think is most overdue for this?
Currently working at Nuplay, where our agent handles customer care support for real life customers WISMO, refund, tracking, returns, exchange, all of it - seamlessly by fetching the exact details of the customer, with all the context already built in so they don't have to specify anything! So exciting to see the context layer, the self improving layer and watching its magic play into real, production impact!
Can I deploy chat agent on my shopify website to help my users to shop better, basically give recommendations and if they drop-off or purchase, can a voice ai agent reach out to upsell/take feedback,etc.?
Testing a proposed fix against past runs is the part I'd look at closely. Seeing which cases got better and which got worse would make that approval much easier.
let's gooooooo
Part of the NuPlay team, focused on retail.
In retail, the business changes faster than any agent you ship: product prices, discount and promotions, HR-IT enterprise policies and loyalty rules move every week. So we built NuPlay where agents, systems and context sit on one platform that gets better after every run.
Proud of the Work, Proud of the team & Proud of the platform we have built.
For all the scifi readers here, Asimov wrote about robots following rules. Feels like we’re slowly moving into the next version of that idea, systems that don’t just follow instructions, but actually learn from what happens after they’re deployed.
That’s what I find interesting about NuPlay. The agent runs, screws up, learns, gets better, and keeps adapting.
Still very early, but this is only getting better. Feels like the only way is up from here.
Curious to see how far this can actually go, at what point does an AI agent stop feeling like a tool and start feeling like a real part of the business?
Would this tool tell us what content to be created for a set of questions that people generally ask in GPTs?
Excited to see NuPlay live on Product Hunt! 🚀 The whole concept of building AI agents that don’t just run workflows but actually learn and improve from the outcomes in the real world is what's so compelling. Can't wait to see how NuLoop enables teams to go from just deploying AI agents to actually making them more robust and effective in production.
Wow, this is so amazing.
I work on Nuloop at Nuplay, a system designed to continuously improve AI agents in production. While most agents require ongoing manual support to fix failures, refine prompts, and test changes, Nuloop automates this improvement cycle. It detects agent failures, automatically optimizes prompts, validates the changes, and publishes improvements with minimal human intervention. This enables agents to continuously improve, resolve issues, and adapt proactively—even before users notice something is wrong.
My favourite part of this job has been learning to explain one idea simply: most AI agents are at their best on the day they ship, and NuPlay is built so they get better after that. Every run feeds back into the agent, so it keeps up as policies, products and customer questions change.
About NuPlay AI: It runs enterprise-grade AI agents in production and improves them after every run.
Most AI projects follow the same arc: a promising pilot, a launch, then slow decay. The agent was trained on how the business looked on launch day, and everything after that becomes manual patching. NuPlay closes that gap. Each run is a signal: what worked, what didn’t, what’s changed. That learning goes straight back into the agent, so it gets sharper the more it works.
It’s all on one platform. The agents, the systems they operate in, and the context they draw on live in the same place, so improvements don’t get lost between tools. Deploy an agent, and it runs, learns and improves without a rebuild every time something changes.
Key features:
Agents that improve after every run, powered by NuLoop, our self-improvement layer
Agents, systems and context unified in one platform
Production-ready agents for retail, consumer services, consumer goods and more
Adapts to new policies, products and customer questions without manual rework
Would love your honest take: where have AI agents broken down for you after launch? We’re here all day to answer questions 🙌
About NuPlay on Product Hunt
“AI transformation, run on one self-improving platform”
NuPlay launched on Product Hunt on October 27th, 2026 and earned 111 upvotes and 25 comments, placing #15 on the daily leaderboard. NuPlay runs AI agents for enterprises in production that improve after every run. Deploy agents for retail, customer service, consumer goods etc., across voice, chat, and email, connected to your existing systems. NuPlay is built as one platform, has 5 layers which build, run and improve the agents: NuPro, NuStack, NuContext, Nupulse and NuLoop.
NuPlay was featured in Customer Success (6.3k followers), SaaS (45.1k followers) and Artificial Intelligence (482.4k followers) on Product Hunt. Together, these topics include over 196.3k products, making this a competitive space to launch in.
Who hunted NuPlay?
NuPlay was hunted by Nikunj Thakkar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how NuPlay stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Nikunj Thakkar
