UnleashX lets you hire AI employees like people - pick a role, connect your CRM and tools, set your rules, and hand over the work. Execute workflows end-to-end, with humans stepping in only when needed.
Forum Threads
How much should an autonomous AI employee be allowed to decide on its own?
This is the question we spend the most time on with every new customer at UnleashX.
One sales team wanted their AI employee to offer discounts on its own to close deals faster. Sounded fine until we asked, up to how much? Nobody had an answer. Their human reps had never had a written rule, they just knew from experience.
We ended up setting a limit: up to 40% on its own, anything above that goes to a manager. Deals closed faster, and managers only saw the ones that actually needed them.
Turns out giving AI autonomy forces you to write down rules your team has been following without ever saying them out loud.
Should AI agents have names?
Our AI employees have names. Peter is an SDR, Kavya sells insurance, Arjun is a loan officer, Priya handles renewals.
We did it because customers found it easier to talk about "Peter's pipeline" than "the lead qualification workflow." Teams started treating the agents like colleagues with a job, which made it easier to define what each one owns.
Why does every AI chatbot forget who you are?
You call a company, explain your issue to the bot, get transferred, and explain it again. Then you message them on WhatsApp the next day and start from zero.
The fix isn't a smarter model. It's shared memory: past interactions, decisions and customer context stored in one place, available to every agent and every human with permission to see it. When a customer says "can we pick up where we left off?", the next conversation should already know where that was.
This turned out to be one of the hardest parts of our platform to get right, especially with permissions.
Have you seen a company do this well? Which product made you feel remembered?
Support our launch - https://www.producthunt.com/prod...
Hi @kadyan_ajay it looks interesting, congrats on the launch.
What's the setup or training process ? How do we onboard ai employee specifically in our processes, since the same process vary from company to company
@kauzovich_anastasiya
The training process is as simple as you do for new hires.
Just give the defined SOPs, Playbook, ICP and define Guidelines on Do's and Don'ts for your business. You can also decide if you want approval or full autonomy.
The good thing??? No Attrition and no repetitive training when you need more people.
Please explore: https://unleashx.ai/ to know more.
@kauzovich_anastasiya Thanks, Anastasiya! That’s actually an important part of how we’ve built UnleashX. We don’t expect every company to follow the same process. During onboarding, we configure the AI employee around your specific workflows, business rules, knowledge base, tools and escalation requirements. We then test it against real scenarios and fine-tune it before it goes live.
The goal is to make the AI employee adapt to how your team works, not force your team to adapt to the AI. Is there a particular workflow you’re considering starting with?
unleashx.ai for more
What happens when a customer asks for a human to help, how does the handoff look like?
@nuseir_yassin1
Thats the differentiator- You can define your own rules if you want to give full autonomy or human approval with contextual hand-off with intelligent insights.
@nuseir_yassin1 Great question, Nuseir! The handoff is built into the workflow. If a customer asks for a human, or the AI employee reaches a situation that needs human judgment, it can escalate the conversation to the designated team member. The context and conversation history can go along with the handoff, so the customer doesn't have to start from scratch.
We also let businesses define when these handoffs should happen, rather than leaving it to guesswork. How seamless that transition is can make a big difference to the customer experience, don't you think?
Are there any specific AI employees for the dev and engineering teams, or is this more focused on the marketing, support and operations?
@roopreddy Good question, Roop! It's not limited to marketing, support, or operations. UnleashX is designed to support different teams across an organisation, including dev and engineering. You can also build custom AI employees around specific technical workflows and requirements. The idea is to build an AI workforce for the entire business, not just a few departments.
@roopreddy
Right now the pre-build teams are more focused on marketing, sales, support and business ops.
Explore : https://unleashx.ai/
I think the connectors and integrations are going to be useful. Does in connect with all popular CRMs like Zoho and Salesforce??
@angelo_bram Absolutely, Angelo!
UnleashX supports 100+ integrations, including popular CRMs like Zoho and Salesforce. This allows AI employees to connect with your existing tools, access relevant data, and execute actions across your business workflows without disrupting your current tech stack. Is there a particular CRM workflow you'd like to automate?
I would love to have an ai employee handle appointment reminders, follow ups and basic client queries while i focus on more important work. Unleash sounds like a painkiller product xD
@hiteshi_soni
Haha, “painkiller” might be the best compliment we’ve had so far 😄
What kind of clients do you work with? Please explore and signup on UnleashX, our team will reach out to help you set it up for your clients. 🙌
@hiteshi_soni Hiteshi, that's exactly the kind of work an AI employee should take off your plate! 😄 Appointment reminders, follow-ups and routine queries are great starting points. You get more time to focus on the work that actually needs you. What kind of business are you in?
Congrats on the launch. Can you give me an example of an exception handling case? Like a scenario where the AI employees reliably handled the exceptions.
@iamanantgupta Thanks, Anant! Here's a simple example: imagine an AI employee handling customer payment follow-ups. If a customer disputes the amount, says they've already paid, or raises an issue outside the agent's defined scope, it doesn't keep pushing the conversation or make assumptions. It flags the exception, captures the conversation context, and routes it to the appropriate human team member. Once resolved, the workflow can continue. For us, reliable automation isn't just about completing tasks; it's also about knowing when not to proceed independently.
@iamanantgupta
For one our customer in insurance industry, a customer mentions a recent hospitalization and asks if their claim will be covered: The AI recognised that coverage decisions need a licensed agent, so it doesn’t guess or promise anything, and lets the customer know an advisor will call them back.
How do you decide which tasks are safe to fully hand over to an AI employee and which ones should still need human approval?
Congrats @kadyan_ajay & team!
Thanks for sharing @rohanrecommends :)
@rohanrecommends @hamza_afzal_butt Great question, Hamza! We generally look at the risk and impact of a task. Repetitive, rule-based tasks with clear outcomes are good candidates for full automation. But when a decision involves money, sensitive information, exceptions, or significant customer impact, it's better to have a human approval step. The idea isn't to remove humans from the process, but to involve them where their judgment matters most. Thanks for the wishes!