AI agent development for sales and customer support that handles real conversations, qualifies real leads, and resolves real queries in production.
By Citara Labs, a design and engineering studio. GCC, India, and USA.
An AI agent that qualifies inbound leads, handles initial enquiries, books meetings, and passes qualified conversations to the human sales team with full context.
An AI agent that resolves the queries it can resolve, escalates the ones it cannot, and improves over time as it encounters more of the real questions your customers ask.
Every agent is connected to the systems that run the business: CRM, ticketing, knowledge bases, and communication platforms. Agents that work inside your existing infrastructure, not alongside it.
AI agents degrade without maintenance. Every engagement includes an operations plan: monitoring, retraining, and the human oversight structure that keeps the agent performing.
The right AI agent deployment does not replace the human team. It handles the volume of work that does not require human judgment so the human team can focus on the work that does. Citara Labs builds agents that understand that distinction.
High inbound volume, slow response. Inbound enquiries are taking too long to respond to. An AI sales agent handles the initial response and qualification immediately.
Support team handling repetitive queries. The support team is spending most of its time answering the same twenty questions. An AI agent handles those so the team can handle the ones that actually need them.
After-hours coverage. The business needs to be responsive outside business hours without paying for a 24-hour support team.
Scaling without proportional headcount. The business is growing and the volume of sales and support conversations is growing with it. AI agents allow the business to scale the conversation volume without scaling the team at the same rate.
We map the conversations the agent will handle, the ones it will escalate, and the systems it needs to connect to before any development begins.
The agent is built, trained on the business's actual knowledge and conversation patterns, and tested against real scenarios before deployment.
The agent goes live with monitoring in place. An operations plan defines who reviews performance, how the agent is retrained, and when a conversation is escalated to a human.
The Sr. Agentic Workforce that handles research, documentation, and monitoring at Citara Labs is the same model we deploy in client environments. We know where the edge cases are because we encounter them every week.
An AI agent built by Citara Labs is not a chatbot with a new name. It is a production system built to the engineering standard that any other production system at the business would be held to.
Production engineering standard. Built and tested to the same standard as any other production system in the business.
CRM and platform integration. Agents work inside your existing infrastructure, not alongside it.
Operations plan included. Every deployment includes monitoring, retraining, and human oversight structures.
We run our studio this way. The agentic architecture we sell is the same architecture we operate. We know what works because we use it.
We run our studio with a Sr. Agentic Workforce. Here is what that actually looks like.
Every company has an AI strategy. Very few have AI running inside their actual operations. This is how we run ours.
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Most company websites are invisible to AI answer engines. Not because the companies are obscure. Because the websites are structured in a way that AI systems cannot read, cite, or trust.
Read →AI agent development for sales and support is one of five services under the AI Deployment practice. The same senior team that builds agents also builds private knowledge systems, agentic workflows, and AI integrations.
See the full practice →A short conversation with a senior partner is enough to establish what the deployment would look like and whether there is fit.
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