How do agencies actually use AI agents day to day — not in theory, but in real client work? Umesh Kumar, founder and CEO of RAN Lab Technologies, joined the Airo Builder community to answer exactly that.

RAN Lab is an AI-first technology and digital transformation company building web and mobile applications, enterprise software, and AI agents across healthcare, marketing, manufacturing, and e-commerce. In this session, Umesh walks through the specific workflows his team has automated, the monitoring tool they built with Airo, and the single insight that changed how he thinks about AI output.

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In this session

How AI became part of the daily workflow

Umesh started using AI for content generation — drafting proposals, preparing for client meetings, and structuring research. Over time, he realised the real value was not in one-off prompts but in building agents that could run those steps automatically.

Today, eight or nine AI agents work behind the scenes at RAN Lab: researching clients, drafting proposals, suggesting architectures, and preparing meeting briefs. What used to take hours of manual preparation now happens before Umesh walks into a room.

Key takeaway: AI does not need to replace how you work. It should improve how you work — giving good people the ability to achieve more, faster.

Automating proposal drafts with AI agents

Proposal generation was one of the first workflows Umesh automated. He connected an AI agent to his employee data — salaries, business expenses, profit margins — so the agent can generate a complete proposal draft, including timelines and costs, from a short brief.

The process started simple: a template the agent would fill in, with Umesh handling pricing manually. Over time he added employee data, then pricing logic, then timeline generation. The agent is not always 100% right, but it is 80–90% there — and Umesh only needs to cross-check before sending.

Using AI for security testing

On the technical side, RAN Lab uses AI for SAST and DAST (static and dynamic application security testing). As developers write code using Copilot, Cursor, and other AI tools, the team runs security checks to catch loopholes before they reach production.

Monitoring client websites with an AI-powered tool

The biggest operational win Umesh described was a website monitoring tool his team built with Airo AI Builder. Managing hundreds of client sites meant constantly checking whether each one was live, how fast it loaded, and when SSL certificates were due to expire.

The tool they built tracks:

  • Uptime — which sites are online or offline
  • Latency — response time for each site
  • Page speed — performance scores
  • SSL expiry — certificate status and renewal dates

An AI agent runs in the background. When it detects a site with low performance or an expiring certificate, it also searches for nearby businesses with similar issues — turning a monitoring dashboard into a business development tool for the RAN Lab sales team.

Speeding up prototyping for clients

Before Airo, RAN Lab's client onboarding involved Figma designs, UX flows, and weeks of back-and-forth before a client could see what their product would look like. Now, Umesh gives Airo a few competitor URLs and some context, and within hours has a working prototype to share.

Clients can open it in a browser or on mobile. They can interact with it like a real product — not a static Figma file that requires explanation. Deals that used to take weeks to close now close in days.

Why context beats prompts

The biggest shift in Umesh's thinking: it is not the prompt that matters, it is the context.

When he gives Airo or any AI tool real business context — how the company runs, its goals, its constraints — the output fits the work far better than a carefully worded one-off instruction. Competitor URLs, employee data, profit margins, client industry details: all of it becomes context that makes the AI more useful.

Key takeaway: Give AI the right business context and see whether it can help you solve a problem faster or better.

When a prototype is ready to be the final product

For straightforward websites, the Airo-built prototype is the final product. RAN Lab launches it directly for the client. When a project requires dynamic functionality or complex custom flows, the team moves to custom development — but the Airo prototype has already done the job of getting client buy-in and aligning on direction.

Start with one problem and automate it

Umesh's advice for anyone starting out with AI in their workflow: do not try to automate everything at once. Pick one real, repeatable problem. Automate it. See the value. Then expand.

His proposal agent started as a template filler. It grew into a system that handles timelines, costs, and business context. His monitoring tool started as a simple uptime checker. It grew into a lead generation engine.

The pattern is the same every time: one problem, one automation, then build from there.

Timestamps

  • 00:00 — Welcome and session introduction
  • 02:12 — What RAN Lab builds
  • 04:08 — How AI became part of the daily workflow
  • 05:56 — Automating proposal drafts with AI agents
  • 06:55 — Using AI for security testing
  • 08:20 — Monitoring client websites with an AI-powered tool
  • 11:15 — Speeding up prototyping for clients
  • 16:14 — Why context beats prompts
  • 17:37 — When a prototype is ready to be the final product
  • 18:51 — How AI helps speed up decision-making
  • 20:16 — Planning for a client's future business needs
  • 23:24 — The lesson Umesh wishes he'd known sooner
  • 24:22 — Giving AI business context
  • 26:19 — Start with one problem and automate it
  • 30:12 — The episode's main takeaway
  • 33:39 — Website traffic and SEO discussion
  • 37:40 — Redesigning a landing page to improve conversions
  • 39:27 — Final takeaways: give AI context and solve one problem first

Resources

Frequently Asked Questions

Can Airo AI Builder be used for client prototyping?

Yes. RAN Lab uses Airo to build working prototypes for clients within hours of a brief. Clients can interact with the prototype in a browser or on mobile, which speeds up decision-making and shortens the sales cycle compared to static Figma designs.

Does context matter more than prompts when using AI?

According to Umesh Kumar, yes. Giving AI real business context — competitor URLs, company goals, employee data, profit margins — produces far better output than a carefully worded prompt alone. The more relevant context the AI has, the more useful its output becomes.

When should an agency use Airo versus custom development?

For straightforward websites, Airo can be the final product. When a project requires complex dynamic functionality or custom integrations, RAN Lab moves to custom development — but uses Airo to build the initial prototype and get client alignment first.

How can AI help with website monitoring for agencies?

RAN Lab built a monitoring tool with Airo that tracks uptime, latency, page speed, and SSL expiry across all client sites. An AI agent runs in the background and flags issues — and also identifies nearby businesses with similar problems, turning monitoring into a business development opportunity.