How We Built Smart Pitch — and What We Learned Along the Way

In today’s world, AI is no longer a luxury—it’s a necessity for driving smarter decisions, faster innovation, and personalized experiences.

We have come up with our requirements for AI to support the conversion from MQL (Marketing Qualified Lead) to SQL (Sales Qualified Lead).

How did the idea originate?

In our organization, whenever a prospect reaches out to us, we search for company information like company size, revenue, location, industry type, contact person details, designation, decision-maker, and LinkedIn profile. This information helps the Sales team prepare better and deliver a stronger pitch by understanding the customer before the call.

Also, during the MQL to SQL stage, we look for things like:

• – Have we worked on a similar use case before?
• – Do we have clients in the same region?
• – Have we served clients from similar industries?

This information helps the Sales team convert the prospect into a client and increases our chances of winning the deal.

Earlier, this entire process was manual and time-consuming. So, we decided to automate it with an AI agent that can gather this information for us in just a few minutes.

Implementation approach

After the project was approved internally, we started exploring how to make it happen. Initially, we didn’t know where or how to start. During our research, we came across Copilot Studio, which allows us to build custom agents from scratch based on our needs.

We learned about Copilot Studio’s and began building our agent. We named it Elevator Pitch.

Version 1 Highlights:

• -User could enter the prospect’s name, requirement, industry type, and location.
• -The agent fetched relevant information from our internal content library (like case studies published on the website).
• -It provided related case studies based on region, requirement, and industry.
• -The results were promising, but we still had to manually collect company information, contact details, and prepare the MQL to SQL document.
• -We also showcased the agent to the Microsoft AI team. They liked it and suggested we reduce back-and-forth questions with the user.

This feedback led to the idea for Version 2, which would automate more steps and also pull information from the internet.

Version 2 Enhancements:

• -We integrated Azure OpenAI for internet search, but the results weren’t very good.
• -Then we switched to ChatGPT API, which provided better results. However, we faced challenges pulling contact person names, designations, and LinkedIn profiles due to API limitations on personal data.
• -Even with this known limitation, we proceeded and successfully completed Version 2.

Version 2 Features:

• -The user simply selects either Smart Pitch or Content Search.
• -If Smart Pitch is selected, the agent asks for:
• -Company Name
• -Contact Person (if known)
• -Requirement
• -Based on this input, the agent:
• -Searches company information online
• -Attempts to find contact details
• -Summarizes the company profile
• -Then asks the user to confirm and generate the MQL to SQL document

Company & Contact information with a single click on MQL to SQL, the agent now generates the document within minutes—something that earlier used to take hours or even a full day.

Live demo in Zurich & New York

On 22nd May 2025, we had an event scheduled at the Microsoft office in Zurich with one of our clients, where we shared the Buchi journey with CloudFronts. We discussed how we collaborated to connect their multiple systems and prepared their data for insights and AI initiatives.

At the same event, we had the opportunity to demonstrate our Smart Pitch product, which caught the audience’s attention. It was a proud moment for us to showcase our first AI product at the Microsoft office—delivered within just a few months of hard work.

Our second opportunity came on 06 June 2025 in New York, at the AI Community Conference, where we presented again in front of a global audience.

What Next in Version 3:

So far, we have built this solution using Microsoft’s inbuilt Knowledge Center, ChatGPT API, SharePoint, company websites, and Dataverse. Since we were working with both structured and unstructured data, we faced some inconsistencies and performance issues.

This led us to reflect and identify the need for Version 3 (V3), which will include:

• –MCP Server as a central bridge,
• –Vector Database to handle unstructured data more efficiently, and
• –Databricks to clean and prepare the data.
• -Replace ChatGPT with inbuilt Internet Search

The development of Smart Pitch V3 is currently in progress.

We’ll share our thoughts once it goes live. A demo video has also been shared, so you can see how smart and fast our Agent is at delivering useful insights.

Delivering Answers in Minutes—Thanks to Smart Pitch

I’d also like to share a quick story. One day, our Practice Manager was on leave, and we received a prospect inquiry about Project Operations to Business Central (PO-BC) pricing. I wasn’t sure where that information was stored, and suddenly our CEO asked me for the details.

I was a bit stressed, unsure where to search or how to respond. Then I decided to ask our Smart Pitch agent the same question. To my surprise, the agent quickly gave me the exact information I needed.

It was a big relief, and I was able to share the details with our CEO in just a few minutes—without even knowing where the document was uploaded.

We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.