Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent

Client Overview

The startup is a SaaS platform built for operators who need to make fast, data-driven decisions. They had a product, a team, and a vision. What they were missing was the AI infrastructure to make it all actually work.

Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study
Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

The Results

The Results

The Results

The client came out of this engagement with a fundamentally different product. The rigid, script-bound platform is gone. In its place is an autonomous system with memory, context, and specialised agents working across the business. Their go-to-market accelerated, their valuation improved, and the platform now generates value for operators without anyone having to drive it manually.

100%

100%

100%

Proprietary AI IP Created

16+

16+

16+

Autonomous Agents Deployed

0

0

0

Manual Operator Decisions Needed

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent

Client Overview

The startup is a SaaS platform built for operators who need to make fast, data-driven decisions. They had a product, a team, and a vision. What they were missing was the AI infrastructure to make it all actually work.

Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study
Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

The Results

The Results

The Results

The client came out of this engagement with a fundamentally different product. The rigid, script-bound platform is gone. In its place is an autonomous system with memory, context, and specialised agents working across the business. Their go-to-market accelerated, their valuation improved, and the platform now generates value for operators without anyone having to drive it manually.

100%

100%

100%

Proprietary AI IP Created

16+

16+

16+

Autonomous Agents Deployed

0

0

0

Manual Operator Decisions Needed

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent

Client Overview

The startup is a SaaS platform built for operators who need to make fast, data-driven decisions. They had a product, a team, and a vision. What they were missing was the AI infrastructure to make it all actually work.

Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study
Engineering AI Infrastructure Into a SaaS Platform: From Hardcoded API to Autonomous Agent — Genta AI case study

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Challenge

01

The platform had a fundamental problem. It had no proprietary value, which was slowing down their go-to-market and hurting their valuation in conversations with investors. Under the hood, the system was hardcoded and rigid. It could only handle questions that were pre-scripted, nothing outside of that. There was no memory, no context awareness, and no ability to filter data by user or organisation. The operators using it were still making decisions manually and reactively, which defeated the whole purpose of having the tool. Every day without a fix was costing the business.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

The Solution

02

Genta AI engineers embedded directly with their development team, working side by side to design and deliver every AI component of the client's system from scratch. The entire backend was re-architected around an enterprise agentic system connected to the database through a semantic layer. Specialised agents were then deployed for Pricing, Marketing, Renewal, and Retention, each running on custom protocols that execute actions across all connected systems. The platform went from a reactive tool to one that drives decisions and revenue on its own. The collaboration also gave our client something they didn't have before: real, proprietary AI intellectual property that they fully own.

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

Technologies Used

03

• OpenAI Agents SDK • Nango • Cube.js (Semantic Layer) • Azure SQL • Custom Built MCP Server (Internal)

The Results

The Results

The Results

The client came out of this engagement with a fundamentally different product. The rigid, script-bound platform is gone. In its place is an autonomous system with memory, context, and specialised agents working across the business. Their go-to-market accelerated, their valuation improved, and the platform now generates value for operators without anyone having to drive it manually.

100%

100%

100%

Proprietary AI IP Created

16+

16+

16+

Autonomous Agents Deployed

0

0

0

Manual Operator Decisions Needed

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect

Tell us where the manual work hurts

We’ll tell you straight whether AI can fix it, what it costs, and what it should return. Whatever we build, you own.

Let's Connect