All case studies

AI Powered Internal Developer Platform (IDP)

Role:

Concept Designer & Futurist

Date:

Oct 2024

Problem

Some product proposals promise non-linear growth; this one demanded nothing short of a radical reimagining of how emerging technology can transform developer experience (DX) exponentially. This challenge appeared through one of the product proposals that I was working on last year. The challenge facing—a global insurance giant—was one of time, talent, and transformation. Despite employing some of the world’s most talented developers, their software development lifecycle was plagued by inefficiencies that robbed these experts of their ability to focus on high-value work. Repetitive, and time-consuming tasks like debugging, code reviews, dependency resolution, and compliance checks have become roadblocks, slowing down releases, frustrating teams, and stifling innovation.

This wasn’t just a technical problem—it was a strategic bottleneck. Developer time is among the most valuable resources in any modern enterprise, especially in regulated industries like insurance where speed, accuracy, and compliance are all non-negotiable. Yet, developers were spending more than a third of their time on tasks that, in theory, could be automated. The client’s internal tools lacked integration, usability, and contextual intelligence, which further deepened siloed operations and hindered collaboration.

How can an intelligent developer hub accelerate AI transformation?

Productivity bottlenecks became especially problematic at scale. With hundreds of developers across teams and geographies, scaling operations while maintaining quality and compliance was proving nearly impossible without smarter tooling. Dependencies between teams often led to delays, and the fragmented nature of existing platforms made it difficult to pinpoint what was going wrong, where, and why.

Worse, these operational inefficiencies posed a growing business risk. The inability to efficiently evaluate team output and project health meant the company was flying blind in critical moments—unable to make data-informed decisions about hiring, retention, and investment in new technologies. Innovation was slowing, and time-to-market was suffering as a result.

The larger ambition was clear: build a modern, AI-powered Intelligent Developer Platform (IDP) that doesn't just optimize workflows but transforms them. The vision? To empower developers to build, maintain, and scale with greater speed, accuracy, and creativity—without being weighed down by non-core tasks. But to do that, we needed to deeply understand the developer experience from the ground up.

Process

Solving this challenge required a methodical, human-centered and systems-driven approach—balancing user needs, architectural integrity, and organizational readiness. Our first step was to go deep into the "why" behind the inefficiencies, not just the "what." We started with a Jobs-to-be-Done (JTBD) analysis, conducting in-depth interviews with developers across functions, seniorities, and geographies to understand their day-to-day pain points and routines.

We mapped out the entire developer journey—from onboarding and project setup to deployment and post-launch observability. We paid special attention to repeatable and manual tasks that drained time and energy: long feedback loops, unclear ownership boundaries, inconsistent CI/CD pipelines, redundant debugging, compliance audits, and more.

Next came the gap analysis. We compared the existing platform's capabilities against the ideal state, identifying gaps in scalability, integration, visibility, and support for modern development paradigms like DevOps, AI-First workflows, and remote-first collaboration.

Armed with this intelligence, we realigned the business goals with what was technically and operationally viable. Key to this alignment was getting buy-in across stakeholders—engineering, product, compliance, and C-level executives—ensuring the platform would meet cross-functional needs.

We used this foundation to define the core principles of the IDP:

AI-First Architecture: AI was not an add-on; it was foundational. Every feature needed to support automation, reasoning, or decision-making.
Scalable and Modular Design: The platform had to work equally well for 10 or 10,000 developers.
Self-Serve Empowerment: Developers needed autonomy without compromising governance.
Secure-by-Design: Security and compliance needed to be enforced automatically, not retrofitted.

We conducted a thorough cloud and data strategy audit to ensure the platform could scale reliably and integrate with existing data ecosystems. We designed a composable architecture where services could be plugged in or replaced without massive rework. We followed Agile principles for iterative builds and embedded DevOps best practices for continuous delivery, real-time feedback, and fail-fast experimentation.

Finally, change management was pivotal. We anticipated resistance to new tools and workflows. To ease adoption, we embedded training modules, created internal champions, and designed the platform with a seamless onboarding experience, ensuring that the IDP could be embraced, not enforced.

Product

The Intelligent Developer Platform (IDP) was built not just as a tool but as a developer-centric ecosystem that empowers developers with the intelligence, autonomy, and context they need to thrive. The solution was a multi-layered, modular platform with the following planes:

1. Onboarding

We designed a guided, customizable onboarding experience. It contextualizes tools, sets up project spaces, provisions environments, and walks developers through organizational best practices—all using AI assistants to reduce onboarding time from weeks to hours.

2. Developer Core Control Plane

This is the developer’s mission control—a unified interface to manage projects, access repositories, collaborate across teams, and monitor progress. Each developer has a personalized dashboard that adapts to their tasks, notifications, and recent activity across systems.

3. AI Agents Plane

We embedded specialized AI agents for:

Code Reviews: LLM-powered agents that not only check for syntax or linting but also assess logical consistency, code smells, and architecture patterns.
Security Scanning: AI that understands known vulnerabilities, suggests remediations, and cross-checks with internal security policies.
Compliance Checks: Automated enforcement of coding, documentation, and data handling standards based on internal and external regulations.

These agents operate in the background or as just-in-time assistants, integrated into the IDE and CI/CD workflows.

Product

4. Self-Serve Control Plane

This layer offers automation for environment provisioning, CI/CD pipeline setup, and deployment orchestration. Developers can create, test, and deploy without waiting on DevOps engineers, while guardrails ensure consistency and reliability.

5. Testing & Quality Control Plane

We included AI-powered test case generation, regression detection, and synthetic testing. This minimized manual testing and provided instant feedback loops.

6. Observability & Analytics Plane

Real-time dashboards monitor developer velocity, platform health, code quality trends, and incident response times. The data is actionable—used to optimize workflows, identify training needs, and rebalance team workloads.

7. Data Products Plane

Developers have access to modular, reusable datasets—curated, versioned, and permissioned—so they can quickly build features that depend on high-quality data without duplication or misalignment.

8. Knowledge & Resources Plane

This is the AI-enhanced knowledge hub—one-click access to API specs, internal docs, onboarding guides, architectural blueprints, reusable templates, and best practices. Context-aware retrieval ensures relevant results.

9. Security & Compliance Planes

Built-in policy enforcement, audit trails, identity management, and risk scoring across the development lifecycle ensured that security was proactive, not reactive.

Each layer was API-first, extensible, and designed for continuous evolution. Importantly, everything was built with a strong human-in-the-loop mindset—empowering developers while augmenting their workflows intelligently.

Result

While the platform is still in deployment phases, early results from product prototyping and internal benchmarks point to a substantial savings, it is estimated:

33% reduction in time spent on non-core development tasks, such as manual reviews, provisioning, and documentation.
Up to 50% faster onboarding for new developers and cross-functional team members.
Improved velocity in CI/CD pipelines, with time-to-deploy reduced by over 35%.
Significant reduction in error rates thanks to AI-augmented code reviews and test automation.
Clear visibility into developer productivity, enabling better forecasting, load balancing, and investment decisions.

The IDP concept proved to be a solid vision, that has potential to balance continuous delivery with continuous innovation, adding real-time insights ensuring that both product and platform evolves in harmony.

Reflection

Reflecting on past projects, I see two perspectives: the successes that make me happy and the insights I've gained now that could have enhanced my approach in the past.

From a Product Designer’s Perspective:

If I had to do it again, I’d double down even earlier on the user journey mapping and bring design into early technical planning. Developers are power users—they need speed, minimal friction, and context-aware interfaces. While the platform works, we could have made workflows even more intuitive by embedding more proactive UX nudges and behavior-driven design from the start.

From a Product Manager’s Perspective:

The biggest lesson was that building AI-native platforms requires a shift in thinking—not just automating tasks but reimagining workflows entirely. I would also have pushed for earlier investments in training and change management. AI tools don’t just change processes; they change habits, and that cultural shift needs hands-on guidance. Future iterations will likely focus more on agentic AI—AI that can plan, reason, and act across tasks, not just assist.

Creative Confidentiality: In the spirit of professional discretion and digital camouflage, some client identities have been subtly transformed into their alter-ego personas. This ninja-like name-swapping applies exclusively to projects completed as an external design mercenary. For all other showcased works, brand and product names remain true to their original, registered identities.

Authenticity Stamp: Every pixel, wireframe, approach, and design concept you'll discover here is 100% crafted by the hands (and occasionally caffeinated brain) of Naren Katakam. No design outsourcing, no smoke and mirrors—just pure, unadulterated creative craftsmanship.