Insights

How We Automated Research-Driven Content Generation

October 2025

Not just random posts — we built a system that researches, writes and publishes content with precision.

Introduction

At Kodable, we build products — not marketing campaigns. As engineers, we simply didn’t have the time (or the desire) to spend hours creating content, researching trends, and managing social posts. Content creation always felt like something that pulled us away from what we do best — building. So instead of trying to become marketers, we decided to engineer a solution. That’s how our automated, research-driven content engine was born.

The Challenge
Content used to follow this pattern: idea → manual research → draft → edit → publish. Problems:

Ideas felt recycled, because we didn’t always know what was working.
Research was ad-hoc.
Time to publish was long.
Scaling felt impossible without hiring more people.
We needed a better way.

Our Solution: Research-Driven Automation

Here’s how we built it:

Define the focus: We picked key topics aligned with Kodable’s mission (e.g., “coding education for kids”, “EdTech trends”, “why kids learn programming early”).
Automated scraping and research: Using tools & APIs we pulled data from top YouTube videos and X posts about these topics. We transcribed videos, collected high-engagement posts, and gathered signals on what works.
Idea generation via AI: Feeding that research into our AI agent, we asked it: “Based on this research, generate 10 content angles we haven’t used yet, each with a hook, suggested format, and target audience.”
Content creation: Then the AI wrote drafts, suggested visuals (we generated images with AI), and produced platform-specific versions (LinkedIn, blog, etc.).
Human review & publish: We still reviewed for brand voice, accuracy, and tone — then scheduled publishing automatically.
Scaling & iteration: Because the pipeline is automated, when a topic works we replicate it; when it doesn’t, we refine.

Results & Impact

We cut content research + draft time by ~70%.
We began publishing content with deeper insights (because it was grounded in research of what was resonating).
Engagement improved because our content better matched what audiences were already responding to.
We freed our team to focus on strategy and creative tasks rather than repetitive work.
We can now test new topic clusters quickly, iterate, and scale without adding headcount.

What Makes It Different

It’s not just “AI writes posts”. It’s “AI researches + writes + formats + publishes”.
The system uses data from outside our brand to inspire content, not just internal ideas.
We built it for internal use at Kodable, meaning it fits our voice, audience and mission.
It’s end-to-end but also modular: we can plug in new topic streams or channels easily.

Lessons Learned

Good outputs require good inputs: the research block needs depth and relevance.
Brand voice matters: even the best AI draft needs tuning to feel like Kodable.
Review loops still matter: automation is powerful but not total replacement for human judgement.
Start small, iterate fast: we launched with one topic stream, refined workflow, then added more.
Measure, refine and scale: we track which content angles perform, feed that back into our research module.

Conclusion

At Kodable, we didn’t settle for more content. We settled for smarter content. Our research-driven automated content engine allows us to publish with agility, relevance and scale. For any business looking to go beyond “just posting”, this is how we recommend you think about content automation: not random text, but research-backed automation.

Interested in how we built this or want help doing the same? Reach out to us at Kodable — we’d love to share our approach and explore how your team can build a similar system.

Our Vision: Smarter Tools, Smarter Growth

Looking ahead, we have big plans for Kodable’s AI content system.
What started as an internal automation project is evolving into something much bigger — a complete, intelligent platform for content creation and learning.

We imagine a clean, intuitive UI where anyone on the team can log in, choose a topic, and instantly see researched insights, suggested content ideas, and ready-to-publish drafts — all powered by AI.

No technical setup, no complicated workflows — just simple, smart tools that work in the background.

But our real dream goes beyond automation.
We want to build a system that learns from us — one that studies our past posts, tracks engagement, and adapts based on what truly resonates with our audience.
Imagine analytics that don’t just report performance but actually help the AI improve its next draft.

Over time, every piece of content, every experiment, every click will feed back into the system — teaching it what great content looks like for Kodable.
The more we use it, the smarter it becomes.

Ultimately, our goal is to transform this from a productivity tool into a self-evolving content intelligence system — one that grows with us, amplifies our voice, and helps us tell better stories through technology.