Marketing intelligence for developer tools
Developers ask assistants for library and tooling recommendations inside the editor, mid-task. That is a recommendation surface with no analytics, no click and no second chance.
What makes developer tool companies different
The recommendation happens inside the editor
An assistant suggesting a library during a coding session is making a vendor decision. No dashboard anywhere records it.
Documentation is the marketing
Assistants quote docs, changelogs and issue threads far more readily than landing pages. Teams that treat docs as an afterthought are invisible in the moment that matters.
Training data lags your roadmap
Models frequently describe versions two releases old. Without monitoring you cannot tell that your best feature does not exist as far as the assistant is concerned.
These questions are being answered without you
Assistants answer category questions with a handful of brand names. Noma tracks whether yours is one of them, across ChatGPT, Perplexity, Gemini, Claude and Grok.
- best library for [task] in Python
- [tool] vs [competitor] for CI
- how to add [feature] to a Next.js app
- is [tool] still maintained
Three agents, three separate jobs
Know the audience is real before you pay for it
Creator budgets in developer tools are set against follower counts, which is the number most likely to be bought.
- TruAI scores follower quality profile by profile, calibrated for Indian accounts
- Forecast expected return before the brief goes out, not after the invoice
- Attribute sales to the product, so you learn which creator sells what
Be named when an assistant answers
Category questions are increasingly answered with a handful of brand names, chosen without anyone visiting a website.
- Daily tracking across ChatGPT, Perplexity, Gemini, Claude and Grok
- Share of voice per prompt, and which competitor is named instead
- A 12-point audit of whether your pages can be quoted cleanly
Find the spend that is not working
Read-only access to Meta and Google Ads, explained in plain language rather than another dashboard.
- Creative fatigue flagged before performance drops
- Wasted spend pinpointed at campaign, ad set and creative level
- A full account audit the moment you connect
Playbooks worth starting with
- AI Search VisibilityWhat Is Share of Voice in AI Search and How Do You Actually Measure It?
- AI Search VisibilityHow to Get Cited by Gemini Specifically (It Works Differently From ChatGPT and Perplexity)
- AI Search VisibilityHow to Run a Competitor AI Visibility Audit in One Afternoon (No Agency Required)
- AI Search VisibilityNoma vs Traditional SEO Tools: What Ahrefs and Semrush Genuinely Cannot Do for You in 2026
- Paid MediaWhat a Healthy Google Ads Account Looks Like vs What Most D2C Brands Actually Have
- Paid MediaWhy Your Meta Retargeting Ads Are Showing to People Who Will Never Buy Again
Common questions
How do developer tools get recommended by AI coding assistants?
By being well documented in the places assistants read: clear docs with runnable examples, honest comparison content, maintained changelogs, and answers on the forums developers actually use. Assistants reward content that resolves a task completely, which is why a good quickstart outperforms a good landing page by a wide margin. Monitoring matters as much as publishing, because models describe stale versions confidently — you want to find that out before a prospect does.
Stop guessing which half is working
Nia, Noma and Pulse work independently. Start with the one that hurts most.