Industry · B2B SaaS

B2B SaaS AEO, GEO & AI Search Services in Singapore

Geolix.ai is a Singapore GEO agency for B2B SaaS and developer tools. We do one thing: get the nine engines to name you when buyers ask for the best tool in your category, for alternatives to a competitor, or for a head-to-head verdict — across crawlability and markup, comparison pages, and the sources engines quote: G2, Reddit, Hacker News, Zhihu and CSDN.

Why AI search is different for B2B SaaS

SaaS buying runs bottom-up: developers pick a tool, then ask for budget. The selection happens in prompts like best X tool and X vs Y — the highest-intent questions in AI search, because the next step is a trial signup.

Engines assemble those answers from G2, Reddit, Hacker News and comparison posts, outdated pricing and long-fixed gaps included. The middle of the funnel disappears into a chat window, and marketing sees fewer leads without seeing which answer lost them.

7 signs your B2B SaaS brand needs GEO

Run the checklist yourself — the more that hit, the more buyers you are losing inside AI answers.

01

Ask ChatGPT for the best tool in your category — you are not in the first names.

02

Ask for alternatives to the category leader — you are missing or last on the list.

03

AI describes you with old pricing, a retired plan or a gap you shipped the fix for.

04

In X vs Y questions, the verdict keeps landing on the other side.

05

G2 and Reddit threads describe a product several versions old, and nobody owns that layer.

06

Docs and changelog render client-side or sit behind a login — the very pages engines would cite.

07

DeepSeek and Qwen answer your category with local tools and your global competitors only.

Your buyers are asking AI right now

These decision prompts are exactly what we track daily — whether you appear, where you rank, and who beats you.

What's the best project management tool for a team under 50?
What are cheaper alternatives to Salesforce?
HubSpot or Salesforce for an early-stage startup?
Which help desk platforms can be self-hosted?
Best free CRM a small sales team can actually run on?
Notion or Confluence for a team knowledge base?
Good alternatives to Tableau for BI dashboards?
Which e-signature tools are SOC 2 compliant?

What we build for B2B SaaS

01

Docs and changelog engines can read

Page by page: what renders server-side, what sits behind a login, the robots.txt policy for GPTBot and PerplexityBot, and an llms.txt pointing at docs, pricing and changelog.

02

Structured data for product, pricing and entity

SoftwareApplication, Organization and FAQPage JSON-LD tied to the pricing page, so a retired plan stops being repeated as current fact — plus entity disambiguation.

03

Comparison and alternatives pages built to be quoted

The X vs Y and alternatives pages your buyers actually type, written answer-first: where you fit, where you do not, migration cost, pricing differences.

04

The third-party layer engines quote

G2 and Capterra listings completed and correctly categorised, Reddit and Hacker News threads stuck on old versions worked, Zhihu and CSDN built on the Chinese side.

05

Daily shortlist-query monitoring

Real phrasings from Search Console and your sales logs, run daily across nine engines: who gets recommended, how you are described, which pages the answers cite.

How we work on it

01 / TECHNICAL

Crawling and rendering come first, content ships second

Docs sites often render entirely client-side; add a login wall and a robots.txt nobody configured, and your most quotable content is invisible. Fix that, then publish.

02 / SOURCES

The shortlist is decided outside your own site

Best-tool answers are assembled from G2, Reddit, Hacker News, Zhihu and CSDN — your own site mostly serves as the fact check. So half the effort goes to the layer you do not control.

How the engagement runs

Weeks 1–2

Markets, buyer profiles and competitors scoped; the query set locked from real phrasings; crawl audit and nine-engine baseline.

Month 1

Rendering and login walls on docs, robots.txt AI-crawler policy, llms.txt, SoftwareApplication and Organization JSON-LD, entity disambiguation.

Months 2–3

Comparison and alternatives pages shipped, pricing and integration docs rewritten answer-first with FAQPage markup, stale versions cleared.

Months 3–4

G2 and Capterra completed, old Reddit and Hacker News threads addressed, Zhihu, CSDN and industry media built on the Chinese side.

Ongoing

Daily runs, weekly reconciliation: which questions now shortlist you, which pages the answers switched to citing, what gets built next.

Frequently asked questions

What happens between signing and the first data we see?

First we scope the monitoring together: target markets, buyer and developer profiles, the competitors to benchmark, and the shortlist questions to track — real best-tool, alternatives, and X vs Y phrasings. Those queries then run daily across all nine engines while the dashboard builds your baseline: who each engine recommends today, how it describes your product, and which sources it cites. Your first weekly report delivers the visibility baseline, the competitor comparison, and a prioritized list of source gaps, and we reconcile against it every week after.

How soon do AI answers start to change?

There is no fixed timeline, because the mechanism sets the pace: engines first have to crawl new sources, then gradually trust them when assembling answers, and both steps depend on how contested your category is and what sources you already have. Qualified long-tail questions usually move first — phrasings that specify a deployment model, a compliance requirement, or a team size — while broad best-of head terms move last. We never promise a date; every query is logged daily on the dashboard, so what you see is a trend line, not a verbal assurance.

How do you measure results?

On three levels. Visibility rate: the share of your shortlist questions where an engine mentions you at all. Recommendation rate: the share where AI goes further and actually recommends you. Source citations: which pages the answers cite, and which of those we built. Each metric is broken down per engine and per query, benchmarked against competitors, and reconciled line by line in the weekly report — which queries moved, and on the strength of which sources.

We are an English-first product. Do Chinese engines really matter?

That depends on your market map, but SaaS companies routinely underestimate their Chinese touchpoints: entering China means developers vetting your stack on DeepSeek and Qwen, existing Chinese customers mean procurement and IT running checks on Doubao and Tencent Yuanbao, and local resellers and integrators do their homework in Chinese engines too. None of these read G2 or Reddit — they cite Zhihu, CSDN, and WeChat-published articles, so your English reputation does not carry. At minimum, look at your Chinese-side baseline on the dashboard before deciding how much to invest.

We already run SEO and content in-house. How do we work together?

Your team keeps doing what it does well; we close the loop on the engine side. The dashboard shows which queries are slipping and whom each engine cites, so your writers work against a real priority list, and we take over whatever you want managed — diagnosis, restructuring content for AI citation, and building authoritative third-party sources. Your existing blog, docs, and comparison pages are the raw material; the job is turning them into sources the nine engines cite, not standing up a rival content operation.

How is white-hat work guaranteed? Any compliance or brand risk?

We do three things only — diagnosis, fact-based content, and authoritative source building — all grounded in verifiable information about your product: no poisoning, no fake volume, no fabricated reviews, no posing as users in communities. Developer communities are ruthless with astroturfing, and one exposed scheme hurts a brand more than never doing GEO at all, which is exactly why we stay white-hat. Every deliverable is traceable item by item in the weekly report, and our Base plus Bonus pricing — with the Bonus paid only after agreed KPIs are met — leaves us no incentive to cut corners.

See where you stand, then decide

Run a free snapshot and see how nine engines answer your category's questions today.

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