Why do you organize by industry instead of horizontally?

The playbook structure is shared across categories, but the citation surfaces are not. ML engineers learn from Hugging Face and Hacker News. CMOs learn from Lenny's Newsletter and MKT1. CX leaders learn from Support Driven and CX Today. LLMs sample those sources to assemble their answers. We work category by category because the source-of-truth surface is different for each buyer.

What industries do you work with?

Ten B2B SaaS categories today: MarTech, SalesTech and RevOps, AI/ML SaaS, DevTools, Data Infrastructure, Cybersecurity, FinTech, HealthTech, HR Tech, and Customer Support. Mid-market focus between $5M and $50M ARR.

What if our category is not listed?

Most adjacent B2B SaaS categories fit one of the ten playbooks. Vertical SaaS, eCommerce platforms, LegalTech, ProcureTech, and EdTech typically map to the closest analog. Book a category teardown and we will tell you which playbook applies.

Is the SEO and GEO playbook different per industry?

The shared structure is the same: AI Visibility Audit, /llm-info/ and category claim page, comparison and migration content, and citation engineering. The variance is in the citation surfaces, vocabulary, and content angles. We tune those per category so the work clears the bar for your specific buyer.

What is the minimum ARR you work with?

We focus on mid-market B2B SaaS between $5M and $50M ARR. That is where the AI-shortlist opening is biggest, where category-claim positioning has the most leverage, and where the work compounds fastest.

How fast do results show across industries?

AI category-claim accuracy and shortlist inclusion improvements typically show in 6 to 10 weeks once /llm-info/ and category content ship. Google ranking improvements for stack and migration queries follow in 3 to 6 months. Citation placements on Tier 1 sources follow editorial cycles, typically 2 to 4 months. The exact timeline varies per industry.

Case study: Gumlet turned ChatGPT mentions into 20% of inbound revenue. Read it →

Industries we work with

Every B2B SaaS category has its own AI-search problem. We have fifteen playbooks for it.

DerivateX is the SEO and GEO agency for mid-market B2B SaaS between $5M and $50M ARR. The shared playbook is structured. The buyer, the citation sources, and the AI-default problem are different in every category. Pick yours.

See How AI Names You Book a Category Teardown

The Fifteen Playbooks

One playbook per category. Each tuned to where AI defaults today.

Pick the one closest to your category. Each playbook covers the unique 2026 AI-search dynamic, the citation sources that move your shortlist, and the 90-day path to getting cited correctly.

01 MarTech

MarTech SaaS

Your buyer is a marketer using AI to evaluate marketing software. The meta dynamic is the moat, and operator newsletters carry citation weight.

CMO buyer Lenny's & MKT1 cite at Tier 1

Read the MarTech playbook

02 SalesTech & RevOps

SalesTech & RevOps SaaS

CROs ask ChatGPT for revenue proof. AI describes you with activity metrics. AI SDR vocabulary is being defined in real-time and the citation winner gets the category.

CRO + RevOps buyer LinkedIn is Tier 1

Read the SalesTech playbook

03 AI / ML SaaS

AI / ML SaaS

ML engineers ask Claude before they ask you. Claude defaults to OpenAI and AWS Bedrock. Category vocabulary changes monthly. MCP server docs are the newest GEO surface.

AI engineer buyer Hyperscaler-default is the #1 problem

Read the AI/ML playbook

04 DevTools

DevTools SaaS

Cursor is the new SERP. Copilot is the new Stack Overflow. Get recommended inside the IDE, not just in ChatGPT. Docs are GEO surface area, not just documentation.

Developer buyer IDE recommendations are the new surface

Read the DevTools playbook

05 Data Infrastructure

Data Infrastructure SaaS

Data engineers ask Claude. Claude says Snowflake. Cost surprise is the #1 switching trigger in 2026. The stack-cell AI puts you in decides which shortlist you join.

Data engineer buyer Cost is the #1 trigger

Read the Data Infra playbook

06 Cybersecurity

Cybersecurity SaaS

CISOs ask ChatGPT before they ask your SDR. Category misclassification (CNAPP, XDR, IGA) is the deal-killer no one talks about. Analyst-tier citations move the shortlist.

CISO buyer Category accuracy is the #1 problem

Read the Cybersecurity playbook

07 FinTech

FinTech SaaS

Half of AI answers misstate SOC 2, PCI-DSS, or bank partner claims. One compliance misstatement on a sales call kills the deal. Get cited correctly across the four LLMs your buyers run.

Bank + FI buyer Compliance accuracy decides the deal

Read the FinTech playbook

08 HealthTech

HealthTech SaaS

Hospital CIOs ask ChatGPT before they ask your AE. AI confuses HIPAA, FDA SaMD classification, and EHR integration in one answer. Clinical accuracy is the credibility floor.

Hospital CIO buyer KLAS + peer journals cite at Tier 1

Read the HealthTech playbook

09 HR Tech

HR Tech SaaS

HR buyers compare you against BambooHR, Rippling, and Workday on AI search before sales. The mid-market positioning gap between SMB and enterprise is where you win.

HR leader buyer Mid-market positioning gap

Read the HR Tech playbook

10 Customer Support

Customer Support SaaS

Zendesk pricing pushed CX leaders to ChatGPT. The "Zendesk alternative" prompt is the highest-value query in support SaaS right now. Per-seat vs per-resolution is the new pricing tension.

CX leader buyer Zendesk-alternative is the moment

Read the Customer Support playbook

11 E-commerce

E-commerce SaaS

DTC operators ask ChatGPT for Shopify alternatives before they ever hit a vendor site. AI flattens headless commerce and monolithic platforms into one shortlist. Differentiation has to be encoded.

E-commerce manager + CTO buyer "Shopify alternative" is Tier 1

Read the E-commerce playbook

12 LegalTech

LegalTech SaaS

GCs and legal ops query AI for vendor comparison, then verify with bar-cited sources. ABA-cited articles and legal trade publications are the citation tier that moves the shortlist. Ethics rules matter.

GC + legal ops buyer ABA-cited is Tier 1

Read the LegalTech playbook

13 Data & Analytics

Data & Analytics SaaS

BI leaders and heads of analytics ask AI to compare vendors. The modern data stack categorization (BI, semantic layer, reverse ETL) is fragmented in LLMs. The cell AI puts you in decides the shortlist you join.

BI lead + data team buyer Modern data stack cell decides shortlist

Read the Data & Analytics playbook

14 EdTech

EdTech SaaS

L&D leaders, university CIOs, and K-12 admins compare LMS vendors in ChatGPT. AI confuses K-12, higher-ed, and corporate L&D categories. Segment specificity decides which shortlist you join.

L&D + admin buyer Segment confusion is the #1 problem

Read the EdTech playbook

15 PropTech

PropTech SaaS

Brokers, property managers, and real estate investors query AI for vendor comparison. Residential, commercial, and transaction-management get conflated in LLMs. The category split has to be staked or AI flattens you.

Broker + property mgr buyer Vertical conflation is the #1 problem

Read the PropTech playbook

What's Shared Across All Ten Playbooks

The playbook structure is constant. The citation surfaces are different.

Every engagement starts here. The variance lives in the citation surfaces, vocabulary, and buyer language we tune to your category.

AI Visibility Audit

Baseline pull across four LLMs. We see exactly where AI puts you today, what it gets wrong, and which competitors it cites instead.

/llm-info/ + Category Claim

Canonical machine-readable page that stakes your category, integrations, compliance posture, and competitive position. The page LLMs sample as your source of truth.

Comparison + Migration Content

Named comparisons against your incumbents, plus migration playbooks for buyers leaving them. Bottom-of-funnel content tuned to your specific category dynamics.

Citation Engineering

Structured placement on the sources LLMs actually sample for your category. Operator newsletters, analyst firms, GitHub, Hacker News, or Support Driven, depending on where your buyer reads.

Proof Across Different Buyer Types