Four services. One outcome: your firm is the one the AI recommends.
MadTech Growth is a Generative Engine Optimization advisory for professional services firms in the NYC tri-state area. We work with law firms, wealth management and RIA practices, medical and specialty practices, and insurance brokerages — industries where the buying decision is driven by trust, credentials, and reputation. Every service below exists for one reason: to make sure that when a prospective client asks ChatGPT, Perplexity, Claude, or Gemini about your category, your firm is in the answer.
We measure and optimize how your firm appears across the AI systems your prospects actually use — ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Because LLM responses vary significantly across runs, we test each diagnostic prompt at least ten times per platform to produce a reliable visibility baseline rather than a point-in-time snapshot.
The output is a clear picture of where your firm is cited today, where competitors are cited instead, and where the category is being captured by directories, media outlets, or incumbents. From there, we prioritize the interventions that move the needle fastest — on-site, off-site, and in the third-party surfaces AI models trust.
What's included
- Multi-prompt, multi-platform visibility measurement (≥10 runs per prompt)
- Competitor benchmarking across your category and geography
- Citation-source analysis — which sites are the AI pulling from
- 90-day prioritized optimization roadmap owned by the firm
- Quarterly re-measurement for retainer clients
Who it's for
Firms that suspect — or have confirmed — that they are underrepresented in AI answers for their core service queries, and want a defensible measurement baseline before investing in remediation.
Read the full GEO & AI Visibility page →For professional services queries, AI systems weight third-party citations, verifiable credentials, and E-E-A-T signals more heavily than any other factor. A firm with weak external authority cannot be optimized into AI recommendations through on-site content alone.
Brand Authority Architecture is the work of building the citation footprint AI models actually trust: authored content in industry publications, podcast and press placements, Wikipedia and Wikidata presence where warranted, authoritative directory inclusion, and the structured author-bio and credentials schema that connects claims on your site to verifiable sources off it.
What's included
- E-E-A-T audit of existing author and firm credentials
- Third-party citation gap analysis against category leaders
- Wikipedia and Wikidata entity review and (where eligible) development
- Expert positioning and contributed-content strategy
- Author schema (Person, hasCredential) and sameAs linking
Who it's for
Established firms whose on-site content is strong but whose off-site footprint does not yet match their real-world standing — and firms whose partners and principals are category experts but are not yet recognizable to AI systems as such.
Answer Engine Optimization is the on-site discipline of writing and structuring content so AI engines extract and cite it cleanly. The patterns that win are specific and testable: direct answers in the first 40–60 words, citation-optimal paragraph blocks of roughly 134–167 words, a verifiable fact every 150–200 words, consistent heading hierarchy, and complete schema markup appropriate to your category (Attorney, FinancialService, MedicalBusiness, Physician, FAQ, Person).
We rewrite and restructure service pages, FAQ libraries, and educational resources to meet these patterns — then validate the work by re-running the same prompts we used at baseline and observing whether your paragraphs are the ones being paraphrased.
What's included
- Content pattern audit against AEO extraction benchmarks
- Service-page and FAQ rewrites for citation extraction
- Schema implementation for your category (Attorney, FinancialService, MedicalBusiness, etc.)
- llms.txt and llms-full.txt authoring and maintenance
- Post-implementation measurement against baseline prompts
Who it's for
Firms with decent topical coverage but content written in the narrative, persuasive style of traditional marketing copy — which AI extractors routinely skip in favor of more machine-readable competitors.
The next phase of AI discovery is agentic. AI systems will not just recommend firms — they will initiate contact, qualify the prospect, propose meeting times, and hand a warm, pre-briefed lead to the human on the other side. Firms whose intake flows, scheduling, and service information are agent-readable in 2026 will be the default recommendation when the agentic layer matures over the following two years.
Agentic Commerce Readiness audits every surface an AI agent might touch on behalf of a prospect — from your contact page to your scheduling tool to the structured service and pricing information on your site — and closes the gaps that would cause an agent to drop off or route the prospect to a competitor.
What's included
- Agent-path audit: discovery → evaluation → contact initiation
- Machine-readable intake endpoints and scheduling integration review
- Service and pricing schema so agents can surface accurate comparisons
- llms.txt / llms-full.txt authoring (agent-facing)
- Crawler permissions review (GPTBot, ClaudeBot, PerplexityBot, Google-Extended)
Who it's for
Forward-looking firms that understand agentic workflows are not a 2028 problem — they are a 2026 positioning opportunity, and the incumbents that move first will be the ones AI agents default to.