Arobis AI vs PitchGen

Arobis AI and PitchGen are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Arobis AI

Arobis AI runs structured audits against real buyer prompts across ChatGPT, Gemini, Claude, and Perplexity, then restructures your content and entity signals so AI engines cite you instead of skipping you. The workflow moves through three stages: audit what AI surfaces about you, restructure content for semantic clarity, and build authority signals that AI models use to decide who gets recommended. This is a done-for-you service, not a software platform — there is no dashboard to log into, no API to wire up, and no self-service configuration. Teams that need real-time competitive monitoring or want to run their own prompt tests are dependent on Arobis to surface that data. Because pricing is custom and the service model is agency-style, iteration speed is tied to the engagement cadence, not your sprint cycle.

PitchGen

Pitchgen scrapes a prospect's website, runs a digital marketing audit, and outputs a formatted presentation built for agency sales conversations. The one-shot workflow means a consultant can walk into a pitch with data-backed slides without manually assembling screenshots and SEO findings. The ceiling appears when a client wants custom audit logic, deep technical SEO output, or branded deliverables that diverge from Pitchgen's templates — at that point, teams are back in PowerPoint finishing what the tool started. White-labeling is available, though it sits behind a paid tier. No self-hosted option exists, so any data passing through the audit is on Stack Max LLC's infrastructure.

Attribute	Arobis AI	PitchGen
Pricing	Paid	Paid
Price	—	$29/mo
Free trial	No	7 days
Open source	No	No
Has API	No	Yes
Self-hosted option	No	No
Platforms	Web-based SaaS platform	Web-based / Cloud SaaS
Pros	Audits run against actual buyer prompts across ChatGPT, Gemini, Claude, and Perplexity simultaneously, so you see your real AI Share of Voice instead of inferring it from proxy metrics. Content restructuring targets semantic clarity and entity signals — the specific signals AI engines use to decide who gets cited — which means optimization effort is not wasted on factors that move Google rankings but have no effect on generative answers. Authority Engineering builds citation signals across the web, so your brand accumulates the external trust footprint that AI models weight when selecting sources rather than relying solely on on-site content. The service model handles the diagnostic and execution work, so marketing teams without in-house GEO expertise can close the AI visibility gap without hiring or retraining before the category is locked in. The free AI Visibility Audit provides a concrete baseline of where your brand surfaces across AI platforms before any engagement begins, so the decision to proceed is grounded in actual data rather than vendor claims.	One-URL-to-deck generation collapses a multi-tool audit workflow into a single step, so consultants running ten prospect conversations a week are not spending evenings assembling slides. API access lets higher-volume agencies pipe audit outputs directly into CRM or proposal tooling, so the tool can fit inside an existing sales workflow rather than sitting beside it. White-label output keeps client-facing decks free of third-party branding, so agency owners can present findings without explaining what tool generated the report — though this is a paid-only feature. Freemium entry point with a trial period means a consultant can validate the output quality against real prospects before committing to a paid tier, avoiding a blind spend on a workflow they haven't tested. Built specifically for digital marketing audit scenarios, so the output framing and slide logic matches what agency sales conversations actually require — unlike generic AI deck builders that need heavy prompt engineering to produce relevant structure.
Cons	There is no self-service dashboard or software platform — competitive Share of Voice data, prompt test results, and optimization progress are delivered through the service engagement, not pulled on demand. Teams that need to monitor AI visibility weekly on their own schedule cannot do that here. The service model ties iteration speed to engagement cadence. When a product launch or category shift requires rapid content signal updates, waiting on a service cycle is a hard constraint — not a workflow preference. Teams running high-frequency content experiments move to in-house GEO tooling or software platforms that let them push changes and measure AI response without an external dependency. No API and no self-hosted option means the service cannot be wired into an existing marketing data stack or analytics pipeline. Reporting lives inside the engagement, not inside your BI tools. The vendor site went live in early 2025, which means the track record, case study depth, and long-term citation durability of the optimization work are unproven at the scale and time horizon that enterprise procurement requires. Teams with rigorous vendor evaluation criteria will have limited third-party validation to reference.	Audit depth is bounded by what the tool surfaces in a single automated pass — consultants whose clients expect crawl-level technical SEO analysis, Core Web Vitals detail, or competitor backlink depth will find the output thin and end up supplementing it manually, which erodes the time advantage entirely. Template-driven output means any client with strong brand standards or a non-standard deliverable format gets a deck that requires significant post-processing — at scale, teams doing this for every pitch are effectively maintaining a PowerPoint workflow on top of a tool they're paying to avoid one. No self-hosted option means client website data is processed on Stack Max LLC's infrastructure; agencies with enterprise clients who have data handling requirements in their contracts will hit a compliance conversation that Pitchgen cannot resolve, at which point those teams route those engagements to a manual or self-hosted audit stack. Free-tier output includes Pitchgen branding, so any consultant using the trial period in a live client-facing context is either disclosing the tool or upgrading before the first real pitch — the trial effectively has a shorter useful window than its length suggests.

Bottom line

Only PitchGen exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Arobis AI and PitchGen?

Arobis AI is Paid, while PitchGen is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Arobis AI better than PitchGen?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.