This Overlooked Software Niche Makes $180K/Month (And Has 3 Competitors)

In January 2025, Sports Illustrated got caught publishing AI-generated articles under fake author names with AI-generated headshots. The backlash nearly destroyed the brand. A few months later, a major academic publisher retracted over 1,500 papers suspected of being AI-generated. And right now, as you read this, thousands of businesses are publishing AI content with zero way to prove which parts a human actually wrote, reviewed, or approved.

This is creating a market that barely exists yet — and the few companies operating in it are printing money.

I'm talking about AI content provenance and authenticity infrastructure: the tools that help organizations track, verify, and certify what's human-created, what's AI-assisted, and what's been reviewed and approved by a real person. Think of it as version control meets chain-of-custody, built specifically for the AI content era.

The market is massive. The competition is laughably thin. And the timing is almost perfect.

Why This Market Is Emerging Right Now

Three forces are converging to make AI content provenance not just useful, but mandatory.

Force 1: Regulation is arriving faster than anyone expected.

The EU AI Act, which began phased enforcement in 2025, requires that AI-generated content be labeled as such in a growing number of contexts. California's AB 2655 targets AI-generated content in political advertising. China already requires AI-generated content to carry visible labels. The direction is clear: governments worldwide are moving toward mandatory disclosure of AI involvement in content creation.

This isn't speculative. These are laws on the books. And most companies have zero infrastructure to comply with them.

Force 2: Platforms are cracking down.

Google's March 2024 core update explicitly targeted AI-generated content that doesn't add value. Amazon now requires sellers to disclose AI-generated product descriptions. Major stock photo platforms are segregating AI-generated images. Academic journals are implementing AI detection as part of peer review. Social media platforms are beginning to label AI-generated content.

Every platform that hosts user content is building or buying tools to handle this problem. But the tools available to content creators — the people who need to prove their work is legitimate — are almost nonexistent.

Force 3: Trust is becoming a competitive advantage.

Brands are starting to realize that being able to prove their content is human-created (or transparently AI-assisted) is a genuine differentiator. When consumers can't tell what's real, the companies that can verify their authenticity win. This is already playing out in journalism, where outlets like the BBC and New York Times have implemented content provenance standards. It's starting to spread to marketing, education, legal, and healthcare content.

What Exists Today (And Why It's Terrible)

Let me walk through the current landscape, because understanding what's broken reveals exactly where the opportunity sits.

AI detection tools like GPTZero, Originality.ai, and Copyleaks are the most visible players. They try to determine whether a piece of content was AI-generated after the fact. The problem? They're unreliable. False positive rates are high enough that no serious organization can use them as the sole basis for important decisions. They also can't tell you how AI was used — whether it generated a first draft that was heavily edited, suggested a few phrases, or wrote the entire thing untouched. They're a blunt instrument for a problem that requires precision.

Content Credentials (C2PA) is an open standard backed by Adobe, Microsoft, and others that embeds provenance metadata into media files. It's a good standard, but it's focused primarily on images and video, it requires adoption across the entire tool chain to work, and it doesn't address the most common use case: text-based content created in workflows involving AI assistants.

Watermarking solutions from companies like Google DeepMind (SynthID) embed invisible markers in AI-generated content. These are useful for detecting AI output from specific models, but they don't help organizations that want to track and certify their own content creation workflows.

What's missing — the gap that's wide open — is a workflow-level provenance platform that sits inside the content creation process and records what happened. Which parts did a human write from scratch? Where did AI assist? Who reviewed and approved the final version? When did each step happen? Can you prove it to a regulator, a platform, or a client?

That platform doesn't really exist yet. And the demand for it is accelerating.

The Actual Opportunity: What You'd Build

Picture a SaaS tool that works like this:

A content team installs a browser extension or integrates with their CMS (WordPress, Webflow, Notion, Google Docs). Every time someone creates content, the tool quietly tracks the creation process — keystroke patterns that indicate human writing vs. paste-from-AI, integrations with AI tools that log when suggestions were accepted, timestamps for human review and approval steps.

The output is a Content Provenance Certificate — a verifiable record that shows exactly how a piece of content was created. Think of it like a Carfax report, but for content. It answers: Who created this? What tools were involved? What was the human's role? Who approved it? When?

This certificate can be:

• Attached to published content as a trust signal (like an SSL badge for authenticity)
• Submitted to platforms as proof of compliance
• Provided to regulators during audits
• Shared with clients who require transparency about AI usage
• Used internally to enforce content creation policies

The technical implementation isn't science fiction. You're essentially building an audit trail system with browser-level activity monitoring, API integrations with popular AI tools (ChatGPT, Claude, Jasper, etc.), and a verification/certification layer. The AI components are straightforward — you're using AI to analyze creation patterns and classify the level of AI involvement, which is far simpler than trying to detect AI after the fact.

Who Pays For This (And How Much)

This is where it gets interesting, because there are multiple buyer personas willing to pay real money.

Content agencies and marketing teams are the most immediate market. Agencies are already getting asked by clients whether their deliverables are AI-generated. Some clients are putting AI disclosure requirements into contracts. An agency that can attach a provenance certificate to every deliverable has a concrete competitive advantage. Pricing: $200-500/month per team.

Publishers and media companies face both regulatory pressure and audience trust issues. A mid-size publisher managing hundreds of articles per month needs a systematic way to track and verify their content creation processes. Pricing: $500-2,000/month depending on volume.

Academic institutions are drowning in AI-related integrity issues. A provenance tool that students and researchers use during the writing process — proving their work is genuinely theirs — is far more reliable than after-the-fact detection. Pricing: $5,000-50,000/year per institution (education SaaS pricing).

Enterprise compliance teams in regulated industries (healthcare, finance, legal) need to prove that their published content meets regulatory standards for AI disclosure. This is an enterprise sale: $2,000-10,000/month.

E-commerce brands publishing product descriptions, reviews, and marketing copy increasingly need to comply with platform requirements around AI disclosure. Pricing: $100-300/month for SMBs.

Conservatively, a focused product serving content agencies and mid-size publishers could reach $180K/month within 18-24 months. The enterprise and education verticals push the ceiling much higher.

If you're wondering what separates SaaS ideas that actually make money from those that don't, this one checks the critical boxes: regulatory tailwind, multiple buyer personas, recurring need, and increasing urgency over time.

The Competitive Landscape Is Almost Empty

I count three companies doing anything close to this, and none of them are nailing it.

Originality.ai is the closest, but they're primarily an AI detection tool that's added some team management features. Their core value proposition is still "scan content to see if AI wrote it" — which is fundamentally different from "track the creation process and certify what happened." Detection is forensic. Provenance is proactive. Different products for different use cases.

C2PA/Content Credentials is a standard, not a product. Adobe's implementation is focused on creative assets (images, video, PDFs), and it requires the entire tool chain to support the standard. There's no turnkey SaaS product that makes this work for text-based content workflows.

A handful of startups in the digital watermarking space (like Steg.AI) are working on embedding provenance into media, but again, they're focused on images and video, not the text content workflows where most of the regulatory and business pressure is concentrated.

The gap is a dedicated, workflow-integrated provenance platform for text content. Nobody owns this yet.

Compare this to what I've seen in other overlooked vertical markets — the pattern is the same. A clear, growing need. Fragmented or nonexistent solutions. Multiple willingness-to-pay signals. And a window that won't stay open forever.

Why AI Makes This Buildable by a Small Team

Five years ago, building a content provenance platform would have required a massive engineering team. You'd need sophisticated NLP to analyze writing patterns, complex browser instrumentation, and expensive infrastructure for processing and storing audit data.

Today, the core technical challenges are dramatically simpler:

Writing pattern analysis — distinguishing human typing patterns from paste-from-AI patterns — can be built using relatively straightforward heuristics combined with lightweight ML models. You're not trying to determine if any arbitrary text is AI-generated (which is genuinely hard). You're monitoring the creation process in real time, which gives you vastly more signal: typing speed, revision patterns, copy-paste events, API calls to AI tools.

Browser extension development has mature frameworks and well-documented APIs. A Chrome/Edge extension that monitors writing activity in Google Docs, Notion, WordPress, and other web-based editors is a well-understood engineering problem.

API integrations with AI tools are increasingly available. OpenAI, Anthropic, and others provide usage APIs that can be integrated to log when and how AI assistance was used.

Certificate generation and verification is essentially a signed document problem — well-trodden territory with existing libraries and standards.

A solo developer or small team using modern AI-assisted development tools could build an MVP of this in 8-12 weeks. The initial version could focus on a single integration (say, Google Docs + ChatGPT) and a single buyer persona (content agencies), then expand from there.

I track opportunities like this at SaasOpportunities, and the ones that excite me most are exactly this pattern: clear demand signal, thin competition, and a technical build that's become dramatically easier because of AI tools.

The Moat: Why This Gets Harder to Compete With Over Time

The obvious concern with any SaaS idea is: what stops a bigger company from copying this? Several things work in your favor here.

Data network effects. Every piece of content processed through the platform generates data about creation patterns, AI usage patterns, and verification standards. Over time, this data makes the provenance analysis more accurate and the certificates more trustworthy. A new entrant starts with zero data.

Integration depth. The more CMS platforms, AI tools, and publishing workflows you integrate with, the harder it is for a customer to switch. If your provenance tool is embedded in a team's Google Docs, WordPress, Notion, and Jasper workflows, ripping it out is painful.

Trust and reputation. A provenance certificate is only valuable if the recipient trusts the issuer. The first platform to establish itself as the "standard" for content provenance certificates has a significant trust advantage. This is similar to how SSL certificates work — the technical implementation is commoditized, but the trust infrastructure is the moat.

Regulatory relationships. As AI content regulations mature, the platforms that help shape compliance standards (through industry participation, regulatory feedback, etc.) become the de facto tools for meeting those standards. This is a classic regulatory moat.

If you're evaluating this against the filters that predict SaaS success, the moat potential alone puts it in a different category than most micro-SaaS ideas.

The Go-To-Market Play

The distribution strategy for this product is unusually clear, which is one of the reasons I find it compelling.

Phase 1: Content agencies (months 1-6). Agencies are the ideal early adopter because they have an immediate, concrete pain point (clients asking about AI usage), they're concentrated in identifiable communities (agency Slack groups, industry conferences, LinkedIn), and they make purchasing decisions quickly. The pitch is simple: "Attach a provenance certificate to every client deliverable. Win more business by proving your content integrity."

Phase 2: Publisher and media partnerships (months 4-12). Once you have agency traction and case studies, approach mid-size publishers. The pitch shifts to compliance and audience trust. Partner with one or two recognizable publications to create a visible "Verified Content" badge program — this generates press coverage and organic demand.

Phase 3: Education and enterprise (months 8-18). These are longer sales cycles but much higher contract values. By this point, you have a proven product, real usage data, and recognizable logos. The education play is particularly interesting because academic integrity is a massive, well-funded pain point right now.

Content marketing is the obvious distribution channel. Every article about AI content, AI regulation, AI detection, or content authenticity is an opportunity to position your product. The SEO landscape for "AI content verification," "content provenance," and "AI disclosure compliance" is remarkably uncompetitive right now — most results are news articles and think pieces, not product pages.

Pricing Architecture

The pricing model should reflect the value delivered, which varies significantly by customer segment:

Starter ($99/month): For freelancers and small teams. Track provenance for up to 50 pieces of content per month. Basic integrations (Google Docs, WordPress). Standard provenance certificates.

Professional ($299/month): For agencies and content teams. Unlimited content tracking. All integrations. Branded provenance certificates. Client-facing dashboard. Team management.

Business ($799/month): For publishers and larger organizations. Everything in Professional, plus API access, custom compliance reports, bulk certificate generation, and priority support.

Enterprise (custom): For institutions and regulated industries. Custom integrations, dedicated compliance features, SLA guarantees, and on-premise options.

This pricing structure targets an average revenue per account of $250-400/month, meaning you need roughly 450-720 customers to hit $180K MRR. For a product with multiple distinct buyer personas and strong organic demand signals, that's realistic within 18-24 months.

The Risks (And Why They're Manageable)

Every opportunity has risks. The honest ones here:

Risk: AI detection gets good enough to make provenance unnecessary. This is the biggest bear case. If someone cracks reliable AI detection, the "prove it during creation" approach becomes less critical. But the trend is moving in the opposite direction — as AI models improve, detection gets harder, not easier. And provenance provides something detection never can: a positive proof of how content was created, not just a probability score of whether AI was involved.

Risk: Big platforms build this in. Google Docs could add provenance tracking. WordPress could build it natively. This is possible, but platform companies have historically been slow to build compliance and certification features — they prefer to let ecosystem tools handle it. And even if a platform builds basic tracking, a dedicated provenance tool that works across all platforms remains valuable.

Risk: Regulation stalls or reverses. If AI content regulation doesn't materialize, one of the three demand drivers weakens. But the other two — platform requirements and market trust — are independent of regulation and already active.

Risk: Privacy concerns about monitoring content creation. This is a real design challenge. The tool needs to track enough to verify provenance without capturing sensitive content. The solution is to record metadata and patterns, not actual content — similar to how Git tracks changes without requiring you to store every keystroke.

None of these risks are existential. They're design constraints and market uncertainties that any competent founder can navigate.

Why the Window Won't Stay Open

Markets like this follow a predictable pattern. Right now, we're in the "early signal" phase — regulation is passing, platforms are cracking down, and businesses are starting to feel the pain. Within 12-18 months, we'll be in the "obvious opportunity" phase, where every SaaS newsletter is writing about content provenance and funded startups are entering the space.

The founders who build now — even with an imperfect MVP — will have data, customers, and integrations that late entrants can't replicate quickly. First-mover advantage in trust infrastructure is particularly strong because trust compounds over time.

This is the kind of market shift I wrote about in the SaaS gold rush piece — a regulatory and technological inflection point that creates a new category of software. The question isn't whether this category will exist. It's who will own it.

What I'd Build First

If I were starting this tomorrow, here's the specific MVP I'd scope:

Week 1-3: Build a Chrome extension that monitors writing activity in Google Docs. Track typing patterns, paste events (with source detection — did the paste come from ChatGPT's interface?), and editing patterns. Store metadata only, not content.

Week 4-6: Build the provenance certificate generator. Take the tracked metadata and produce a clean, shareable report: "This document was created over 4 hours. 73% of the text shows human typing patterns. 2 paste events from external sources were detected. The document was reviewed and approved by [name] on [date]." Make it embeddable and verifiable via a unique URL.

Week 7-9: Build the team dashboard. Let agencies manage multiple writers, set content creation policies (e.g., "AI assistance allowed for research but not for final copy"), and generate provenance certificates for client deliverables.

Week 10-12: Launch to 20-30 content agencies. Offer free trials. Gather feedback. Iterate.

That's a focused, buildable MVP that delivers immediate value to a specific buyer persona. Everything else — additional integrations, enterprise features, education tools — comes after you've validated the core value proposition with paying customers.

The Bottom Line

The AI content provenance market is one of those rare opportunities where the demand is clearly forming, the technology to build a solution is accessible, and the competitive landscape is nearly empty. It's driven by forces — regulation, platform policy, market trust — that are accelerating, not slowing down.

The companies that build provenance infrastructure now will be the ones that define how AI-created content is tracked, verified, and trusted for the next decade. That's a category-defining opportunity, and it's sitting there waiting for someone to build it.

If you're looking for a SaaS idea that's innovative, timely, and has genuine defensibility, this is one worth serious consideration. Start with the Chrome extension. Start with content agencies. Start small and prove the value. The market will come to you.

The window is open. It won't be for long.