SaaS & Technology
Keep trial abuse out of your funnel, protect docs and pricing from scraping, and let real evaluators reach signup unchallenged.
Software has to stay open to sell
Competitors scrape pricing and documentation. Fraud rings farm free trials. Credential lists get replayed against your login page. Your own customers run uptime checks, and buyers now send AI agents to read the docs before a human arrives.
Lock it all down and you block the evaluation that produces pipeline. CHEQ classifies each entity: what it is, whether it is authentic, and what it intends, so you can respond proportionally at signup, login, and docs.
Keep software growth open to buyers, not to abuse
Software growth runs on low-friction access – free trials, public documentation, self-serve signup. Each of those is also where unwanted automation arrives first.
Qualify signups
Separate real trial and demo requests from bulk automated signups before sales works them.
Secure accounts
Catch automated login attempts before an attacker reaches a workspace full of customer data.
Enable customer automation
Your customers' own integrations and uptime checks keep working, while unapproved automation does not.
Trust product data
Automated sessions stay out of product and campaign analytics, so roadmap and spend decisions hold.
The automation software actually faces
Software threats cluster where the product is open – the docs, the pricing page, the free trial, and the login. Each calls for a different response.
Free trial and signup abuse
Bulk automated signups claim free tiers at volume, so trial capacity and infrastructure cost go to accounts that were never going to buy.
Credential stuffing
Attackers replay breached password lists against your login page. Because software logins get reused across a team, one match can open a whole workspace.
Workspace takeover
Once credentials work, the session looks like an ordinary user, so exports, new seats, and settings changes all happen before anyone flags the account.
Docs and pricing scraping
Competitors track your pricing page hourly, and scrapers pull whole documentation sets. You want to be read by buyers, not copied wholesale.
AI agents and customer automation
Buyers send AI agents to read your docs and compare plans, and customers run uptime checks. Blocking either costs deals and support goodwill.
Fake demo requests
Automated submissions fill demo and contact forms, so SDRs work a queue of accounts that never existed and your sending reputation drops with it.
Paid search waste
Automated clicks drain the category and competitor campaigns software companies bid hardest on, so cost per real evaluation climbs while reports still look healthy.
Polluted product analytics
Non-human sessions inflate documentation views, pricing page visits, and trial starts, so the roadmap and the campaign budget both get planned against automation.
Third-party script risk
Analytics, chat, and experiment scripts ship faster than anyone reviews them. On a signup or login page, a changed script quietly touches customer data.
Why this holds up on a self-serve product
Self-serve means no human reviews the signup, and your attackers build software for a living. The classification has to be right in the session.
Triple-layer intelligence
Trial farmers rotate infrastructure faster than they rebuild identity, which is where the third layer catches them.
Proportional enforcement
Constrain a questionable trial instead of blocking it, and a real evaluator never meets a wall.
Explainable verdicts
When a customer disputes a blocked signup, the reasons behind the verdict are already recorded.
Calibrated to your funnel
You calibrate thresholds against your own confirmed abuse, so a free trial and an enterprise login differ.
SaaS compliance obligations
Buyers from many countries read your docs, start trials, and request demos, and analytics and ad tags collect data at every step.
Docs and signup consent
Hold analytics and experiment tags on docs and signup pages until an EU visitor gives consent.
California retargeting opt-outs
Helps keep the ad and retargeting tags on pricing and trial pages in line with a California visitor's opt-out.
Privacy control evidence
Real-time audit logs produce evidence of your privacy controls on demand, helping reduce the time spent preparing for audits.
Related solutions and industries
SaaS & technology questions
How does CHEQ tell an AI agent evaluating your product from a scraper copying it?
CHEQ separates them by classifying the entity and its likely intent, not by detecting that the visitor is automated. Both arrive as automation against the same public pages, so a rule that blocks automation blocks the buyer’s agent alongside the scraper.
Three questions carry the classification:
- What is it, the entity making the request and the operator behind it, rather than the user-agent string it declares
- Is it authentic, whether the identity it presents matches that operator
- What did it come to do, an informed inference from behavioral signals and history, not a certainty about any single agent
CHEQ surfaces that classification with the evidence behind it, and you set the policy that follows. In practice that usually means allowing an agent acting for a real buyer, constraining bulk extraction, and making a deliberate decision about training crawlers rather than defaulting either way.
How does CHEQ reduce free trial abuse without adding friction to real signups?
By assessing the entity behind each signup during the session, so most people never see a challenge. Friction on a self-serve funnel is expensive in its own right: a CAPTCHA at signup costs conversions from exactly the buyers you spent the most to reach.
Several signal types combine at the point of submission:
- User data validation, which flags suspicious or invalid email addresses and phone numbers on the form
- Identity Intelligence, covering risk scoring, synthetic identity signals, and cross-session consistency
- Behavioral and network signals, which reflect how the signup was completed rather than only what it contains
Enforcement is graduated rather than binary. A clean signup proceeds untouched, while bulk automated registration can be constrained or throttled.
Thresholds calibrate against your own confirmed abuse, so a generous free tier and a paid trial can carry different bars.
Can CHEQ protect documentation and pricing pages without blocking search engines and AI crawlers?
Yes. Not all automation is adversarial, and for a software company being read is part of how the product sells, so the goal is governing extraction rather than closing the pages.
CHEQ classifies each crawler as its own entity type, which supports different policy for:
- Search engine crawlers, where indexing your documentation and pricing is the point
- AI and LLM crawlers, where being cited in an assistant’s answer may be worth allowing even when a full corpus pull is not
- Competitive and commercial scrapers, where repeated extraction of pricing or documentation is the thing being constrained
- Undeclared automation, where the operator does not identify itself at all
Because enforcement is proportional, the response can be monitoring or throttling rather than blocking, which keeps a mistaken classification from quietly removing you from search results.
How does CHEQ keep fake demo requests out of the pipeline?
CHEQ assesses each form submission in real time and passes the verdict into your marketing and CRM systems, so a fake request is flagged before a rep works it.
In B2B software the cost of a fake lead compounds in three places:
- Sales capacity, spent on accounts that were never buyers
- Email reputation, degraded as sequences hit invalid addresses
- Funnel reporting, which shows demand that does not exist, so targets get set against it
Verdicts and threat data enrich each lead as it enters the funnel, which supports routing, scoring, and suppression rules in the systems you already run, including Marketo, HubSpot, and Salesforce. Detection is calibrated to your own lead quality standards rather than a generic default.
Will CHEQ block our customers' own automation?
Not by default. CHEQ surfaces a classification and your team sets the policy that follows, so automation you have approved stays approved.
This matters more for software than for most verticals, because your customers are technical and their automation is legitimate:
- Uptime and status checks, which run against your login and marketing pages on a schedule
- Partner and reseller activity, which arrives as automation on behalf of a real account
- Internal tooling, which your own teams point at your own properties
Those entities can be given their own policy, and because enforcement is proportional, an unrecognized one can be monitored or throttled rather than cut off while you work out what it is.
How does CHEQ fit into an existing software marketing and security stack?
CHEQ’s classifications are built to move into the systems your team already runs rather than living in a separate console. The data is designed to be granular, explainable, portable, and integrable.
On the marketing side, classifications feed Invalid Traffic Analysis so funnel reporting reflects real evaluators, support audience exclusion and IP blocking in Google Ads for Click Fraud Protection, and reach lead routing through Marketo, HubSpot, and Salesforce.
On the analytics side, pre-built connectors cover Adobe Analytics, GA and GA360, Amazon S3, and a retrospective data API for exporting classifications. CHEQ is an Adobe Gold Technology Partner, and 55% of its top 250 customers run on Adobe.
On the security side, classifications reach infrastructure through CloudFront and Cloudflare, so the verdict is available at the edge and not only in the console.
Ready to tell evaluators from scrapers?
Find out what your free trial is actually attracting before sales works the list.