We offer one of the best anti-spam solutions available. This solution was born in the crucible of the EFF(Electronic Freedom Foundation) and has been refined since 1991.

Here's what some of our customers are saying:

"I usually have 200 emails sitting in my mailbox, but I only had about 11"
Insurance Company, Skip Jue, Owner

"Now that i think about it ... there has been nothing. That is good stuff. "
Logiquick, Jahi Toler, Owner

Here are the techniques we use to filter incoming email:

• Bayesian Logic: Based on stream of what words are known to exist in Spam and which words are known to exist in Ham(legitimate emails). This is based on the same technique that Google uses for processing.
• Header Analysis: Spammers use a number of tricks to mask their identities, fool you into thinking they've sent a valid mail, or fool you into thinking you must have subscribed at some stage. Our solution tries to spot these tricks.
• Whitelisting: A valid list of email addresses and domains which are allowed to send email to our system.
• Text Analysis: again, spam mails often have a characteristic style which our system tracks.
• Blacklists: Our solution supports many useful existing blacklists, such as spamhaus.org. Blacklisting based on what spam links too. Our blacklist is also based on IP addresses. We are very careful about being overly aggressive with blacklists, as often times legitimate domains can be blacklisted by them.
• Razor: Vipul's Razor is a collaborative spam-tracking database, which works by taking a signature of spam messages. Since spam typically operates by sending an identical message to hundreds of people, Razor short-circuits this by allowing the first person to receive a spam to add it to the database at which point everyone else will automatically block it.
• Pyzor: An open source in implementation of Vipul's Razor

Once identified, the mail can then be optionally tagged as spam for later filtering using the user's own mail application.

If incoming email receives a high score, it is most likely spam and will be automatically deleted. The chances of a message not being spam, but being rated as spam are approximately 1 in 100,000, so false positives should be minimal.

Spam that falls into the grey area of what is or is not spam, will be rated as "low spam" and may be filtered by your email application into an appropriate folder. Spam which receives a "no spam" score will almost certainly be legitimate email.