INTEGRATING LEGACY SYSTEMS for a call center is hard enough, and now other customer support channels like e-mail and chat have to be tied in as well. Stream International, based in Canton, Mass., knows how to tackle that challenge. Stream provides technical support for customers of high-tech companies such as RoadRunner and Sirius Radio (and other more familiar names). The company's 10,000 agents handle about 35 million phone calls, chat sessions and e-mail messages per year.

CIO contributing writer Louise Fickel spoke with Lloyd Linnell, senior vice president and CTO at Stream, to find out how Stream's technology infrastructure integrates customer touchpoints to put the right information on its agents' screens.

Linnell has worked in IT management and product development for more than two decades; his resume includes work on early versions of voicemail, ISDN and cell-based network technology. He joined Stream as CIO in 1996.

CIO: Let's talk first about the IT infrastructure that handles all of your calls and e-mail messages.

Linnell: We built a product suite, Emediate, which integrates best-of-breed technologies across all of the various touchpoints: voice, e-mail, chat, collaboration, and Web self-help. We chose Kana as our e-mail engine, Net Effect's NEware for chat and collaboration, and Malloy's Cognitive Processor as our knowledge engine. For our ACDs, we use Aspect in North America and Nortel in Europe. (Automatic Call Distributors route incoming calls, based on the number called and a database of handling instructions.)

We tied all of those products together with IBM's MQ Messaging Bus and then connected everything to our back-end eCRM system. So if a customer were to start an interaction with our company via e-mail and later pick up the phone or do a chat, the agent would have at their disposal all of the past history and know what solution had been undertaken.

You mentioned neural networks. How does that technology work for Stream?

Every time a solution is found to work, the call center agent marks it. Over time the network learns [that] the more a particular piece of knowledge is used, the more likely it will be suggested the next time an agent has to solve a similar problem. The knowledge will age itself out of the system if it doesn't get used for a period of time. It's much like the human body in that if you use something, it gets stronger.

And what about the network itself -- that seems especially relevant as we're talking about making all of this updated knowledge available to your agents. What is your network infrastructure?

We have a frame relay virtual private network that connects all of our sites in North America and Europe. Our frame relay network is augmented in certain areas with point-to-point facilities for very high bandwidth connections. Everything is fully redundant so that if a link or a site goes down, we can re-route it to that same facility a different way. We're moving to an ATM-based network because we're also involved

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