# How Actively's Per Account Agents use Parallel to turn the entire web into a proactive sales intelligence layer

## Key highlights

Customers using Actively with Parallel’s web intelligence APIs report:

• - 23% higher win rates
• - 25% increase in revenue per rep
• - 2x conversion rates
• - 2x faster rep ramp time

## The limits of legacy sales intelligence

Most sales intelligence tools work from the same playbook: track a fixed list of trigger events (leadership changes, funding rounds, job postings) and blast them to every customer the same way. The result is predictable. Every competitor sees the same CEO appointment on the same day and sends the same congratulatory email.

But the deeper problem isn't speed. It's depth. The information that actually reveals buying intent falls into two categories, and legacy tools struggle with both.

• - Unbounded facts that provide deep structural context about the Account like what technology stack a company runs on, strategic initiatives, funding rounds, etc. This information augments Agents in real-time in making decisions like account qualifications. Changes to these facts can signal big structural changes on the account.
• - Point-in-time facts that provide stateful information about an account that is subject to frequent changes like countries of operation, current tools used, etc.

Legacy tools track a handful of point-in-time events with no context. They ignore unbounded facts entirely because they can't be reduced to a firmographic field. The result: reps get the same shallow alerts as everyone else, with no understanding of why a signal matters for their specific deal.

## Per Account Agents: from fetching information to reasoning about it

At the core of Actively's architecture is the Per Account Agent (PAA), an AI agent assigned to every account in a customer's book of business. The PAA doesn't just collect data. It reasons about what information matters, goes and gets it, and determines what it means for this specific seller and account. You can read more about them here[here](https://www.actively.ai/blog/human-agent-collaboration).

This distinction is critical. Parallel's infrastructure handles the retrieval: searching the web, monitoring public sources, fetching news and company information at scale. But the intelligence lives in the PAA. When Parallel surfaces new information, the PAA is the one that decides: does this matter? For whom? What should happen next? The separation is deliberate: Parallel is exceptional at getting information from the web reliably and at scale. Actively's PAAs are built to reason through that information in the context of a specific customer's ICP, value proposition, and win patterns.

## Two types of facts, two Parallel APIs

The PAA's job is to build and maintain a living picture of every account. That picture is made up of both point-in-time facts and unbounded facts, and each requires different infrastructure.

**Unbounded facts via Parallel's Task API.** When a PAA needs to understand the structural context of an account (what property management software a landlord runs, how many units they manage, what their tech stack looks like), it issues deep research tasks through Parallel's Task API. These are the foundational facts that don't change on a daily basis but are essential for developing a deep POV on an account, qualifying an account and crafting relevant outreach. The Task API lets PAAs gather this information across the public web in minutes rather than the hours it would take a human researcher.

**Point-in-time facts via Parallel's Monitor API.** Once a PAA understands the landscape, it needs to know when that landscape shifts. The PAA identifies the specific point-in-time facts that would change the account's status (a new executive hire, a technology migration, a product launch, a regulatory filing) and sets up Parallel monitors for each one. When one of these facts changes, the Monitor API alerts the PAA in real time.

This is where Parallel's infrastructure proved uniquely valuable. The alternative, regularly polling every source and re-reasoning through the results, doesn't scale. Actively needed the ability to set a large number of highly specific, narrow monitors across hundreds of thousands of accounts, each tuned to the particular facts that matter for that account. Parallel was the only provider whose Monitor API could support this: proactive, always-on monitoring at scale across a narrow set of account-specific questions, rather than broad, generic event tracking.

## Proactive intelligence, two ways

What makes this architecture powerful is that the PAA doesn't wait to be told what to look for. It proactively identifies what information is needed for each account and spins up monitors accordingly. If a PAA determines that a prospect's technology migration status is the highest-signal question, it creates a monitor for exactly that, without a human having to configure it.

But the system also works in the other direction. Customers bring their own domain expertise, telling Actively's browsing agents what patterns to watch for across their market. The result is a two-way feedback loop: customer insight shapes what the agents look for, and the PAAs autonomously identify account-specific monitoring needs that no human would think to configure manually.

The monitoring layer grows with the accounts it covers rather than being hand-configured up front. Every account is continuously watched. Every signal is reasoned about, not just detected. Every action is grounded in real-time web evidence.

## What's next

Actively and Parallel are continuing to push the boundaries of what proactive GTM intelligence can look like. As Parallel's monitoring and search capabilities evolve, the partnership is focused on making AI-native GTM the standard, where every sales team has AI agents that know their market better than any human researcher could.