Four weeks to a GTM system their team could run.

Strong technology. No repeatable route to the right buyers.

Prediction Guard had strong technology and real demand signals. The missing piece was a repeatable go-to-market operation that could put the company in front of the right accounts and turn interest into conversations.

The team needed a partner who could shape the strategy, build the systems and leave the knowledge behind. Stan worked directly with Prediction Guard to connect account research, outreach, account-based marketing (ABM) and answer engine optimisation (AEO).

A full GTM system, delivered in four weeks.

The build brought market research, account and contact scoring, buying signals and CRM-connected outreach into one operation. Alongside the campaigns, frontBrick built ABM and AEO systems so the team could work on account engagement and visibility beyond the inbox.

• Find and prioritise the market. Clay research and scoring helped the team evaluate more than 30,000 people and focus on relevant accounts and contacts.
• Connect ABM and outreach. Account context, buying signals and persona-specific messaging shaped the campaigns and the next step for each conversation.
• Build for AI visibility. An AEO system supported work to secure relevant article placements and improve how Prediction Guard could be discovered in AI search.
• Make it operable internally. Six live outreach campaigns, reusable Claude skills and eight written and video standard operating procedures supported the handover.

70 interested leads from a month of LinkedIn outreach.

From 16 September to 16 October 2026, the LinkedIn campaign sent 457 connection requests and received 230 acceptances, a 50.3% acceptance rate. Of 214 people messaged, 103 replied, a 48.1% reply rate. The campaign recorded 70 interested leads.

Campaign activity from 16 September to 16 October 2026. Analytics exported 17 October. Open the original for a closer look.

People at Apple, Meta, Ford, Uber, Amazon, Palantir and Vanguard were among those who expressed interest. These are companies represented among the leads, not a list of newly won customers.

From an interested reply to a useful sales handoff.

The first week produced 11 interested leads. The same campaign subsequently reached 70. Interested replies were routed into Slack and HubSpot with company and conversation context, giving the team a clear next step.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Ford Motor Company
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Apple
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Vanguard
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Palantir Technologies
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Meta
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Amazon
Contact updated in HubSpot and added to leads pipeline.

Clay · #cold-outreach-leads
New interested lead in HeyReach.
Company: Uber
Contact updated in HubSpot and added to leads pipeline.

Seven interested-lead notifications from the campaign. Names and profile details are hidden.

Client feedback from the campaign. Open the original for a closer look.

ABM and AEO became part of the same operating system.

ABM focused the work around relevant accounts and the people involved in their buying decisions. Research, segmentation, messaging and handoff shared that context, so the team could carry it from campaign planning into a sales conversation.

The AEO build addressed another part of discovery: getting Prediction Guard into relevant articles and improving visibility when buyers research the category through AI. It gave the team a system for this work alongside outbound. The measured lead results shown here come from the LinkedIn campaign; no separate AI visibility lift or article-placement total is claimed.

The team could take over after handover.

Documentation was part of delivery. The engagement included eight written and video SOPs and a live walkthrough. Prediction Guard’s Clutch review describes the team beginning to operate parts of the system internally within a day of handover.

“Operating the new system after handover was very smooth, largely because of how thorough the handover was.”

Sharan Shirodkar
Director of GTM Engineering, Prediction Guard
5.0 Clutch review · September 2026

Read the verified Clutch review ↗

Strategy and execution, from the same partner.

“What we needed was someone who could think at the strategy level and then actually build the thing. Stan did both, which is rarer than it sounds.”

Catherine Bowen
Director of Go To Market, Prediction Guard

Catherine Bowen’s public LinkedIn recommendation, 22 September 2026. Open the original for a closer look.

The result was both a working campaign and an internal capability: an audience strategy, connected workflows, documented processes and a team equipped to keep improving them.