Home Case Studies Signal-Based GTM Automation
Client Zero
Multi-step AI workflows across six systems — run on our own business
How we built and operate a signal-based go-to-market system for SynAdopt and ActiveLoc, and what it teaches about agents in production.
Last reviewed: 29 September 2026
Organisation
SynAdopt and ActiveLoc (our own operations)
Workflow
Prospect research, qualification, outreach preparation and CRM updates
Systems
Workflow orchestrator, B2B data and enrichment tools, professional-network research, email outreach platform, CRM
Good prospecting doesn’t scale by hand
For a lean team, finding the right companies at the right moment is slow: research across several data sources, qualification against changing criteria, message preparation, and keeping the CRM current. Doing it manually limited volume; buying a single all-in-one platform would have locked us into its data and costs before volume justified them.
What we worked with
- Several data and enrichment sources with different coverage and costs
- Buying signals such as hiring, funding and technology changes
- An email outreach platform and a CRM
- An orchestration layer connecting them, with AI steps for research and drafting
How we built it
1
Defined the workflow in stages
from signal detection to CRM update — so each step could be tested on its own.
2
Made the ideal-customer criteria measurable
scoring rules that update as results come in, rather than a fixed list.
3
Used AI where judgement is needed
summarising research and drafting personalised messages — and rules everywhere else.
4
Added human approval points
no message is sent and no record is overwritten without review at agreed stages.
5
Controlled cost
cheaper data sources first, premium enrichment only for qualified records.
6
Monitored everything
volumes, costs, data quality and outcomes by stage.
Controls we apply
- Human approval before outreach is sent
- Personal data collected only from appropriate sources and processed for a defined business purpose, with opt-out honoured
- Separate credentials for each tool and workflow
- Logs of every AI-generated draft and every automated update
What changed
The team spends its time on conversations rather than research. Qualification criteria improve every month because results feed back into scoring. Costs stay predictable because expensive steps run only when earlier steps justify them.
Lessons that transfer to any multi-step AI workflow
Break the work into stages
you can test, monitor and improve separately.
Put humans at the decision points
, not in every step.
Let results update the rules.
Static criteria go stale quickly.
Design for cost from the start.
Model and data costs grow with volume.
These patterns apply directly to order intake, supplier follow-up, service triage and other cross-system workflows.
See this system live in the Executive Workshop.