Enterprise AI and go-to-market systems
Strategic account intelligence platform
A governed intelligence system that helped sales teams and leaders move from fragmented account research to current, evidence-backed decisions.
- Audience
- Sales teams, technology leaders, and supporting organizations
- Role
- Architect, research lead, team lead, and principal builder
The problem
Account strategy was constrained by the cost of assembling context.
Research was spread across disconnected systems, repeated by hand, and stale by the time teams used it. A forty-hour process limited formal coverage, left sellers switching between sources, and made relationship history, buying signals, and opportunity decisions difficult to carry forward.
The solution
One governed system connected signals, decisions, and action.
I led the design and build of a hybrid intelligence platform that combined company and employee research, market and financial signals, relationship history, opportunity evidence, communication guidance, and public-sector coverage. The system selected the appropriate model for each workflow, preserved the evidence behind recommendations, and kept people responsible for validation and action.
Outcomes
The result was a change in operating capacity.
These outcomes describe what this implementation made possible. They are not generic product promises.
40 → 4–6
hours per initial account brief
Reduced a manual, multi-source research process to a governed same-day workflow with human review.
50 → 100+
strategic accounts covered
Expanded formal intelligence coverage beyond the capacity of the previous research model.
90 → <7
days between intelligence refreshes
Replaced quarterly snapshots with current signals, ongoing monitoring, and proactive alerts.
~5.7×
first-year return
Produced approximately $2.3 million in value from a $400,000 investment through capacity, coverage, and operating gains.
How it worked
The architecture followed the work.
The system was organized around an operating sequence people could understand, govern, and improve.
Collect
Bring company, people, market, financial, relationship, and public-sector signals into a common evidence model.
Synthesize
Turn fragmented data into account models, opportunity maps, risks, and communication guidance.
Remember
Preserve evidence, decisions, recommendations, and outcomes so future work retains provenance and continuity.
Act
Prioritize informed opportunities and next actions while keeping review and judgment with the people accountable for the relationship.
My contribution
I carried the work from research thesis through operating system.
I developed the underlying research, shaped the architecture, led the team, and remained a principal builder. The result drew on earlier work in agent orchestration, recursive decision systems, knowledge continuity, and human-governed automation.
Return to all work