Revenue flow
Business question
Where are leads, deals, and follow-ups losing momentum?
System decisions
Lifecycle design, routing, enrichment, attribution, pipeline controls, forecasting, and management reporting.
AI-native Salesforce and systems advisory
Salesforce and the systems around it: designed, built, and run as one operating environment.
GAT combines Salesforce administrators, developers, architects, Go-to-Market Systems Engineers, and AI engineers around fixed-scope implementations and ongoing systems ownership across marketing, sales, service, data, automation, and AI.
- 50+ Salesforce projects
- 100+ integrations
- 5.0 client rating
- 6+ years Salesforce
GAT Solutions
A Salesforce and go-to-market systems firm built for the work between strategy and production
GAT Solutions is an AI-native Salesforce consultancy. We help leaders decide the highest-leverage move, then architect, build, and launch the business systems (Salesforce plus AI) behind it: from patient communication and EHR integration in healthcare to go-to-market systems for SaaS teams. We’ve proven it in the most demanding, regulated environments, and we’ve built our own Salesforce AI products along the way.
The practice closes a familiar gap: business leaders can describe the operating problem, technical teams can describe the platform, and too few partners can translate cleanly between the two.
The practice combines 6+ years of Salesforce, 8+ years in enterprise systems, and 4+ years of applied AI. That depth spans commercial process, platform architecture, automation, integration, data, security, and adoption.
GAT works across greenfield Salesforce builds, fractional and managed ownership across administration, development, and architecture, go-to-market systems engineering, platform reliability, revenue and service operations, migrations, AI adoption, and AI product engineering. The through-line is making the operating system legible enough to build, govern, and run.
Revenue systemsSalesforce architectureCross-platform automationProduction AI
GAT delivery model
One line of accountability from problem to production
Whether the engagement is a fixed-scope implementation or ongoing ownership, senior discovery, architecture, and hands-on delivery stay connected throughout the work.
01
Frame the operating constraint
Start with the stalled handoff, missed revenue, service backlog, unreliable forecast, or manual control, not a preselected feature list.
02
Map the real workflow
Put the people, decisions, records, platforms, exceptions, and ownership on one page before deciding what Salesforce or AI should do.
03
Build the smallest complete system
Deliver the data model, automation, integrations, reporting, controls, and release path needed for the workflow to hold together in production.
04
Make it operable by the team
Validate with users, document the decisions, train the owners, and measure the workflow after launch so the system can improve instead of calcify.
Business + systems judgment
Every operating question creates an architecture decision
GAT works at that translation layer: what the business needs to control on the left, and what the system must make true on the right.
Operating area
Business question
System decisions
Case and service operations
Business question
Where do requests wait, lose context, or miss a service commitment?
System decisions
Case intake, matching, queues, entitlements, escalation, documents, knowledge, and service visibility.
Platform change
Business question
What must survive an implementation, migration, or consolidation?
System decisions
Process alignment, data mapping, automation rationalization, reconciliation, cutover, training, and ownership.
Production AI
Business question
Where should AI reason, where should rules decide, and who approves the write?
System decisions
Tool boundaries, permissions, deterministic controls, human gates, evaluations, monitoring, and revocation.
Operating principles
What stays true
One practice stays accountable
Discovery, architecture, and core delivery remain connected. The GAT practice hearing the business problem is accountable for how it becomes a working system.
Architecture follows operating reality
A clean diagram is not enough. The design has to survive exceptions, permissions, reporting needs, team habits, and the systems already in place.
AI earns a specific job
Use AI where judgment or unstructured context matters. Keep stable rules deterministic, make consequential actions reviewable, and measure the workflow instead of the demo.
Initial systems consultation
Bring the operating problem. We’ll map the build or ownership model it needs.
- You leave with
- A focused consultation with a senior systems consultant
- A current-state fit and priority read
- A recommended path: implementation, ongoing ownership, or product