GPN vs In-House AI Team
Get to value in days without building and maintaining a full AI stack.
Summary
In-house AI teams offer deep customization but require long build cycles. GPN delivers a ready-to-run agentic GTM workflow with governance and model optionality built in.
- Leaders who need results quickly
- Teams without dedicated AI engineering bandwidth
- Organizations seeking built-in governance and auditability
- Highly bespoke workflows with unique data pipelines
- Organizations investing heavily in AI infrastructure
- Teams that need full control of every component
Key Differences
Time to value
Days to launch workflows
Months to build and iterate
Maintenance
Managed platform updates
Ongoing engineering investment
Model optionality
Built-in, provider-agnostic
Requires custom orchestration
Governance
Policy-based approvals and logging
Must be built from scratch
Cost predictability
Subscription with known scope
Variable build and ops costs
Scalability
Designed for multi-market GTM
Depends on internal resourcing
Why Teams Choose GPN
Strengths
- Fast deployment with proven GTM frameworks
- Lower operational burden for model changes
- Governance and auditability out of the box
Tradeoffs to Consider
- In-house teams can offer deeper bespoke customization
- Some integrations may require collaboration with your engineering team
Frequently Asked Questions
Can we still use our data science team?
Yes. Many customers keep their teams focused on proprietary models or analytics while GPN runs GTM workflows.
How customizable is GPN?
GPN supports configurable workflows, model routing, and scoring logic without requiring you to build core infrastructure.
What if we want full control later?
GPN does not prevent you from building in-house. It can serve as a fast starting point or a long-term platform.
Want a Custom Comparison?
We can map your current workflow and show where GPN fits best.
Talk to the Team