We don't need headcount.
We move to the obelisk.
With AI handling volume work, the model where juniors execute and seniors review no longer holds. Human focus belongs on judgment, design, and quality assurance. ENGORGIO operates as an obelisk — connecting judgment, design, implementation, and operations directly.
The Obelisk
ENGORGIO's team structure isn't a pyramid — it's an obelisk. Narrow and tall.
Three layers — top (judgment and accountability), core (design and implementation), edge (specialist connections) — put zero distance between decision and execution.
We don't solve problems with headcount. We prevent context from degrading through structure. The structure enterprises will need.
No handoffs between phases
The people who proposed it build it. The people who built it operate it.
This isn't idealism — it's a design principle for preserving judgment quality.
What we defined as the problem, what we chose to sacrifice — that tacit knowledge doesn't survive a handoff document.
Working AI-native
In a world where AI handles volume work, human attention belongs on judgment, design, and quality assurance.
ENGORGIO's team is built on that premise. A small team with long-term commitment is what makes "20% development, 80% operations" possible.
The era when team size was a proxy for value is over.
Partner ecosystem
The obelisk's "edge" — specialist connections — is how we access expertise beyond our core.
Partnerships with Databricks, Snowflake, and other technology leaders structurally secure the expertise we need.
Specialization doesn't have to be internal. What matters is that connections are designed, not accidental.
The 20/80 commitment
Our team is designed for long-term commitment, not project-based rotation.
When the people who designed and built a system also operate it, operations are fundamentally different. They understand the trade-offs, what was sacrificed, and why.
20% development, 80% operations isn't a metric — it's a commitment to staying with what we build, long after the initial excitement fades.
How the delivery team is organized
Multi-project oversight, senior stakeholder relationships, POC-to-scale delivery
LLM/Agent development, client-embedded, technical obstacle resolution
UX/UI, agent personality/behavior, brand integration, prototype-to-launch
Production reliability, SLOs, observability, incident response, AI infra optimization
Business requirements, domain knowledge, user feedback, acceptance criteria
Design, build, operate — same team from Discover to Deploy
Governed Data+AI Architecture
Continuous architecture evolution with both decentralization and central governance. A foundation platform integrating Data Mesh Governance, RBAC/ABAC, Data Catalog, Lineage, and Data Quality.
Organization-wide AI-native transformation, workflow redesign, coaching (“should use AI” → “AI is default”)
Independent layer — primarily active during Scale phase
Service lines & team mapping
| Service Line | Duration | Primary Roles | Client Touchpoint |
|---|---|---|---|
| AI Strategy | 2 weeks+ | Delivery Lead + Software Engineer + Experience Designer | CxO / Board |
| AI Agents | 12 weeks+ to production | Software Engineer + Experience Designer + SRE + Delivery Lead | Tech team + Business leaders |
| Data+AI Platform | Ongoing | AI Applied SRE + Software Engineer + Delivery Lead | Platform / Infra team |
| AI at Scale | Ongoing | Delivery Lead + SRE + Adoption Specialist | CxO to frontline |
| Adoption | Ongoing | Adoption Specialist | All departments |
Share your intent for the future.
The first conversation: find the starting point of transformation together. What you want to change. What hasn't changed yet. Start there.