Fortune 500 companies weren't failing at AI because of ideas or budget. They were failing at execution. 15 enterprise deployments, 5 sectors, $30M+ TCV, 96% expansion rate — in 6 months.
Company:
Unframe AI
My Role:
AI Product Manager, Enterprise Solutions
Year:
2026
Techstack
MCP Architecture · Multi-Agent Orchestration · Agent Orchestrator · Knowledge Fabric · Data Connectivity · Building Blocks · RAG Pipelines · IoT Stream Processing · Legacy ERP/CRM Integration
Fortune 500 companies weren't failing at AI because of ideas or budget. They were failing at execution. Pilots stalled in procurement for months. POCs never touched production systems. Every enterprise had a different tech stack, a different CIO, a different security posture, and a different definition of "done" — which meant every engagement risked becoming a one-off custom build with no leverage for the next one.
My job was to be the person in the room who made deployment real — not in six months, but in days — and to make sure each deployment made the next one faster, not just the same.
STEP 1 — Embedded Discovery, Every Time
Every engagement started with a direct conversation with the CIO or VP about what was actually broken — not a requirements doc. The output was a single use case tied to a measurable outcome. "If it doesn't move a number, we don't build it." Scope, assemble from reusable building blocks, deploy — then expand once KPIs moved. 96% of clients expanded to additional use cases.
STEP 2 — Architecture on Legacy Infrastructure
Real enterprises don't have clean data or modern stacks. Across this portfolio: 10 disconnected monitoring tools, core banking ledgers from the 1990s, 7 sites with 3 different PLC vendors and 2 SCADA historians, and 4 inbound document channels feeding 3 downstream systems with no shared schema. Every agent operated on top of existing infrastructure — read-only where required, normalized at the integration layer, never requiring a rip-and-replace.
STEP 3 — Delivery and Live Iteration
Deployments went live in 3–15 days. The work wasn't done at launch — it was done when KPIs were moving and the client's own team could run it independently. By the fifth use case at a given client, deployment time dropped to hours, because the Knowledge Fabric and connector libraries built for use case 1 carried forward to every subsequent use case.
STEP 4 — Internal Lifecycle Automation
As onboarding scaled, I audited historical client architectures and found the same data-ingestion and routing patterns being hand-rebuilt for every new client. I isolated those repeatable blocks and worked with engineering to generalize them as platform-level reusable modules — turning a per-client rebuild into a configuration step. This compounding effect is why deployment times dropped from weeks to days across the portfolio.
RESULTS
15 enterprise AI applications shipped across 5 sectors
$30M+ portfolio TCV in 6 months
96% client expansion rate to additional use cases
3–15 days to first deployment per client
Hours to subsequent deployments by the 5th use case at a given client
