I build the operating systems that turn ambiguous AI markets into prioritized accounts, cleaner GTM processes, sharper deal narratives, and measurable pipeline. This page is tailored for LangChain's Deal Strategy & Operations Manager role.
LangChain sits at the center of the AI agent stack: orchestration, observability, deployment, evaluation, and the workflows that turn LLM experiments into production systems. I have been living in exactly that world through OpenClaw, Hermes Agent, Claude, Codex, Qwen, Llama, Cloudflare, and practical business-development automation.
For Deal Strategy & Operations, I would bring a hybrid profile: investment-banking commercial discipline, hands-on agent workflow building, CRM/data operating rigor, and the ability to translate messy market signals into clearer GTM decisions.
My edge is not just knowing AI language. It is building real workflows, tracking real accounts, and turning rough strategic questions into structured outputs that sales, partnerships, product, and leadership can act on.
I automated my end-to-end business-development workflow: sector intake, company sourcing, ownership screening, PELS scoring, contact enrichment, email verification, QA validation, and Excel delivery.
The input can be a rough sector description or company URL. The output is a scored and prioritized target list ready for outreach, with the same QA steps applied every time.
I built PELS, a 5-dimension, 0–100 scoring model to predict exit likelihood and prioritize business-development targets using public and enriched data. The methodology turns ambiguous market signals into ranked accounts and repeatable outreach logic.
For LangChain, the same operating instinct applies: make deal quality, account prioritization, pipeline inspection, pricing/deal process, and sales execution more legible and repeatable.
Built multi-step AI workflows that perform sourcing, enrichment, scoring, drafting, QA, and handoff. This mirrors the exact enterprise challenge LangChain helps customers solve: make agent workflows reliable enough to use in production.
Created structured target lists, buyer universes, CRM objects, and account-priority logic so BD efforts become inspectable rather than ad hoc.
Turned market research into sector whitepapers, pitch materials, company briefs, outreach, buyer maps, and partner/account-specific narratives.
Built and deployed projects with OpenClaw, Hermes Agent, Claude, Codex, Cloudflare, and local models without waiting for perfect specs.
Can explain why agent orchestration, observability, evaluation, and workflow automation matter in business terms: risk reduction, speed, pipeline, and repeatability.
Understands that GTM performance depends on clean account data, consistent process, useful fields, and feedback loops — not just more outbound volume.