UNKAPPED
Founding forward-deployed engineer at an applied AI and GTM engineering firm: inside client teams in cybersecurity, DevTools, cloud and AI-native software, taking what they bought into production.

Forward-deployed engineer
That’s the gap I work in. I sit inside a client’s team, in the systems they already pay for, and turn the tools they bought into systems that run in production. Then I stay until they run without me.
Running now
Every recorded outside call becomes one record tied to the right client, with a summary a person reads first. Nothing reaches a client or the CRM without a gate.
Where I work
And one product I am building alongside the client work.
Founding forward-deployed engineer at an applied AI and GTM engineering firm: inside client teams in cybersecurity, DevTools, cloud and AI-native software, taking what they bought into production.
Applied AI GTM for AI startups from Seed to Series B, with fixed prices: Teardown $1.5K, Sprint $15K, Lab from $7.5K a month.
My notebook on Substack: what breaks when AI goes to production, one outcome at a time.
The product I am building alongside the client work.
Systems in production
A workflow needs someone to watch it. A system has a schedule, retries, an alert when it goes quiet, and tests that fail loudly.
Inbound qualification from 4 hours to under 3 minutes, with a branded brief on the AE’s desk before the first call. 8 n8n workflows, a 5-provider enrichment waterfall and a B2B gate; 698 unit and 143 end-to-end tests.
31 recordings became 15 meetings, each tagged, matched to the client and summarised for a person to review in one click. Account memory, call scores and search sit on top. 22 workflows, 15 live, 1,159 tests.
Claude Haiku classifies every LinkedIn and email reply and drafts an answer. A person decides, and nothing sends on its own. Multi-tenant Supabase with row-level security, 183 commits in 16 days.
Role map, research, signal, gap, angle and a draft under 90 words, then a second model checks facts and spam risk. 1.5%+ positive replies across 150+ domains.
500+ pages, one per account, built on whether AI assistants named that company in its own category. Every visit tracked to the page view.
Anonymous traffic resolved to a person and an account, enriched, scored and in a sequence the same day.
Lead qualification from 48 hours to 9.6, with written reasons pushed into the CRM. Part of $180K ARR growth in six months.
Scores people across several intent sources, rejects anything it cannot confirm and records why: 102 of 1,562 got through. Rejecting is the feature.
942 contacts enriched, 584 verified emails and 242 of 275 buyer emails passing the copy check, synced back to HubSpot with a reason.
How I work
Aim first: TAM, ICP, personas, the signals worth chasing. Then build what executes it.
If an experiment takes a week, rebuild the process until it takes days.
Repeated GTM work becomes a system that keeps running.
Seven tools and two approvals to ship one campaign? Remove the handoffs.
Find the bottleneck, test one idea, ship, measure, repeat.
Rules I keep arriving at
When the data is uncertain, skip the person and record why. Most tooling fails open and quietly contacts the wrong people.
Sending more is not the answer. Two intent sources agreeing beats one source shouting.
A quiet morning looks like success. Alert on zero, not only on errors.
If the handover is a recording and a spreadsheet, the buyer rented a workflow.
The record
Every number below comes from the role it sits under.
Writing
A green tick, a workflow that finished, and the part I cared about never ran. Why I now check which steps actually ran, not the tick.
Shorter notes from the same work: forward-deployed engineering, applied AI and GTM engineering.
Kit
If your stack already has some of these, the build starts from what you have.
Next step
Tell me where it stalled. That’s the conversation I want to have.
Or ask an assistant about me: