Systems Studio

Quiet infrastructure that makes a business actually run.

Klarsparrow designs and builds the systems most teams never get around to — the outreach engines, data pipelines, and internal tools that turn manual, expensive, error-prone work into something that runs reliably on its own.

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Selected Work

Two systems, built and running in production.

Not demos. Both of these were built end-to-end, deployed, and operated — the kind of infrastructure that keeps working after the engagement ends.

01 / Outreach

AI-personalized outbound system

Cold B2B outreach has three failure modes that quietly kill a small team's pipeline: generic mail-merge gets no replies, sending too fast burns your domain, and over-contacting turns warm leads cold.

So I built the engine instead. It drafts personalized, per-contact email in a strict brand voice, sends on a schedule that protects domain reputation, never contacts the same firm twice inside a cooldown window, and gets sharper over time by feeding its own click-and-bounce results back into the drafting prompt.

The clearest result is a deliverability turnaround — verification gating took bounce rate from reputation-risk territory to near-zero, even as send volume scaled.

18% → <1%
Bounce rate after verification gating
~3,780
Personalized emails delivered
2,300+
Delivered in a single month
Self-paced
Always-on worker, no babysitting
Next.js Postgres / Supabase Claude API Resend Fly.io worker

Today

TUE · JUN 17
Coach — Deliverability is holding at 0.7% bounce across verified sends. Two replies came in overnight; Cedar & Pine Mercantile opened your last note three times. Lead with them today.
0.7%
Bounce rate
verified
312
Sent · 30d
on pace
41%
Open rate
+6 pts
9
Replies · 7d
in pipeline
Today's moves3 ranked
1Cedar & Pine Mercantile— follow up, opened 3×DRAFT READY
2Harbor Goods Co.— reply received2H AGO
3Junebug Home— cooldown clears todayQUEUE
Sample data — fictional store names, figures match the real story.
02 / Data Engineering

Data pipeline for scattered, undocumented sources

The data was real and public — but it lived across many inconsistent web sources, none with a clean export, no two structured the same way. By-hand assembly was slow, error-prone, and stale the moment it was done.

I built a pipeline that does the assembly automatically: headless-browser recon discovers each source's true data shape, a resilient harvester handles the awkward parts, and a resumable graph worker expands outward along scored relationships — with confidence decaying across hops so the dataset stays anchored to high-quality signal.

Entity resolution collapses near-duplicates into one canonical row per real entity. Runs are crash-safe and cost-bounded, and a re-run refreshes the data along the same relationships instead of rebuilding from scratch.

1 dataset
From several thousand raw records
Resumable
Crash-safe durable work queue
Cost-bounded
Explicit budget ceiling per run
Refreshable
Re-run updates, never rebuilds
Python Puppeteer recon Postgres work queue Graph traversal Entity resolution
Scattered no export · inconsistent SOURCES, NEVER MEANT TO BE READ PROGRAMMATICALLY Recon + harvest headless browser → shape retries · backoff · token refresh Graph traversal seeds → expand along edges scored edges · confidence decay drift rules · resumable queue Entity resolution canonical key · collapse dupes coalesce first-non-empty Clean dataset one row = one real entity · CSV RE-RUN REFRESHES ALONG THE SAME RELATIONSHIPS expanding, not snapshot
Pipeline architecture — re-run refreshes the dataset, never rebuilds it.

What I Build

Messy, manual, or expensive — turned into a system that runs itself.

The case studies above are two examples. The underlying work is the same across all of it: taking an operational problem and turning it into infrastructure that runs reliably on its own.

01

AI-assisted outreach

Personalized, per-contact email at volume — with the guardrails that keep it from backfiring: address verification, send pacing, suppression rules, and reputation protection.

02

Data pipelines

Turning scattered, inconsistent, no-export web sources into clean, structured, deduplicated datasets — assembled and refreshed automatically instead of by hand.

03

Third-party API integration

Wiring commerce and marketing platforms into your own systems — handling the authentication, pagination, and data-shape mismatches that make these integrations brittle.

04

AI content automation

Production workflows that generate on-brand product imagery and marketing content programmatically, with cost controls — replacing repetitive manual production.

05

Internal operations tools

Custom dashboards that pull live data from the platforms you already use and turn it into the one view your team actually needs — order tracking, margin, pipeline status.

06

Systems that outlast me

Deployed, documented, and handed over running. The goal is infrastructure your team owns and trusts — not a dependency on the person who built it.

How I work

Small, fast, end-to-end. I scope the system, build it, deploy it, and hand it over running — usually solo, usually in days or weeks rather than months. If you have an existing tool that almost works, I can extend it; if you need something from scratch, I build it.


Contact

Have a system worth building?

Tell me the manual, expensive, or brittle part of how your business runs. If it can be turned into quiet infrastructure, I'll tell you how.

hello@klarsparrow.com