Builds.

Internal build · 01 / 06

Shipping document automation

Export shipments arrive as supplier PDFs, native or scanned. Somebody retypes weights, pallet counts, line items, and party addresses into a filing, a carrier email, and a spreadsheet, every shipment, with the transcription errors that implies.

• A PDF extraction pipeline with three fallbacks, from native text to OCR for scanned documents, run off the main event loop.
• Automatic customer creation with fuzzy matching, so the receiving party is matched on upload instead of picked from a dropdown.
• Document generation with auto-fitting text, so data never bleeds across the boxes of a fixed government form, plus templated email bodies and a spreadsheet export driven by a mapping file.
• A watch folder with per-customer routing: drop PDFs in, shipments appear on the dashboard at pending status with a needs-attention table.

Pack list → filing

Internal build · 02 / 06

Call system with an auto-logged CRM

Call activity dies on a notepad. Nobody knows what was said, who answered, or which approach got a reply, so nothing can be improved on evidence.

• Two-leg dialing: the system rings the operator’s cell showing the business number, bridges to the other party on answer, records both legs, and pulls status and duration on hangup.
• Local transcription of every recording, so audio never leaves the machine, with an inbox mode that transcribes and files inbound voicemail the same way.
• A five-question debrief after each call that writes one structured row: outcome, role reached, sentiment, loss reason, and the verbatim reply.
• An inbound flow that rings the cell, takes a recorded voicemail, and notifies, plus a form receiver that files the submission and sends the sender an instant plain-text reply.

Call → logged row

Internal build · 03 / 06

Website technology fingerprinting

Working out what a business’s website is built on, and what software it already pays for, is slow manual research that nobody can reproduce or check.

• Platform and vendor detection across the common site builders, storefronts, and agency-built stacks.
• Detection of paid tooling already in place: chat widgets, booking, call tracking, ad pixels, and email tools.
• Extraction of published contact details and the structured data a site publishes about itself, plus staleness tells such as an old copyright year.
• Batch mode from a URL file with CSV and JSON output, where every fact is tagged with the page it was read on, nothing behind a login and no volume scraping.

URL → sourced record

Internal build · 04 / 06

Short-form content system with retention analytics

Short-form video is normally judged on taste. Without instrumentation nobody can say which render decision caused a result, so every week starts from opinion.

• A render engine with per-format scripts: crop and zoom, caption burn-in, loudness normalization to a target, face tracking, and cover generation.
• Automatic feature extraction at render time, roughly twenty-four attributes per clip, written to a sidecar file and the database with no manual entry.
• A quality gate before anything ships: a contact-sheet montage, a cuts audit, and a loudness check.
• An analytics reader that pulls the platform’s own per-video insights, including the full per-second retention curve, into a time-series table, with an experiments module that reports lift on an explicit evidence ladder.

Retention curve

Internal build · 05 / 06

Marketplace data aggregation and decision scoring

Deciding what a listing is worth means watching six sources continuously and knowing what comparable items actually sell for. That is a full-time job nobody does well by hand.

• A staged pipeline: discover, dedupe, tag, signal, value, calibrate, score, alert, and persist, with retries and structured logging at every stage.
• Six source scrapers feeding a fast keyword tagger for category, brand, condition, and age.
• A multi-source comparable valuation layer with an eight-signal weighted conviction score, a net-margin calculation, and a calibration pass.
• Alerting to chat and SMS, a dashboard API, a React front end, and a fifteen-table async database with a per-module test suite.

Listing → conviction score

Internal build · 06 / 06

AI video production pipeline with a cost-and-acceptance ledger

Generated video quality is unpredictable and the spend is invisible. Without a ledger nobody can answer what an accepted clip actually costs, or which approach produces them.

• A command-line pipeline submitting text-to-video and image-to-video jobs with resolution, duration, and seed control, and multi-attempt batching.
• Per-model and per-resolution cost estimation applied to every attempt at submission time.
• A generation ledger recording model, provider, mode, subject type, resolution, seconds, cost, seed, prompt, and a keep or kill decision with its reason.
• A report command computing keep-rate and cost per accepted clip by model and by subject type, with CSV export.

Ledger → keep rate