SRSHAW RANA

RA 04h 51m · DEC +45° 30′ · SYS-00

SHAWRANA

I build the systems that run businesses. I used to need a dev team for that. Now I run a fleet of AI agents.

  • Montréal
  • 15 years of operations systems
  • Agent fleet online
01 / The chartRA 06h 12m · SEC-01

Every star is a system I built.

Grouped by what they do. Lines connect systems that share a company, a carrier or a lesson. Select a star to read its card.

18 SYSTEMS · 6 CLUSTERS · TAP A STAR

CL-A · BUSINESS SYSTEMS (PRE-AI)CL-B · AI IN PRODUCTIONCL-C · VOICE + OUTBOUND AICL-D · INTELLIGENCE PIPELINESCL-E · AGENT INFRASTRUCTURECL-F · OFF-DUTY BUILDSSR-001Design&Rank operations systemSR-002Prime CS dialer platformSR-003Augmented underwritingSR-004Saivpoint save deskSR-005Company rolodexSR-006AI call deskSR-007Collections outreachSR-008Vinsight deal pipelineSR-009Marketing feedback systemSR-010Evergreen wikiSR-011Geo-leak lead finderSR-012The agent fleetSR-013Telegram bridgeSR-014Seeded-defect QASR-015Lego systemSR-016Jeep WK2 parts huntSR-017Home network controlSR-018Library-hold sweeper
  • Design&Rank operations system

    SR-001 · 2015 · CL-A · BUSINESS SYSTEMS (PRE-AI)

    Design&Rank started on paper. Leads on paper, closers dialing from paper, customer service logging jobs on paper for the dev team.

    I picked a modular base and rebuilt it with my dev team around the whole company: SDRs, closers, customer service, retention, fulfilment and monthly billing in one pipeline.

    New hires kept telling us it was the easiest system they had ever learned.

    40 · people on one pipeline

  • Prime CS dialer platform

    SR-002 · ~10 yrs running · CL-A · BUSINESS SYSTEMS (PRE-AI)

    Prime CS sets appointments for car dealerships and gets paid per lead, so every extra minute per lead eats margin. Before the build it ran on spreadsheets and manual dialing.

    I designed a dialer pipeline with voicemail detection, so agents only ever speak to a live person, with the right record already on screen.

    It handles every client's intake and delivery format, from email to SFTP, and gives supervisors dashboards to run each campaign to goal.

    ~10 yrs · in production

  • Augmented underwriting

    SR-003 · AI era · CL-B · AI IN PRODUCTION

    Built solo for a micro-lending company. Human underwriters stay in charge; AI adds decision signals to every application.

    The data is processed on the company's own box with local models, so applicant files never leave it.

    The result was a faster approve or decline cycle and a lower default rate.

  • Saivpoint save desk

    SR-004 · 2026 · CL-B · AI IN PRODUCTION

    An AI desk that answers refund, cancel and billing messages for a digital-offer subscription brand I run.

    It resolves what it can, saves what it should, and routes edge cases to a human.

    83% · resolved by the AI alone

  • Company rolodex

    SR-005 · 2026 · CL-B · AI IN PRODUCTION

    One registry and app for every company I own or manage: directors, fiscal year-ends, deadlines, incoming letters and a ledger.

    An agent works from it with fixed rules. Secrets stay in a password store; the database only keeps pointers.

    12+ · companies run from one app

  • AI call desk

    SR-006 · 2026 · CL-C · VOICE + OUTBOUND AI

    It places real phone calls for me: parts desks, suppliers, quotes. One brief in, one call out, then a transcript and a structured result on my phone.

    It runs a speech-to-speech model over a Canadian carrier, handles French, answers callbacks, and hangs up on voicemail.

    Every call needs my go first.

    $10/day · hard spend cap

  • Collections outreach

    SR-007 · AI era · CL-C · VOICE + OUTBOUND AI

    A context-driven SMS and email agent for the micro-lending company that reaches defaulted clients and sets up payment plans.

    A compliance check runs against every outbound message before it is sent.

  • Vinsight deal pipeline

    SR-008 · AI era · CL-D · INTELLIGENCE PIPELINES

    Pulls car sales data and trends. Every new opportunity passes a predictive gate, then a deterministic gate, then an LLM that reads the context and decides.

    The team stopped scouting and started closing what the pipeline fed them.

    3 · gates before a human sees a deal

  • Marketing feedback system

    SR-009 · AI era · CL-D · INTELLIGENCE PIPELINES

    Reporting across Google Ads and Meta for the micro-lending company, fed back into creative and ad strategy.

    An LLM compliance layer reviews all marketing output before it runs.

  • Evergreen wiki

    SR-010 · 2026 · CL-D · INTELLIGENCE PIPELINES

    Every agent session I run gets mined each night. Learnings land in a wiki with backlinks, a health check, and an index the next session reads first.

    It is how a lesson from one project reaches the others without me repeating it.

    100+ · pages, grown from sessions

  • Geo-leak lead finder

    SR-011 · 2026 · CL-D · INTELLIGENCE PIPELINES

    Finds businesses whose Meta ads spill across a border they do not serve. That leak is a warm lead.

    A pre-triage classifier cuts paid lookups before anything is scored.

    230 · qualified from ~12,800 ads

  • The agent fleet

    SR-012 · 2026 · CL-E · AGENT INFRASTRUCTURE

    A strong model drives: it plans, judges and writes the final word. Cheap, uncapped lanes do the typing and the browsing. A read-only scout does volume retrieval. A reviewer with fresh context checks the work.

    I picked the execution model with a bake-off: same harness, same 34-check verifier, only the model changed. DeepSeek Flash scored 34 of 34 in 69 seconds; GPT-5.6 Luna scored 34 of 34 in 189.

    2.7x · faster at the same score

  • Telegram bridge

    SR-013 · 2026 · CL-E · AGENT INFRASTRUCTURE

    Full Claude Code from my phone, built on an open-source bridge and extended: one private group per project, voice notes transcribed, a scheduler for recurring jobs.

    Anything longer than a couple of minutes goes to a background lane, so the chat stays free while the job runs.

    Approvals only apply to the message I reply to.

    8 h · background job window

  • Seeded-defect QA

    SR-014 · 2026 · CL-E · AGENT INFRASTRUCTURE

    A builder agent does not get to grade its own gates. A 13-agent workflow built each quality gate, then attacked it with a planted defect it should catch.

    A gate that misses its own seeded defect is fixed or marked not trusted.

    3 · false negatives caught

  • Lego system

    SR-015 · 2026 · CL-F · OFF-DUTY BUILDS

    Sort by shape, never by colour: eight labelled bins. Next comes an inventory of every part, then custom instruction booklets designed only from parts already owned.

    Designing around the inventory is what saves the money.

    8 · shape bins

  • Jeep WK2 parts hunt

    SR-016 · 2026 · CL-F · OFF-DUTY BUILDS

    A scheduled agent hunts used body panels for a 2014 Grand Cherokee: checks fitment, emails yards, logs quotes and replies.

    It negotiates. Nothing gets bought without my confirm.

    15 · yards quoted in one sweep

  • Home network control

    SR-017 · 2026 · CL-F · OFF-DUTY BUILDS

    Plain-English control of the home DNS filter from a chat channel: see what a device is streaming, block a site for 15 minutes, pause a tablet.

    Built as a small command-line tool over the filter's API, with timed unblocks scheduled by the agent.

  • Library-hold sweeper

    SR-018 · 2026 · CL-F · OFF-DUTY BUILDS

    Three small command-line tools: check my holds, sweep what is on the shelf at my branch right now, and place a hold.

    The catalogue API signs every request, so the sweeper implements the signature instead of driving a browser.

02 / The fleetRA 09h 40m · SEC-02

How the work gets done.

A strong model plans and judges. Cheap lanes execute. Scouts retrieve. A reviewer with fresh context checks the result. Counts below are from the last 7 days of real sessions.

ORBIT 1 · REVIEWORBIT 2 · RETRIEVALORBIT 3 · EXECUTIONREVIEWERFRESH CONTEXTSCOUTREAD-ONLYSCOUTDIGESTSDEEPSEEK FLASHIMPLEMENTDEEPSEEK FLASHBROWSEGPT-5.6 LUNASECOND VENDORGROK 4.6SECOND VENDORWORKERESCALATIONDRIVERSTRONG MODEL · PLANS · JUDGES

LIVE COUNTS · LAST 7 DAYS

UPDATED 2026-09-26

712

SESSIONS

254

DISPATCHES

183

EXECUTION

63

SCOUT

8

REVIEW

  • Driver

    Plans, judges, writes the final word

    Strong model

  • Execution lanes

    Typing and browsing from a written spec

    Cheap, uncapped

  • Scouts

    Volume retrieval, returns a digest

    Mid-tier, read-only

  • Reviewer

    Fresh-context check before anything ships

    Strong model

SELECTION

The execution model was picked by bake-off: same harness, same 34-check verifier. DeepSeek Flash 34/34 in 69 s. GPT-5.6 Luna 34/34 in 189 s.

04 / DispatchRA 15h 02m · SEC-04

Run the fleet for 60 seconds.

Tasks arrive. Route each one to the cheap lane, the strong model, or yourself. You are scored on value shipped per dollar.

  • 1 · CHEAP LANE · $0.01
  • 2 · STRONG MODEL · $0.50
  • 3 · YOU · MAX 3
Play Dispatch →
Shaw Rana

SR-000 · OBSERVER

05 / ContactRA 23h 59m · SEC-05

Building something that has to work on Monday?

I read every message. X is fastest.