AI agents · automation · run inside your own accounts
We design, build, and run AI agents and automations inside your own accounts: lead follow-up, client ops, reporting, content. Fixed quote before we build. Cost per run on the table before go-live.
§ 02 · What we build
OPS
Agents wired into your CRM, inbox, and calendar that handle the repetitive decisions: triage, lookups, replies, follow-ups. A human steps in where it matters.
Automate this for me →FLOW
The glue between your apps. Your CRM, sheets, inbox, and AI connected so handoffs happen without anyone copy-pasting between tabs.
Automate this for me →GROWTH
The content and marketing engine this very site runs on: AI drafts, human review, scheduled publishing, live tracking. We rebuild it for your business.
Automate this for me →01 · Free
We look at how the work flows today and find the one thing worth automating first.
20 minutes · no deck
02 · Fixed quote
We build it on your real data and show you the cost per run before it goes live.
cost per run shown first · about a week to the first live build
03 · Yours
It runs in your own accounts, documented in plain English, so you own it. We stay for support if you want.
your accounts · plain-English docs
§ 04 · Before / After
Running it by hand
Running on AI agents
§ 05 · Why Cart Gaze
Every system we sell runs on our own business before it runs on yours. This site is the demo.
You get a fixed quote upfront and see the cost per run before anything goes live.
Everything runs in your accounts on open, standard tools, fully documented. No lock-in.
Agents handle the repetition; approval steps keep a person on anything customer-facing or sensitive.
We track model releases and price changes daily, so your build uses what is best now.
You get documentation your team can actually read, plus support after launch if you want it.
§ 06 · Work with us
The guides, the daily briefing, and the live radar are produced by AI agents and automations we built and run for a few dollars a month. Cart Gaze builds the same kind of system for small teams and solo operators.
Sources → AI draft → Human review → Publish → Live radar
AI agent & automation expert · PMP-certified PM
Qasim builds product for the love of it. When an idea lands, he skips the deck: a working prototype is usually running within hours, built with the same AI agents and automations he sells. He puts his own output at roughly 20× what it was before agents. One person shipping like a small team.
A PMP-certified project manager and the founder of Cart Gaze LLC, he spent years shipping enterprise telecom systems before pointing that delivery discipline at the repetitive work drowning small teams. The long game is an Agentic OS: agents, automations, and shared memory that quietly run a business. This site is the working prototype. Its drafting pipeline, daily tool radar, and publishing engine are the exact systems he installs for clients.
The blog is our lab notebook: the agents and automations we ran on our own business, with the real costs and what broke along the way. If we recommend it, you can read the test.
A search tool turns post-cutoff questions from hallucination bait into retrievable facts, and it brings 3 problems with it: choosing sources, paying for pages that become tokens, and reading text that may be aimed at the model rather than the user. This is the wiring guide: the
Most tutorials get you a working agent in ten minutes and skip what breaks it in week two. Sixteen lessons covering the loop, tool descriptions, memory, fallbacks, guardrails, evaluation, and the cost levers that decide your bill.
Same model, same tools, same prompt, 2 different machines: one answers when you type, the other triages your inbox at 6am and escalates what it couldn't handle. The difference is supervision, and removing it changes everything: latency stops mattering, durable state starts, and
Every prompt containing personal data becomes 5 copies of that data: provider logs, your traces, caches, eval exports, bug reports. This is the redaction architecture that actually holds: a single boundary function, layered detectors, consistent pseudonyms like [PERSON_1] that
A 500-token answer takes 8 seconds either way; streaming decides whether the user reads for 8 seconds or stares at a spinner for 8 seconds. This is the mechanism: time to first token and cadence as the 2 numbers that define perceived speed, the buffering layers that silently
A RAG bot is only as current as its last ingest, and re-embedding the whole corpus nightly pays for thousands of unchanged documents to pick up a handful of edits. This is the incremental alternative: hash to detect changes, upsert by stable chunk IDs, tombstone deletes, and
§ 09 · How we publish
AI assists the drafts; a named human tests, edits, and fact-checks every piece. We disclose the tools, versions, sample size, and dates behind each claim.
