What types of processes can you automate?

We specialize in automating repetitive workflows across operations, marketing, sales, and customer support using AI and custom logic. We've worked with government agencies (Saudi Arabia, Morocco) and private enterprises across 15+ industries to automate complex processes with enterprise-grade security.

Do I need technical knowledge to use your service?

No technical knowledge required! Our platform is designed with a user-friendly interface that allows anyone to set up and manage automated workflows with ease. We provide comprehensive training and ongoing support.

Can you integrate with our existing tools?

Yes, we offer seamless integrations with over 100+ popular tools including CRM systems (Salesforce, HubSpot), email platforms (Mailchimp, SendGrid), and project management tools (Asana, Trello).

How long does implementation take?

Most implementations take 1-2 weeks. Simple automations can be deployed in 3-5 days, while complex enterprise solutions typically take 2-4 weeks due to security compliance and approval processes.

Can you self-host n8n or AI models for us?

Yes! We offer self-hosted solutions for complete data control and security. We can deploy n8n, custom automation platforms, and open-source AI models (Llama, Mistral) entirely on your infrastructure (on-premises or your cloud).

Is your AI secure and compliant?

Absolutely. We follow industry-leading security standards including SOC 2 compliance, end-to-end encryption, and regular security audits. We've successfully delivered solutions for Saudi and Moroccan government agencies.

All Days

Day 7

Founder OS — NotebookLM + Claude Code

claude code series — day 07

you've been paying Claude
for research it shouldn't be doing

there is a free tool that handles all of it.
NotebookLM — powered by Google Gemini — processes your docs, APIs,
YouTube tutorials, entire codebases.
Claude just builds from the output.

notebooklm — freeinfinite memory for Claudezero token wasteno hallucinations on outdated APIsno bloated CLAUDE.md

scroll to explore

the workflow

research and build — separated

two tools, two jobs, zero overlap. this is what eliminates hallucinations.

01 — research

drop your sources into NotebookLM

PDFs, API docs, YouTube tutorials, entire codebases. NotebookLM processes everything using Gemini and generates a structured implementation plan.

free — Google pays the tokens

→

02 — handoff

pass the grounded plan to Claude Code

NotebookLM outputs a structured, source-anchored implementation plan. Claude doesn't research. it receives the plan and executes from it.

the key handoff moment

→

03 — build

Claude Code just builds

no hallucinations on outdated APIs. no token burn on context gathering. no vague outputs from stale training data. pure execution from today's source.

token-efficient output

—

most people stuff their CLAUDE.md with docs and API references trying to give Claude "context."
that is the wrong job for CLAUDE.md.

NotebookLM handles all that for free. CLAUDE.md stays focused on rules and behavior only.

setup — pick one, get infinite memory

two ways to use it — you choose

either path gives Claude access to everything you've ever fed into NotebookLM. permanent. searchable. available in every future session — no re-explaining, no re-uploading.

github.com/jacob-bd/notebooklm-mcp-cli

install

$uv tool install notebooklm-mcp-cli

orpip install notebooklm-mcp-cli | pipx install notebooklm-mcp-cli

$nlm login

what you can do

$nlm research start "shadcn v4 components"

$nlm notebook create "My Project"

$nlm source add MyProject --url "https://ui.shadcn.com/docs"

$nlm notebook query

$nlm studio create MyProject

$nlm share public MyProject

drop any URL, PDF, YouTube video, Google Drive file, or codebase as a source. Claude gets infinite, permanent memory from everything you feed it. never re-explains the same thing twice.

github.com/PleasePrompto/notebooklm-mcp

install — one command

$claude mcp add notebooklm npx notebooklm-mcp@latest

Chrome opens → sign in with Google → restart claude code → /mcp to verify

inside claude code — just talk to it

"Add this URL to my library"

"How does the new Sheet component work?"

"Search my notebooks for authentication patterns"

"What did we decide about the database schema last week?"

Claude queries NotebookLM autonomously as a tool — no switching windows, no copy-paste. everything you've ever fed it is permanently available. your entire knowledge base, inside every session.

what you can do with it

3 workflows that actually work

not theoretical. these are the exact use cases that convinced me to switch.

before

building with shadcn docs

Claude Code pulls from its training data — which was last updated months ago. it uses deprecated component APIs. you get build errors. you spend an hour debugging packages that no longer exist.

with notebooklm

drop the latest shadcn docs into NotebookLM

NotebookLM extracts the exact new components, props, and patterns from the current docs. Claude Code builds your dashboard using only those components.

first shot. no outdated package errors. no debugging session.

before

watching a YouTube tutorial

you watch an hour-long tutorial on Python async patterns. you take notes. you copy some code. Claude tries to replicate it from stale training data and gets the patterns slightly wrong.

with notebooklm

drop the tutorial URL into NotebookLM

NotebookLM transcribes and summarizes the exact patterns from the video. Claude Code generates working code from that summary — not from training data from 2024. from today's source.

working implementation from the current tutorial. zero drift.

before

onboarding a new engineer

new teammate joins. you spend 3 hours on calls walking them through the codebase. they still ask you questions for the next 2 weeks because context doesn't transfer cleanly.

with notebooklm

drop your codebase into NotebookLM

NotebookLM generates a structured onboarding document — architecture overview, key patterns, gotchas, decision log. it can also generate an audio walkthrough your new teammate can listen to.

your new teammate gets context without asking you anything.

the rule nobody tells you

CLAUDE.md has a 200-line auto-load limit

Claude only reads the first 200 lines of your CLAUDE.md automatically. everything after that requires manual loading. this changes how you should structure it.

wrong approach

CLAUDE.md as a knowledge dump

×full API documentation pasted in
×tutorial walkthroughs and how-tos
×framework setup instructions
×tool comparisons and research notes
×reference tables and version histories

right approach

CLAUDE.md as a behavior layer

→rules for how Claude should respond
→project structure and naming conventions
→build command + test command (validation loop)
→what it can and cannot do autonomously
→pointers to external context (NotebookLM)

—

CLAUDE.md = rules and behavior. NotebookLM = research and context.

keep them separate and both get better.
your CLAUDE.md stays under 200 lines. NotebookLM absorbs the research load for free.

day 07 of 30 — claude code series