Beat 01
The information exists, but it's expensive to reach
It's in the ERP, in a policy PDF, in a portal, in someone's head. Every answer costs a search, a login, or an interruption.
Pre-launch — onboarding first design partners
01 — The problem
Your company already knows the answer. Nobody has time to go get it.
Three things are true in almost every company we've looked at.
Beat 02
The work is sequential when it should be parallel
Twelve calls to make, twelve suppliers to chase, twelve documents to check — done one at a time, because there's one person doing them.
Beat 03
The problems surface too late to fix
The certificate expired last week. The claim was rejected on Tuesday. The branch was down 20% on Saturday. By the time anyone notices, prevention is no longer an option.
And the AI you already bought doesn't know you
Generic AI writes a decent email but can't tell you your own leave policy, your headcount, or what a similar line item actually cost. Policy lives in PDFs nobody opens; procedure lives in the head of whoever has been there longest. Every answer needs a human to supply the context first.
Nobody logs in — and access control still holds
Your people never open a portal or type a password; they message their own assistant. But the assistant acts as them, with their credentials, so your existing roles and permissions decide what it can reach. If they can't see a salary in the ERP, neither can their assistant.
Every option so far asks you to choose between useful and safe. Give the AI a service account and it can technically reach everything; lock it down and it's a novelty. LabIB is built so the safety mechanism is the same mechanism that makes it useful.
02 — How it works
Three steps, no new dashboard
People use it from the messaging app they already have open. No new app, no new login, no training session.
1
Everyone gets their own assistant
Each person has a private assistant with its own memory of how they work. It learns their patterns and preferences over time. Nobody sees anyone else's conversations or data.
2
It already knows your company
Company policy, department procedures and live system data are built in. Ask in plain language — Arabic or English — and it answers and acts from your actual rules and your actual systems.
3
A human approves anything that matters
Routine work runs on its own. Anything that reveals personal data or commits people, money or time goes to the responsible person first. Every step is logged in your system of record.
03 — In practice
Ten workflows. Five industries. One platform.
The flagship workflows from each industry — the ones that pay for the deployment on their own. Pick one and watch it run.
Not on this list? The departments below are the same in almost every business — start there.
04 — Output
Most AI gives you a paragraph. LabIB gives you the deliverable.
Every workflow ends in something you can open, send, file or act on.
Spreadsheets
Priced BOQs, cost models, reconciliations, variance reports, forecasts.
Presentations
Board packs, management reviews, client proposals, project updates.
Documents
Reports, submissions, contracts, method statements, formal letters.
PDFs & forms
Filled forms, assembled submission packs, certificates, merged sets.
Q1
Q2
Q3
Analyses & charts
Trends, comparisons, root-cause breakdowns, performance rankings.
Ticket · routed
Register · updated
Structured records
Tickets, registers, logs, trackers — filed into your systems, not attached to an email.
Draft · awaiting approval
Sent after approval
Communications
Drafted emails, messages and notifications — always human-approved before sending.
This is the difference between an assistant and a chat window.
A chat window ends in text. This ends in a file, a filing, a booking or a call — with a person's approval on anything consequential.
05 — By department
Whatever you make or sell, these departments look the same
Pick yours. Each one shows what people ask it on a normal Tuesday, and what it runs on its own.
06 — Capabilities
The parts people don't expect
Each of these is the reason a workflow above collapses from days into minutes.
It makes phone calls
Not a chatbot behind a form — actual outbound calls to suppliers, staff and contacts, with the transcript and outcome reported back. And a dozen at once. The person receiving it needs nothing: no app, no internet, just their phone.
It works in parallel
Twelve suppliers, twelve checks, twelve candidate reviews — simultaneously. This is where days become minutes, and it's the one thing a human team structurally cannot match.
It reads scans, photos and handwriting
Site reports, stamped certificates, delivery notes, handwritten forms, whiteboards, equipment photos. Your paper trail becomes searchable data.
It listens
Voice notes in Arabic or English, from the roadside or the shop floor, transcribed and acted on. No typing from people whose hands are busy — and both languages in one conversation.
It runs without being asked
Scheduled sweeps and event triggers mean the expiry, the variance and the rejection are caught the day they happen — not the month they're discovered.
It works around missing APIs
Government portals, supplier ordering sites, legacy web apps your vendor won't open up — it navigates, fills forms and extracts information like a person would. This removes the most common reason integrations “aren't possible”.
The one that compounds
It gets better at your business
Most software is the same on day 300 as it was on day one. This is the capability that separates LabIB from every tool that isn't.
It learns each person individually
Every assistant builds its own understanding of how that person works — the format they want reports in, the suppliers they check first, the level of detail they need. Two people in the same department get assistants that work differently, because they do.
It writes down how the work is actually done
When it solves something, it captures that as a reusable procedure. The tenth time your team runs a process it's faster than the first — because someone wrote down how you do it, and that someone was the assistant.
Undocumented knowledge stops walking out the door
Every company runs on procedures that live in the head of the person who's been there longest. LabIB captures that operating knowledge as it's used, so it survives turnover instead of leaving with it.
But learning never becomes policy on its own
Individual learning stays private to that person. Department-level patterns are surfaced to a human as a proposal, never applied automatically. A lead approves it — and only then does it become the standard.
So you get the adaptivity without the drift: your team's real practice informs your official process instead of silently replacing it. One person's shortcut never quietly becomes everyone's rule — and nothing an assistant learned can override a company policy.
07 — Connections
It works with what you already run
Nothing migrates, nothing gets replaced, and your team keeps working in the tools they already use.
Where your people already are
One assistant, reachable wherever that person actually works. Deployment is configured for whatever your team already uses.
TelegramWhatsAppSlackMicrosoft TeamsGoogle ChatDiscordSignalEmailSMS
SMS matters more than it sounds. Drivers, field crews and site workers without smartphones or data are included too. Most tools quietly leave them out.
And what your business runs on
Connected under each person's own login — the system of record stays the system of record.
Your ERP — connected per employee, not through a service account that sees everything.
Google Workspace & Microsoft 365 — mail, calendar, drive, and the Office file formats, per person.
Notion, Airtable, Obsidian, IMAP/SMTP — knowledge bases, structured records, full inbox triage.
Anything with an API — CRM, HRIS, accounting, WMS, maintenance, fleet, POS, lab systems, machine data. The honest answer to “does it work with X?” is yes, with an integration built for X.
Systems with no API at all — it drives a browser like a person: navigates, fills forms, extracts what's there.
The phone network — real calls to real phones, in parallel, with transcripts returned.
08 — Security & trust
Built for the questions your auditors will ask
These claims are true today because they are architectural — not because of a policy we promise to follow.
It sees only what the person sees
Each assistant acts with that employee's own credentials. Your existing permissions do the access control — nothing to re-configure, nothing to trust the AI about.
Private between people
Assistants can't read each other's conversations, memory or notes. A person's history is as private as their direct messages.
A human decides anything consequential
Disclosing personal data, committing people, money or time, or writing to a system of record — all require approval from the responsible person, under their own identity.
Rules that can't be argued with
Company and department policy is loaded on every single interaction and is read-only to the assistant. Nothing anyone types can suspend or reinterpret it.
Built against manipulation
Anything arriving from outside — a message, a document, a field in your ERP — is treated as information about a request, never as an instruction to obey.
Your deployment, your data
One isolated environment per company, on your infrastructure or a private server. Every request, approval and disclosure is recorded in the system you already audit.
The name
لبيب — one who understands, not one who obeys
لبيب describes sound judgment: sharp-minded, quick to grasp. From لبّ, the core of the mind. The English reading arrives at the same place from the other direction — a lab is where things get built and tested, and an intelligence bureau is a department's own desk. The unit of deployment here is literally the department. One name, two languages, one pronunciation: la-BEEB.
09 — Deployment
Start with one department, read-only
Setup is measured in hours. Trust is earned in stages — this is the rollout we recommend.
Step 01
One departmentOne environment, connected to your existing systems through their APIs.
Step 02
Answers onlyThe assistant answers and reports, and changes nothing. Your team watches what it says.
Step 03
Approval-gated actionsTurn on the work that writes back, calls out and files — each behind a named approver.
Step 04
A second departmentEnable cross-department requests through the single audited channel.
10 — Pricing
One platform fee per company
No per-seat scramble. Every plan is the monthly platform fee plus AI usage, billed transparently at what it costs us.
Startup
Under 20 people. The whole platform, nothing withheld.
SAR 1,999/mo
- A private assistant for every person
- Calls, parallel work, documents, portals
- Two system integrations
- Shared-infrastructure deployment
Design partner
Company
Under 300 people. The plan most companies land on.
SAR 9,000/mo
- Everything in Startup, for the whole company
- Department agents and cross-department requests
- ERP integration under per-person permissions
- Your own isolated environment
- Workflows built around your real procedures
Enterprise
Over 300 people, multi-entity, or on your own infrastructure.
Let's talk
- Everything in Company, at scale
- Multi-entity and multi-site deployments
- On-premise or private cloud, your region
- Custom integrations and a named engineer
AI usage, billed transparently
Model spend is passed through per company, with the right model routed to each task — heavy reasoning where it matters, fast and cheap where it doesn't.
Onboarding & integration
A one-time setup fee, quoted after we review your systems' APIs. Setup itself is measured in hours, not months.
Where it runs
Your own server, a private VPS, or our managed infrastructure. One environment, one storage volume, one credential set per company.
What we need from you
A system of record with a usable API, one department to start with, and a named approver inside it.
Who it's for
Companies whose departments have to coordinate — and where the work is operational, repetitive and currently sequential.
Who it isn't for
Companies with no system of record, or teams looking for a public-facing customer support bot.
11 — Questions
Before you commit
Is this just a chatbot with our data in it?
No. Answering questions is the smallest thing it does. It places calls, drives portals that have no API, reads scans and photographs, runs a dozen checks simultaneously, and produces the workbook, the pack or the filing at the end. The chat window is the interface, not the product.
Is our data used to train AI models?
No. Company data stays within your deployment. The enterprise AI providers used for processing do not train on business data by default — we confirm your specific configuration during setup.
What if it gets something wrong?
It cannot act unilaterally on anything consequential. Every disclosure of personal data and every commitment of resources needs a human approval, and every step is logged. Mistakes surface as a proposal somebody declines — not as an action already taken.
Can employees use it to see things they shouldn't?
No. The assistant acts with that employee's own credentials. If they can't see it in the system, the assistant can't retrieve it — and nobody has to configure a second set of permissions.
One of our systems has no API. Is that a blocker?
Usually not. LabIB can drive a browser the way a person does — navigating, filling forms and extracting information from government portals, supplier sites and legacy web apps. We confirm feasibility per system during setup.
What if we don't use Telegram?
WhatsApp, Slack, Teams, Google Chat, Signal, Discord, email and plain SMS are all supported. SMS means the people without smartphones or data — drivers, field crews, site workers — are included too.
Do we need to change our ERP?
No. LabIB connects to your existing systems through their APIs. The ERP stays the source of truth and nothing migrates.
What happens when someone leaves?
Access is revoked immediately and centrally, and their assistant is decommissioned. Offboarding is a single action — and what the company learned stays with the company.
LabIB · لبيب
We're onboarding design partners now
We're working with a small number of companies to shape LabIB around how they actually operate — their departments, their systems, their rules. If that sounds like yours, let's talk.