LP
AI agent automation for mid-sized companies
The money you earned is leaking
where no one is looking
An invoice never sent. A deal nobody chased. A customer who asked and got no reply. That isn't an efficiency problem — it's revenue walking out the door every month. LP builds AI agent workflows for mid-sized companies that find those leaks, and pulls your team out of the repetitive work along the way.
- Trigger
- AI reads it
- Runs your rules
- Notifies / delivers
Works with the tools you already use
- Telegram
- LINE
- Discord
- Claude
- Gemini
- Google Sheets
- Notion
- Airtable
- Gmail
- Google Drive
- WordPress
- GitHub
- n8n
Where the industry actually is
This isn't a trend prediction — it's already happening
Every number below names its source and links straight to it, so you can check it yourself. A lot of the AI statistics floating around can't be traced to any real study — we don't use those.
37% of companies with 250+ employees and 32% of those with 100–249 are already using AI — versus under 20% of companies with fewer than 20 people. Mid-sized companies are adopting faster than small ones.
U.S. Census Bureau BTOS survey (~1.2M businesses; government data) ↗40%By the end of 2026, an estimated 40% of enterprise applications will include task-specific AI agents — up from under 5% in 2025.
Gartner (a 2025 forecast, not a measurement of what has already happened) ↗66%66% of organizations say AI has already delivered productivity gains, and 40% say it has reduced costs.
Deloitte State of AI in the Enterprise (3,235 senior leaders, 24 countries) ↗60%Sales reps spend 60% of their time not selling: only 40% goes to actual selling, and 11% to data entry.
Salesforce State of Sales 2026 (4,050 sales professionals, 22 countries; Salesforce is a CRM vendor) ↗45%45% of business leaders admit revenue leakage is a systemic problem at their company, and roughly 74% have no automated revenue-assurance process at all.
BCG revenue assurance research (2020; BCG also sells consulting in this area) ↗6%Only 6% of companies see a return within one year of investing in AI. Bain's survey of 951 companies also found that most of those targeting an 11–20% cost reduction actually landed in the 0–10% range.
Deloitte AI ROI research / Bain (951 companies) — unflattering data, included on purpose ↗We include that last number on purpose — because it's exactly why we argue for stopping one leak first and scaling only once you've seen the result. All-in projects are the ones most likely to end up in the group that misses its target. Where you're leaking is specific, and whether it can be fixed should be proven by what actually runs — not by a number on a slide.
The cost of doing nothing
Revenue usually leaks from three places
What these leaks have in common is that no one is watching — not a skills problem, just nobody with the time to reconcile it line by line.
Past money: work done, never billed
What you delivered and what you charged live in two separate systems that no one reconciles — so invoices get forgotten, extra scope goes unbilled, and expired discounts keep applying. Revenue leaks, and nobody notices.
Future money: deals that stall out
A deal that was close to closing sits stuck at some stage, and when reps get busy no one is watching. By the time anyone notices, the customer has usually signed with someone else.
Incoming money: inquiries no one answers
Customer inquiries get buried in inboxes and message threads, and a busy front line has no time to reply. Customers who don't hear back simply go elsewhere.
In one test run, LP's reconciliation workflow surfaced over $15,000 in missed billing (test data, not a real client's books). The real number differs by company — the ways it leaks don't.
How a workflow runs
From trigger to done — it runs every step for you
This is the skeleton of an LP agent workflow. Each step gets customized to your industry and the tools you already use.
Where does your industry leak money?
Here are a few of the most common cases. Click through to see how we automate them.
Appointment-Based Services (Clinics, Salons, Restaurants)
Customers book and ask questions by phone, LINE, or DM, and when the front desk is busy with people in-store there's no time to reply — so they book elsewhere; and the back-and-forth to confirm times and send reminders eats up hours.
See the fixMarketing & Social Media Agencies
Running social media for multiple clients means constant scheduling and content sourcing that eats up half your team's capacity.
See the fixCompanies with a Sales Team (B2B Sales)
Dozens of prospects and proposals sit scattered across your sales stages. When reps get busy, half-finished deals go unwatched, and deals that were close to closing quietly slip away — usually noticed too late.
See the fixHow we work together
Starts with one free message, delivered in six stages
The early stages cost nothing and come with no pressure to decide: first we pin down what you actually need, and find the gap between that and your real data. We only start building once it's clear it's worth doing.
- 01Free
Scope the need
First we turn a vague ask into something written down. We'll pin down what result you want, which existing tools it has to connect to, whether you have sample data, and where your hard limits are (for example: it must never send anything on its own; a human has to approve). What you get isn't a quote — it's our understanding of your process and spec, written out for you to confirm, so you never end up saying "this isn't what I asked for" at the end.
- 02
Data health check
We'll ask for a sample of your real data and screenshots of your current process — we won't build off a verbal description. This stage exists to catch the gap where a demo runs beautifully and real data blows it up: inconsistent naming, mixed formats, tools wired up differently than we assumed. You get a list: what needs cleaning up first, and what will affect the timeline.
- 03
Build
This is where work actually starts: connecting the workflow to your real data sources and output destinations, running in your environment rather than our demo one. What's delivered is a system that genuinely runs — not a demo.
- 04
Training & handover
We walk your team through the whole flow once, live, and hand over an operating manual. It spells out how the system works, who to go to when something breaks, and how far you can take it yourselves. You won't be coming back to us for every small thing.
- 05No extra charge
Sign-off & go live
You run it yourself, confirm it's right, and we both agree the build is done. It's a deliberate checkpoint: with a clear sign-off, both sides stay aligned on whether it's actually finished, instead of drifting into an open-ended grey zone.
- 06Optional
Ongoing maintenance
We won't lock you into a long-term contract at build time. Once the system is running and has genuinely caught something for you, if you want someone keeping an eye on it, we'll talk then. And if we do, the scope gets written down: monthly maintenance allowance, which workflows are covered, and how fast we respond. We work entirely in writing, so response times get stated plainly rather than leaving you expecting us on call.
Automation systems already running
These aren't concept demos — they're systems LP has already built and runs in production.
AI Sales Pipeline Bottleneck Detection
Built for companies whose sales team manages a large volume of deals.
Deals don't fall through loudly. They sit at one stage for two weeks. Three. Nobody touches them. Reps are busy chasing the new thing, so the old one goes quiet, and by the time someone remembers it, the customer has signed elsewhere. This workflow watches every deal, pulls out the ones stuck too long, and ranks them by value and urgency. You see what's slipping.
View this case studyAI Smart Inbox Manager
Built for sales and support teams handling a high volume of business email.
Email doesn't sort itself. So someone reads every one, decides whether it matters, decides whether to reply — every morning, before anything else gets done. On a busy day the important customer email is in there somewhere, and you find it on Thursday. This workflow takes over the triage. The inbox stops being the first thing you fight.
View this case studyAI Revenue Reconciliation (Catch Missed Billing)
Built for design, consulting, and agency firms that bill by project or contract.
One test run surfaced seven missed charges, over $15,000 in total. (Test data.) The ways it leaks are boring: an invoice never sent, extra work never charged, a discount that expired months ago and still applies. Nobody reconciles it, because what you did and what you billed sit in two systems and comparing them takes days. Who has days? This workflow reconciles them and records what's missing.
View this case studyHow small you can start
You don't have to go all in — start with one leak
The order we'd suggest: let us find where it leaks first, then fix the one that stings most, and only scale up once it's proven.
- 01
Find where it leaks
Over email, we dig into where you're currently losing the most money and the most hours, then send back an assessment: whether it's worth doing, roughly how, and what it should stop.
Free — no cost of any kind
- 02
Stop one leak
Pick the one that stings most, and we build a single workflow and get it running — so real results, not promises, tell you whether to go further.
One workflow; scale up once you see results
- 03
Full rollout
Multiple workflows connected into one system, refined continuously after launch. If you'd rather not wait for a custom build, ready-made automation products are available too.
Custom or ready-made — both welcome
Common concerns
What you'll worry about before putting AI into your process
These are principles the systems are actually designed around — not just talking points.
What if the AI gets it wrong?
It doesn't guess when it can't tell. If data is missing, dates are broken, or amounts don't line up, it flags the item as "needs review" and attaches the raw data for you to judge — it won't invent an answer to hand you.
Will it act on its own and send things to my customers?
No. Anything destined for a customer — a catch-up invoice, a re-engagement email — is only ever produced as a draft that counts once you approve it. The system will not send things to your customers by itself.
If something goes wrong, can we trace it?
Yes. Every judgment and every action leaves an auditable record — including items marked as junk or ignored. Even a misjudgment can be traced back and recovered.
Can it connect to the systems we already use?
Yes. The data-reading layer is designed to be swappable (CRM, accounting software, email, Notion, and so on), and the matching and judgment logic isn't hard-wired to specific fields. Change systems later and you change the reading layer, not the engine.
Is this meant to replace my staff?
No. It takes over the repetitive, easily-missed work nobody wants — sorting email one by one, reconciling line by line, watching which deal has stalled. Judgment and decisions stay with people.
I'm not sure our situation is worth it.
Then ask first. The assessment is free of charge, and we'll tell you honestly whether it's worth doing and roughly how — including telling you straight if we think it isn't.
Not sure where to start? Tell us the most painful repetitive task in your company, and we'll help you figure out if it's worth automating.