Repetitive work, disconnected tools, slow response. Growlith wires AI agents, workflows and intelligent routing into the stack you already pay for — an operations layer, owned by you.
Missed leads, manual handoffs, inconsistent processes, overloaded teams and work that happens too slowly — because it still needs a person to remember it. The opportunity is not another hire. It is the hours and the pipeline you already paid for, leaking.
This is an operations problem
Hours autopsy
Set the sliders to your desk. This is not a headcount forecast — it is the loaded cost of repetitive hours, plus the pipeline that dies while the inbox waits. The work your team should never have been doing.
People on repetitive ops
Coordinators, SDRs, ops, support doing machine work
Hours / person / week on busywork
Copy-paste, chasing, routing, status, re-entry
Loaded hourly cost
Salary, tools, management overhead
Inbound leads / day
Forms, chat, ads, partners
Minutes to first human reply
Median, including nights and weekends
The ledger
Repetitive hours / year4,992
Labor spent on machine work$275K
Pipeline lost to slow reply$521K
Drag per year
$796K
Recoverable with an operations layer
≈ $516K
A conservative 55% of repetitive labor plus most of the pipeline that dies in the inbox. Agents, routing and workflows — not another round of hiring. Every Growlith ops audit opens with this ledger.
The form fires at 11:47pm. A human sees it at 9:12am. By then the buyer has already spoken to whoever answered in four minutes. Night, lunch, a busy Tuesday — the clock does not care that your team is excellent.
The fastest reply wins. You are not it.
Sales copies a row into Slack. Ops pastes it into a sheet. Finance re-types it into the CRM. Every hop is a chance to drop the ball, duplicate the record, or wait for someone who is in a meeting.
The process lives in people's heads.
The playbook is a Notion page last edited in March. One operator follows it. Two improvise. New hires invent their own. Quality is a function of who was on shift — which is not a system.
The same task, five different ways.
You hire to absorb volume that should never have been human. Senior people chase invoices, chase leads, chase status. The work that actually needs judgment waits behind the work a machine could have finished at 2am.
Headcount is the only scaling plan.
Approvals sit. Quotes wait. Onboarding stalls because someone has to remember the next step. Valuable work happens at the speed of inboxes — which is to say, not at the speed of the market.
The bottleneck is a person with a to-do list.
CRM, email, Slack, sheets, the form tool, the billing desk — none of them talk unless a human is the API. Context dies in copy-paste. The stack is expensive. The integration is still a person.
Twelve tools. Zero pipeline.
The human API
A team that is the integration layer
The Growlith operations layer
Systems that work while the team sleeps
The human API
A team that is the integration layer
The Growlith operations layer
Systems that work while the team sleeps
The human API
First response whenever someone opens the inbox
The Growlith operations layer
Leads scored, qualified and routed in seconds — night included
The human API
Handoffs that live in Slack threads and memory
The Growlith operations layer
Trigger-based workflows with a named owner and an audit trail
The human API
A Notion playbook nobody follows the same way
The Growlith operations layer
Process automation that executes the playbook, every time
The human API
Hire another coordinator to absorb the volume
The Growlith operations layer
Agents on the repetitive work; humans on the exceptions
The human API
Zapier spaghetti with no guardrails or budget
The Growlith operations layer
An operations layer: agents, routing, approvals, telemetry
The human API
Automations that die when the contractor leaves
The Growlith operations layer
Workflows in your accounts and repo — owned by you
What this is not
A ChatGPT wrapper. A Zapier retainer. A “we'll prompt it for you” slide. If it does not score and route leads, execute a playbook without a human remembering, and report hours and SLA to the board, it is a demo. Demos do not work the night shift.
Not a tool. Four systems in one AI Automations engine — agents, workflows, routing and process — that turn manual operations into intelligent ones.
4/4 capabilities online
1// capability 01 — ai agents2defineAgents({3roster: ["lead-qualifier", "support-desk", "research-ops"],4tools: ["crm", "email", "slack", "sheets"],5guardrails: { approval: "human", budget: usd(2400) },6sla: { firstTouch: "9s", escalate: "vip · legal · >$5k" },7});89// 4 agents live · 12,412 jobs / wk · humans on exceptions
Operators that never sleep
Custom agents for qualification, support, research and the work nobody should re-type.
Not a chatbot bolted onto a help centre. Agents with a job, a tool belt and a budget — lead qualifier, support desk, research ops — running against your CRM, inbox and knowledge. Guardrails decide what they may touch. Humans take the exceptions. The night shift is staffed.
What ships
How it feeds the machine
Agents are the hands. Workflows tell them when to move; routing tells them where the work goes. Without a named job and a budget, an agent is a demo.
12.4K
Tasks auto-run / week
Growlith turns the work your team should never have been doing into a system — identified, automated, connected, executed, monitored and optimized. Six stages. One operating loop. Owned by you.
Identify → automate → connect → execute → monitor → optimize
The ops audit: every repetitive task, every handoff, every inbox that is secretly a queue. We map where hours go, where leads die, and which playbooks only exist in someone's head. The ledger from section one becomes a backlog with owners.
Ships — Process map, hours ledger, lead-SLA autopsy, candidate backlog
The highest-leverage loops go first — qualification, routing, status, chase — encoded as agents and playbooks with guardrails. Quick wins live in weeks two to four. Nothing ships without a kill switch and a human exception path.
Ships — Agent roster, playbooks, guardrails, kill switches
CRM, email, Slack, forms, sheets, billing — wired so a human is no longer the API. Idempotent writes, retries, dead-letter queues. The stack you already pay for starts behaving like one machine.
Ships — Connector graph, retries, dead-letters, named owners
The night shift goes live. Leads score and route in seconds. Processes run the same at 02:14 as they do at 14:02. Approvals wait for humans; everything else does not. This is the moment the team stops being the integration layer.
Ships — Production cutover, SLA clocks, approval desks, runbooks
Uptime, SLA, hours returned, exception rate — in the same control room as CAC and LTV. Failures page a named owner, not a Slack channel that went quiet. You will be able to state, to the hour, what the layer returned this month.
Ships — Ops P&L, SLA dashboards, exception rates, incident log
Models retrained as inbound shifts. Workflows re-cut as the company grows. New playbooks promoted from the exception queue. The layer is operated, not launched — that is why it compounds instead of rotting into Zapier spaghetti.
Ships — Weekly tuning, model retraining, playbook promotion, backlog
01
9s
Median lead response
first touch, including nights
02
318h
Hours returned / month
median managed operations layer
03
12,412
Tasks auto-run / week
across live agent rosters
04
99.98%
Workflow uptime
production graphs, rolling 90d
Verified deployments
Inbound that used to wait until morning now hits the right desk in nine seconds — including the DIFC night shift.
Request the auditOnboarding, chase and QA encoded as playbooks. 318 hours a month came off coordinators and went back into the work that needed judgment.
Request the auditA qualifier agent plus a connected CRM/Slack graph turned a 90-minute first touch into a scored, routed, human-ready file.
Request the auditYou do not need more people to handle repetitive work. You need systems that work while the team sleeps — agents on the loops, workflows on the stack, routing on inbound, playbooks on the process. Headcount is a last resort. The operations layer is the first.
The questions serious operators ask before they let anyone near the workflows their company actually runs on. If yours isn't here, any bureau desk answers within one business day.
contact@growlithacademy.comZapier is glue. A chatbot is a window. An operations layer is a system: agents with jobs and budgets, workflows with retries and owners, lead routing with a scored SLA, and playbooks that execute the same at 2am as they do at 2pm — reported as hours returned and pipeline recovered. Tools move data. We build the machine that decides what moves, when, and who gets the exception.
Inside them. HubSpot, Salesforce, Klaviyo, Slack, Gmail, Sheets, Notion, the form tool you already pay for — we ship agents, graphs and playbooks into that stack. If a platform cannot hold the trigger, the idempotency or the audit trail the layer needs, we say so in week one and propose the cheapest architecture that can. No new SaaS tax for its own sake.
Guardrails are a feature of the architecture, not a prompt. Action and spend budgets, approval desks for money and legal, least-privilege tool access, a kill switch, and a human exception queue. Anything above threshold waits. Anything junk is suppressed. Run history is finance-grade — who, what, when — not a Slack scroll you hope someone read.
The hours ledger is visible in week one. Quick-win loops — inbound qualification, routing, status, chase — go live in weeks two to four, including the night shift. Median systems return a few hundred hours a month by day 90, with a 9-second first-touch SLA on inbound. Your numbers appear in the audit before you commit to anything.
You do. Workflows live in your accounts and repo, agents run against your keys, dashboards ship to your stack. No hostage-keeping, no 'automation built by contractor X' lock-in. If we stop working tomorrow, the night shift keeps running — that is the entire point.
Least privilege, named secrets, no training on your customer data unless you explicitly opt a model in. Regional processing follows the bureau — NYC, London, Sydney, DIFC — and the Data Processing terms already on this site. Agents see only the fields the job requires. Logs are retained to the schedule you set, not ours.
Especially there. When a lead is worth thousands, nine seconds versus ninety minutes is the quarter. High-ticket systems lean on qualification, VIP fast-lanes and human handoff — not a thousand cheap tickets. Volume ops get the hours back. High-ACV desks get the SLA. Both are the same engine, different playbooks.
Identify → automate → connect → execute → monitor → optimize. Week one: the hours ledger and the process map. Weeks two to four: quick-win agents and routing live. Day 90: the full layer — roster, graphs, playbooks, ops P&L. After the first quarter, rolling 30-day, because operations are operated, not launched. A principal replies within one business day of qualification.
High-ticket means high-intent — on both sides. Four questions route you to the right bureau pod — then a direct line to the Academy Team, if you'd rather not wait.
STEP 01 / 05
You don't need more people to handle the loops a machine can own. One 30-minute ops audit shows the hours, the missed leads and the playbooks waiting to run — while the team sleeps. No decks, no juniors, no obligation.