OUTCOME #1
The retyping stops
Data moves between systems without anyone copying it. The job changes from filling in the spreadsheet to checking what the system flagged.
Services
AI automation services that still run six months later
Most automations break quietly. An API changes, a field changes type, the person who built it leaves, and the team goes back to doing the work by hand.
Phos AI Labs decides which steps need AI at all, builds the automation inside the tools you already use, and stays to keep it running.
AI automation is software that runs the repetitive steps of a business process, such as reading documents, updating systems, routing approvals, and drafting replies, with AI handling the steps that need judgment and a person approving what matters. Phos AI Labs starts by sorting the steps. Most need a plain rule, which is cheaper to run and never makes up an answer. AI goes where the rule runs out.
Claude (Anthropic) Partner · Select OpenAI Partner · CCA-F certified team
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75% less
training admin time for GAF, across 1,200 events a year
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40+ AI systems
shipped to production in the last 6 months
Trusted across 450+ builds
Why do so many AI automations stop working?
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, because of rising costs, unclear value, or weak risk controls. The causes we see in audits are more ordinary.
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It automated a broken process
One team used three different tools to produce the same weekly report, depending on who was available. Automating that only makes the inconsistency faster.
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Three sources of truth
The CRM says one thing, the ERP says another, and the Excel file someone updates on Mondays is the one people trust. An automation has to read from one of them.
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An agent doing a scheduled job
A report that runs at 6 AM every day needs a scheduled job. Paying a model to think about it every morning runs up a token bill for work a few lines of code would do.
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Nobody owns it after launch
A field in the ERP changes type and the nightly sync fails without an error. Reports show zeros. The person who built it is gone, and nobody in the building can fix it.
Rule, AI step, agent, or leave it alone?
Every step in a process lands in one of four places. Sorting them is most of the work, and it decides what the system costs to run every month.
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Leave it alone
If a task takes 30 minutes a week, automating it costs more than it saves. The budget goes to the step costing $2,000 a week before the one costing $200.
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A plain rule
No judgment involved. If an invoice is over $5,000, route it for approval; otherwise, pay it. A workflow tool or a few lines of code runs this for almost nothing, the same way every time.
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An AI step inside a workflow
Reading a customer PO from a PDF and filling the order form. Matching an invoice to the warehouse receipt when one says pounds and the other says units. The AI drafts, a person confirms, and each confirmation is logged to a name.
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An agent
Work that needs judgment across many tools, like researching a prospect across the web and your CRM, or answering a client with the full account history. Agents cost the most to run, so they come last.
Automation first. Then the AI layer.
At GAF, the first version fixed the sequence. Classes could no longer be closed in the wrong order, and rosters reached trainers without anyone sending them. The AI layer came after, on top of clean, structured data.
Phos AI Labs builds in that order on every engagement. Once the rules run, the data is consistent, and AI working on consistent data gives answers people trust.
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Deliverable 1 of 11
What you walk away with
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Process map
How the work runs today, step by step, including the parts that live in WhatsApp and email.
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Automation register
Every step sorted into leave it, rule, AI step, or agent, with the hours and dollars behind each one.
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Source-of-truth decision
Which system owns each record. If your ERP's API is slow, a nightly copy the automation can read.
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Build-or-buy call
Keep, extend, replace, or retire, for every tool the process touches.
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Production automations
Built in n8n, in code, or in the tool you already pay for, whichever fits the job.
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Approval points
Where a person confirms, what they see when they do, and a log of who approved what.
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Access plan
Read-only to start, write access granted by role and by use case, every write logged.
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Run-cost estimate
Hosting and model usage per month before you commit, with each task routed to the cheapest model that does it well.
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Monitoring and alerts
Failed runs and silent syncs flagged to a named owner the same day.
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Runbook and handover
How each automation works, how to change it, and who to call.
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Monthly review
What ran, what it saved, what broke, and what to automate next.
What actually happens once you start?
First the audit, then two automations, then a monthly rhythm of building and measuring.
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Map the process as it really runs
AI voice interviews with the people doing the work, plus read-only access to the systems. We look for the handoffs where someone retypes what another system already holds.
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Price every step
Time per task, times how often it happens, times the people doing it. Your department heads check the numbers before anything is built.
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Build two first
One automation picked for return, and one quick win the team sees in the first weeks. People who watch AI take over their copy-paste work tend to back the next project.
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Run with a person in the loop
Every AI step starts with a person approving each output. As the approval rate climbs on real work, routine cases move to the system and people keep the exceptions.
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Measure and expand
Hours saved, error rates, and run cost, every month. The next automation earns its place from those numbers.
Where does automation sit in the engagement?
Automation runs through all three phases. The audit finds it, the team learns to work with it, and implementation keeps it running.
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AI Consulting
The audit that finds where AI belongs and what it is worth.
See AI Consulting → - 02
AI Enablement
Your team gets fluent on the work AI is about to touch.
See AI Enablement → - 03
AI Implementation
Agents and systems that run the work, built and embedded.
See AI Implementation →
What can Phos AI Labs automate for you?
Each area has its own page with the workflows we build most. Start with the one where your team loses the most hours.
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AI Sales Automation
Lead research, qualification, CRM updates, proposals, and follow-up, built around how your team sells.
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AI Marketing Automation
Research, content, campaigns, and reporting connected, so qualified demand reaches sales with its context.
Why does the first automation depend on your industry?
A title research firm's staff type county records into forms by hand. A contractor's change orders arrive as WhatsApp voice notes. A distributor's team rekeys every customer PO into the ERP.
Don’t see your industry?
If someone on your team retypes data another system already holds, that is your first automation.
Book a callWhat does this actually change?
Companies come to Phos AI Labs with people retyping data and automations nobody owns. They leave with workflows that run, a person behind each one, and a monthly number they can check.
OUTCOME #2
Every automation has an owner
Each workflow has a named owner, monitoring, and a runbook. When an API changes, someone sees the failure the same day and fixes it.
OUTCOME #3
A run cost you can defend to the CFO
You know what each automation costs per month in hosting and model usage, and what it saves. Spending $2,000 a month on tokens is an easy yes when it replaces $4,000 of manual work.
Do these automations hold up in production?
Three builds, three kinds of work: field operations, sales and client service, and a company's own back office.
GAF
1,200 training events a year on a spreadsheet. The automation foundation first, then the AI layer on top.
We were using spreadsheets and hoping nothing fell through the cracks. After working with Phos AI Labs, their AI implementation handles 75% of the administration and the team handles the work they're actually great at.
Matt Hegge, Director, GAF
Payroll Mexico
An AI Sales Analyst that qualifies prospects every morning, and an Operations Assistant with full client context in every ticket.
I set up two recurring tasks, one daily, one weekly. We have a bunch of leads to follow. I don't know how Phos AI Labs does this. I am amazed.
Franklin Delano Frith II, Owner, Payroll Mexico
LOW/CODE Agency
A 40-person company where every proposal needed the CEO. The audit found 29 opportunities; two AI employees took the top two.
Every proposal needed me in the room. The sales team couldn't move without me. Now the AI handles the routine ones. I review the complex cases.
Jesus Vargas, Founder and CEO, LOW/CODE Agency
Read the full story → - 707 qualified leads from 2,400 dormant contacts
- 99.7 hrs a week of recoverable time, estimated
Why Phos AI Labs over an AI automation agency?
About half the search results for “AI automation agency” teach people how to start one. Before you hire anyone, ask who owns the workflows, what they cost to run each month, and who fixes them when an API changes.
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A software team behind every automation
The team behind Phos AI Labs has delivered 450+ software projects for LOW/CODE Agency clients. When a workflow tool hits its limit, the same engineers write the code.
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Built in your accounts
Every automation lives in your cloud and your workflow tool, under your API keys, as work for hire. If you ever stop working with us, everything keeps running.
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We stay after launch
Models change and APIs change. Most clients keep at least a small support plan, so someone watches the runs, rotates the keys, and fixes what drifts.
How much does AI automation cost?
AI automation with Phos AI Labs starts with discovery: from $10,000 for companies under 100 employees, and scoped to the teams and systems involved above that. After discovery, building and running the automations typically costs $10,000 to $50,000 a month, depending on how many workflows are in flight.
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Discovery
from $10,000 under 100 employeesProcess map, automation register, build-or-buy call, run-cost estimate, and the two first projects scoped. Larger organizations get a scoped price. You own all of it.
Start here -
Build and run
from $10,000 /mo.Production automations built, monitored, and improved, with a monthly review of what ran and what to build next. Up to $50,000 a month, scaling with how many workflows are in flight.
Book a call -
AI Employees
all-inclusive $2,500 /mo. per roleOne AI employee in one role, set up and managed by Phos AI Labs, all-inclusive with no usage fees. For teams that want one job covered before a full engagement.
See AI Employees
Run costs are part of the decision, so you see them first. Hosting and model usage are billed by the providers to your own accounts, and we estimate them before you commit. For one 100-person distributor, an order-intake AI layer was estimated at $180 to $320 a month in hosting and $900 to $2,200 a month in model usage.
Keep going
Where automation connects to the rest of the work.
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AI Implementation
The phase where automations, agents, and AI employees run inside your operation.
Explore → - 02
AI Employees
One AI employee in one role, set up and managed for you, from $2,500 a month.
Explore → - 03
AI Readiness Scorecard
Ten questions and a personal score, before you talk to anyone.
Explore → - 04
Case studies
What Phos AI Labs has shipped, with the numbers behind each build.
Explore →
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FAQs
What is AI automation?
AI automation is software that runs the repetitive steps of a business process, such as reading documents, updating systems, routing approvals, and drafting replies, with AI handling the steps that need judgment. At Phos AI Labs, a person approves what matters, and plain rules handle every step that doesn't need AI.
How much do AI automations cost?
With Phos AI Labs, discovery starts at $10,000 for companies under 100 employees and is scoped above that. Building and running automations typically costs $10,000 to $50,000 a month. Hosting and model usage are billed to your own accounts, and we estimate them before you commit, often in the hundreds to low thousands per month.
What is the difference between AI automation and regular automation?
Regular automation follows fixed rules and does the same thing every time. AI automation adds steps that need judgment, like reading a messy PDF or drafting a reply. Rules are cheaper and fully predictable, so Phos AI Labs uses them wherever they work and adds AI only where a rule runs out.
What does an AI automation agency do, and how is Phos AI Labs different?
An AI automation agency connects tools and AI models to run parts of a business process. Phos AI Labs adds the steps many skip: an audit before the build, a run-cost estimate, approval points with logs, monitoring, and a support plan. Everything is built in your accounts, by a team with 450+ software projects behind it.
Do you use n8n, Make, or Zapier?
Yes, when they fit. n8n, Make, and Zapier are good at connecting tools with clear rules, and we often build there or extend what you already have. When a workflow outgrows them, our engineers write it in code. Phos AI Labs earns no commissions from any platform, so the choice follows the job.
Can we automate without replacing our software?
Usually, yes. Most automations run on top of the ERP, CRM, and inbox you already use. If a system's API is weak, we build a nightly copy the automation can read. We recommend replacing a tool only when the build-or-buy call shows it costs more than it returns.
Can a person approve what the AI does?
Yes, and that is how every AI step starts. The system drafts, a person confirms, and each confirmation is logged to a name before anything reaches your ERP or CRM. As accuracy holds up on real work, routine cases move to the system and people keep the exceptions.
Which process should we automate first?
The one where someone retypes data another system already holds, and the hours add up. Below about 30 minutes a week, automation rarely pays for itself. Phos AI Labs usually starts two projects at once: one picked for return and one quick win the team sees early.
What happens after an automation is built?
It keeps getting better, and it needs watching. Models improve, APIs change, and edge cases show up that nobody wrote down. One store-support agent we built launched with about 70% of answers approved and reached 94% after weeks of tuning. Most clients keep a support plan so someone watches the runs.