OUTCOME #1
Capacity you can measure
Specific workflows come back with hours attached. The work that never needed judgment stops taking your best people's week.
Services
AI implementation that puts agents inside the work
Your team still assembles the same reports by hand, pulls the same numbers out of three systems, and loses the same follow-ups.
Phos AI Labs builds the agents that take that work and stays embedded to keep improving them.
AI implementation is the work of building AI systems into a company's real operations and running them there: agents, AI employees, and automated workflows connected to the tools the business already uses. Phos AI Labs maps the process, wires in memory and the right tools, then stays in the room while they earn their keep. An agent is only as good as the context you give it before you turn it on. That is what separates implementation from a pilot.
Claude (Anthropic) Partner · Select OpenAI Partner · CCA-F certified team
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40+ AI Systems
shipped to production in the last 6 months
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~6 weeks
to your first working AI system
Trusted across 450+ builds
Why does the work you meant to automate still get done by hand?
Every operator can name the three workflows eating their week. What is missing is a team that can build the thing, connect it to the systems that matter, and keep it alive after launch.
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Work that never needed a person
The same report assembled by hand every Monday. The same data pulled from three systems and pasted into a spreadsheet. Your best people, doing it.
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No team to build it
You have wanted to automate this for two years. Hiring for it means a search you do not have time to run and a salary that only buys one perspective.
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Pilots that never reached production
Something got built, demoed well, and quietly stopped being used. It was never connected to real data, so it was never trusted with real work.
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Six-figure SaaS doing a slice of the job
Three or four six-figure contracts run in parallel, each covering a fraction of what one connected system could. The spend that could fund growth stays locked in licenses.
Every step is one of three things
A twenty-five step process usually resolves into a handful of agents, some plain code, and a small number of human checkpoints. Sorting the steps is the work. Building them is the easy part.
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Plain software
No judgment in it at all. Regular code is cheaper to run, faster, auditable, and it never invents an answer. Most of what companies try to hand to AI belongs here.
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An agent
There is judgment, there are thousands of recorded past decisions to learn from, and being wrong occasionally is survivable. These work best when the output is a yes or no, a routing decision, or a match.
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A human decision, prepared
The cost of being wrong is too high, or there is no history to learn from. The agent gathers every piece of evidence first, so the call takes thirty seconds instead of forty minutes of hunting.
What we do - What you get
The build is the easy part. The context is what makes it work.
This engagement starts with the context layer, then ships production systems on top of it, and keeps a team on them afterward. An agent that knows your business, your voice, and your history outperforms one that only knows the model it runs on.
10 01
Deliverable 1 of 10
What you walk away with
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Company Brain
The living map of how your company actually works: operating manuals, decision rules, customer archetypes, and voice packs any model can read.
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Production agents
Agents built for one job each, connected to your stack, running every day against a measurable outcome.
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AI employees
Named roles that own a function end to end, from prospecting and qualification to ticket triage and daily briefings.
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Private AI Workspace
One company-owned environment where the whole team works, with your access controls and your data inside your infrastructure.
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Systems integrations
Wiring into the tools that run the business: your CRM, your ERP, your help desk, your drive, your inbox.
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Data and memory layer
Structured retrieval so agents answer from your documents and your history instead of general training data.
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Human approval gates
Every consequential action routed for sign-off, so the AI surfaces and prepares and a person still decides.
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Monitoring and measurement
Each system built against a number, tracked after launch, and fixed when it drifts.
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Runbooks and handover docs
How each system works, what it touches, and what to do when it misbehaves, written for your team.
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Roadmap for what's next
The queue of the next highest-value builds, re-ranked every month as the operation changes.
What actually happens once you start?
Ongoing. Starts with the workflows costing you the most, and gets deeper every month.
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We find what is costing you the most
We look at where the team's hours go each week: the repetitive work, the manual reports, the follow-ups that fall through. We pick the two or three with the highest return and build there first.
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We build the context before the agent
The process gets mapped, the memory gets wired, the tools get connected, and the retrieval gets tested against your own documents. This is the step most implementations skip and the reason most of them fail.
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We build, test, and deploy inside your environment
Agents, AI employees, and automated workflows, running in your infrastructure. Your data stays inside your operation and your security team is involved from day one.
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We measure and iterate
Every system is built against a number. If it is not performing we fix it. If a new opportunity shows up we build for it. Nothing ships and gets forgotten.
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We stay in the room
We operate as your embedded AI team: bringing new ideas, taking quick wins fast, and moving into harder processes as the operation matures. Month six should be better than month one.
Where does this sit in the engagement?
Implementation is phase three. Each phase ends with something you own and a decision about whether to keep going, so nothing here commits you to the next one.
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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
You are here. Agents and systems that run the work, built and embedded.
This is for you if:
- Your team still does manual work that has not changed in years.
- You have wanted to automate and have no team to build and maintain it.
- You want a long-term AI partner inside the operation.
This is not for you if:
- You want a single automation and nothing after it.
- You are not ready for an ongoing engagement.
- You have no idea yet which work is worth automating, in which case start with the audit.
What does Phos AI Labs actually install?
Three layers, built in order. The foundation, the environment your team works inside, and the connective layer that runs the operation on top of both.
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Company Brain
The living map of how your company works, structured so any model can read it. The floor everything else stands on.
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Private AI Workspace
One company-owned AI environment for the whole team, with your access controls, your data, and usage you can actually see.
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AI Operating System
The connective layer between your CRM, inbox, project manager, and docs, so your own work keeps making the system sharper.
Why does the first system to build depend on your industry?
A manufacturer, an insurance brokerage, and a logistics operator each lose their week in a different place. The build order follows the operation in front of us.
Don’t see your industry?
If your work runs on drafting, summarizing, or answering from your own documents, there is a fit.
Book a callWhat does this actually change?
Companies come to Phos AI Labs with a list of things they meant to automate. They leave with systems already running, hours back on the calendar, and a team that keeps finding the next one.
OUTCOME #2
An AI department, without the headcount
Phos AI Labs embeds with your team and keeps the whole thing pointed at growth: building, connecting, and improving across the operation.
OUTCOME #3
A system that gets better while it runs
Every deal closed and ticket resolved feeds back into the context layer. Month twelve runs on a system that knows more about your business than any single person in it.
Does the build actually deliver?
It shows up in four places: revenue that did not exist before, hours that came back, administration that disappeared, and doors that were closed until the architecture cleared review.
Payroll Mexico
Two AI employees: an SDR that prospects every morning, and an ops assistant with full context.
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
Read the full story → - ~$300K projected new ARR from outbound alone
- 7 hrs of daily overhead eliminated
LOW/CODE Agency
Five departments audited, then the two AI employees the findings pointed at, including one that read 2,400 dormant leads.
2,400 leads had been sitting in our pipeline for years. The AI read every thread, found 707 worth contacting, and had a personalized draft ready for each one. That pipeline existed in our data the whole time. We just never had the intelligence to see it.
Jesus Vargas, Founder and CEO, LOW/CODE Agency and Phos AI Labs
GAF
The largest roofing contractor training program in North America, rebuilt over three versions in eight months.
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
HRM Mexico
16 years of Mexico labor-law expertise, answering any employer at any hour.
The results from our increase in lead generation are outstanding. This is the perfect example of the importance of moving forward with an AI strategy. We feel very fortunate to have chosen Phos AI Labs as our partner. It has been a positive game changer for us.
Franklin Delano Frith II, Owner, HRM
Read the full story → - 250% more leads vs. the prior year
- 24/7 availability, every conversation captured
Why Phos AI Labs over an automation agency or an in-house build?
Most shops build what you asked for and invoice on delivery. Phos AI Labs decides what to build, builds it, measures whether it worked, fixes what did not, and brings the next idea to the table.
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We have actually done this
A new AI agency launches every week with a landing page and no case studies. Most have never worked with a company doing $5M+ in revenue or connected an agent to NetSuite, Salesforce, or SAP. We have, again and again.
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Context before agents
Two AI employees that know your business, your voice, your clients, and your history will outperform thirty that do not. Every single time. We build the context layer first, which is why our systems are still running in month twelve.
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The hire you can’t make
A senior AI lead costs $250,000+ and takes six months to find. Phos AI Labs embeds that capability the week you start, backed by a full team instead of one person.
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The model underneath can change
We build so that a price change, a rate limit, or a retired model at one provider is our problem to solve rather than yours. We hold Claude (Anthropic) Partner and Select OpenAI Partner credentials, and model choice is made per task on accuracy and cost.
How much does AI implementation cost?
AI implementation with Phos AI Labs typically runs $10,000 to $50,000 a month, depending on scope and how much of the operation we are running. The first systems go into the work costing your team the most, so the engagement shows a return before it shows a big invoice.
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Implementation engagement
from $10,000 /mo.Production agents and AI employees built into the workflows costing you the most, measured after launch and improved while they run.
Book a strategy call -
Embedded AI department
up to $50,000 /mo.Your full AI team on retainer: building, connecting, and improving across the whole operation, with the roadmap re-ranked every month.
Talk scope
The growth you are chasing is often already funded. A company doing $5M+ in revenue usually carries three or four six-figure SaaS contracts doing a fraction of what one connected system could. Part of the work is finding that spend and redirecting it toward the systems that compound.
Keep going
The products these engagements ship, and the tools that make the first conversation sharper.
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AI Employees
Agents that own a role and run your repeat work around the clock. The most common thing an implementation engagement ships.
Explore → - 02
Nexus
The private, company-owned AI workspace your whole team works inside.
Explore → - 03
AI Readiness Voice Audit
A three-minute voice-driven audit, the same interview method used in the full engagement, in miniature.
Explore → - 04
Case Studies
The full library of what Phos AI Labs has shipped, with the numbers behind each build.
Explore →
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FAQs
What is AI implementation?
AI implementation is the work of building AI systems into a company's real operations and running them there: agents, AI employees, and automated workflows connected to the tools the business already uses. At Phos AI Labs it starts with the context layer, then production builds, then ongoing measurement and improvement.
When is my company ready for AI implementation?
When your team still spends real hours every week on work that does not need their judgment. Writing the same reports, routing the same requests, pulling data from three systems into a spreadsheet. If you know which work that is, you are ready. If you do not, start with the audit.
What does an AI agent actually look like inside our operation?
It depends on the workflow. An agent might draft the proposals that used to take three days, surface numbers that used to arrive after the decision, or consolidate three systems into one morning briefing. Each one has a specific job and runs every day against a measurable outcome.
How much does AI implementation cost?
For companies under 100 employees, AI discovery starts at $10,000. Larger organizations get a scoped discovery priced to the number of teams and systems involved. After discovery, ongoing implementation typically runs $10,000 to $50,000 a month, depending on scope. We give you a specific number once we understand the operation.
Where does our data live, and who can see it?
Inside your environment. Agents and automations run in your own infrastructure with your access controls, and your security team is part of the setup before anything gets built. Consequential actions route through a human approval gate, so the AI prepares and a person decides.
Can we start with one or two automations?
That is exactly how it starts. We pick the two or three workflows costing your team the most and build there first. The engagement does not end at delivery; we stay embedded, measure results, and move into harder processes as the impact compounds.
How is this different from hiring an automation agency?
Phos AI Labs finds what to build, builds it, measures whether it is working, fixes what drifts, and keeps bringing new ideas. We operate as your embedded AI team, so month six is better than month one. The engagement is ongoing by design.
Do we own what you build?
Yes. The systems, the context layer, the runbooks, and the documentation all live inside your company and are yours to keep. There is no lock-in and no black box, and Phos AI Labs stays on to keep improving the systems as the business grows.