How our AI consulting and implementation engagements work

We learn your business, find where the time and money go, and build what fixes it.

Most companies don't have an AI problem. They have workflows nobody has questioned in years, knowledge stuck in people's heads, and teams using tools that know nothing about the business.

Phos AI Labs is an Embedded AI Consulting and Implementation Firm. We go inside your operation, work out what should change, then build the foundation, train your people and implement the systems that make it happen.

Three phases. First results within weeks. A full transformation takes 9 to 18 months, and when we leave, your team knows how to run what we built.

Thirty minutes, no deck

A company buys ChatGPT licences for everyone. Six months later, the CEO uses it daily, a handful of employees use it occasionally, and nobody can say what the investment changed. Another company spends $40,000 automating a process it should have eliminated altogether.

Both problems start in the same place: someone picked the tool before understanding the work. So our engagements follow a sequence. First we find where the business loses time and money, then we document what AI needs to know, and only then do we train the team and build the systems.

What happens when a stage is skipped
What gets skipped	What happens
The audit	You automate what's easiest to demo instead of what's worth fixing.
The foundation	Every employee explains the company to AI from scratch. Outputs are generic and confidence drops.
Training	The system gets built, and the people meant to use it keep working the old way.
A shared workspace	Everyone builds their own prompts and workarounds. Nothing compounds across the company.

We move to the next stage when the previous one is ready. If the foundation isn't there, we won't pretend it is.

Phase 01 has two stages that run in sequence. The audit identifies where AI creates value. The foundation documents what AI needs to know before anything gets built.

i. AI Readiness Audit

Two weeks for most businesses. Three to six weeks for larger, multi-department audits.

Every business has a version of the same problem. Sales rebuilds proposals from scratch, operations copies numbers between systems, managers chase updates that should already exist, and the CEO approves decisions others should be able to make. Some of those problems are expensive; others are just annoying. Our job is to tell the difference.

We map how your business actually works, not how the process manual describes it, but how work gets done on a Tuesday when three people are out and a client needs an answer.

An AI agent interviews people across departments, usually for about 40 minutes each, while our consultants run their own conversations to find the handoffs, exceptions and workarounds a questionnaire misses.

We also review the tools you pay for, the AI people already use, the decisions that keep getting escalated and the recurring work filling the team's week. Then we put numbers against what we find.

What you walk away with:

AI Readiness Audit deliverables
Deliverable	What it tells you
AI Readiness Scorecard	Where each department stands on tools, workflows, data and AI capability.
Pain Points Heatmap	What slows the business down, how often, and what it costs.
AI Opportunity Register	Which processes are worth improving, ranked by potential savings, value and confidence.
Prioritized Recommendations	What to tackle first, what can wait, and what isn't worth building.
Scoped Next Step	What your AI Foundation needs, what the work involves, and what it will cost.

The result: your highest-value opportunities are identified, costed and ranked. If you decide not to continue with Phos AI Labs, the findings are still yours.

Explore the AI Readiness Audit

ii. AI Foundation

About four weeks, starting once the audit is signed off.

Ask five employees how your business handles a difficult client and you'll likely get five answers. Some of that knowledge lives in old documents, some in Slack threads, and the important parts often live in the head of someone who's been there eight years. An AI system can't make reliable decisions from that, so we write it down.

Using what we learned in the audit, we build your Company Brain: the business-specific knowledge, rules and workflows your AI systems work from.

What goes into your Company Brain:

What goes into the Company Brain
Document	What it contains
Company context	What you do, who you serve, how you position yourself, and the details AI needs to understand the business.
Voice guide	How your business communicates, what good writing looks like, and what should never reach a client.
Decision rules	How recurring situations are handled, what AI can do alone, and what needs human approval.
Customer archetypes	Who your customers are, what matters to them, and how they decide.
Workflow maps	The actual steps behind the recurring work the business depends on.

These documents are built to be used, not filed. Load the right context into an AI tool and a proposal starts from your services and standards instead of generic advice. A support reply follows your policies. A new hire finds the reasoning behind a decision without asking the same three people.

The Company Brain is also what everything later builds on. Automations need defined processes, agents need rules, and teams need a common standard.

The result: your business has a documented operating context that people and AI can both use.

Explore AI Foundation

Knowing what to build is one thing. Getting a business to actually use it is another. Phase 02 makes sure your team is ready and confident before the technical implementation begins.

Phase 02 has three stages.

i. Team Training

Typically 2 to 3 months, starting once the Company Brain is in use.

This is not a workshop. You've probably seen the usual AI training: someone demos twenty impressive prompts, everyone leaves excited, and on Monday the team goes back to the old way.

We train on real work instead. We sit with the account manager writing proposals, the operations lead compiling Friday's report, the support team working tickets. We watch the work, build a better way to do it, and teach the person responsible to repeat it.

Each role gets its own playbook, the prompts that actually worked, and clear rules on what AI should and shouldn't handle. Then we check whether it stuck. A successful session isn't one everyone enjoyed; it's one where the same employee is still using the workflow weeks later, without us beside them.

What stays with your team:

Role-specific AI playbooks
Reusable prompts and workflows
Clear rules for AI use and human review
Adoption data showing where training stuck and where it didn't

Explore Team Training

ii. Claude Training for Businesses

For businesses deploying Claude as their primary AI platform.

We train your team to get the most out of Claude across daily work. This covers how Claude handles context, how to write prompts that produce consistent outputs, how to use Projects and system instructions, and how to build role-specific workflows inside Claude that the whole team can follow.

Your team leaves with a Claude playbook built around your business, your documents, and your standards, not generic best practices.

This stage is available as part of a full engagement or as a standalone training for teams already using Claude who want to get more consistent results.

Explore Claude Training

iii. ChatGPT Training for Businesses

For businesses deploying ChatGPT as their primary AI platform.

We train your team to use ChatGPT effectively across the work that matters most. This covers Custom GPTs, memory, the right model for each task, how to write prompts that produce reliable outputs, and how to build workflows the whole team can use without starting from scratch every time.

Your team leaves with a ChatGPT playbook built around your business context, your tone, and your recurring work.

This stage is available as part of a full engagement or as a standalone training for teams already using ChatGPT who need more consistent, business-specific results.

Phase 03 is where strategy becomes infrastructure. The team is trained, the Company Brain is in place, and now we build the systems that run on top of that foundation.

This phase typically runs 5 to 14 months across four stages that go deeper as the business is ready.

i. Company Brain

Your company's knowledge, documented and accessible to every AI system you run.

The Company Brain is the foundation every agent, automation and AI workflow draws from. Without it, each system has to be fed context from scratch, outputs are inconsistent, and nothing compounds across the business.

Built during the AI Foundation stage, the Company Brain is expanded and maintained here as implementation deepens. As new departments, workflows and services come online, their context gets added so every system works from the same source of truth.

What it contains: company context, voice guide, decision rules, customer archetypes, workflow maps, onboarding documentation, and the operating knowledge that usually lives in people's heads.

Explore Company Brain

ii. AI Operating System

The layer that connects your agents, automations and workflows into one operating system for the business.

Once individual workflows are running, the AI Operating System ties them together. Your CRM, inbox, project management tools, ERP and other systems connect through defined handoffs, so data moves without anyone retyping it and each agent works with full context.

Built on top of the Company Brain, the AI Operating System routes work to the right tool or agent, keeps a person in the loop where approval is needed, and gives leadership visibility into what is running, what it costs and what it saves.

What it handles: workflow orchestration, system integrations, approval routing, adoption reporting, and the monthly review of what to automate next.

Explore AI Operating System

iii. Private AI Workspace

One company-owned AI environment, grounded in your data and context.

Once people know how to use AI, scattered personal accounts become the next problem. Sales has its prompts, operations has another set, and the best version of the business context sits in one person's chat. When that person leaves, the knowledge leaves too.

The Private AI Workspace moves everything into a shared environment your business owns. Your Company Brain, approved knowledge, reusable AI skills and project resources are available to the people who need them, under the right access controls.

Leadership can see which workflows are used, which teams have stopped, and where outputs are accepted versus rewritten by hand. That data tells us what to improve next.

What changes: shared business knowledge instead of personal prompts, consistent outputs across teams, resources that don't leave with an employee, and visibility into adoption and output quality.

Explore Private AI Workspace

iv. AI Automation

Automations that still run six months later.

AI automation runs across Phase 03, connecting agents and workflows to the systems your business already uses. We automate the repetitive steps that don't need a person: reading documents, updating records, routing approvals, drafting replies, qualifying leads, preparing reports.

Most automations break because someone picked the tool before sorting the process. We sort first. Every step lands in one of four places: leave it alone, a plain rule, an AI step inside a workflow, or a full agent. Rules are cheaper and more reliable; AI goes where the rule runs out.

We build in n8n, Make, Zapier, or custom code depending on the job. Everything is built in your accounts, under your API keys. If you stop working with us, everything keeps running.

Automation specializations:

AI Sales Automation: Lead research, qualification, CRM updates, proposals and follow-up built around how your team sells.
AI Marketing Automation: Research, content, campaigns and reporting connected so qualified demand reaches sales with its context.

Explore AI Automation

How long does an AI transformation take?

The first useful output shouldn't take 18 months. The full change might.

A full Phos AI Labs engagement runs 9 to 18 months, but you aren't waiting until the end to see whether it works. Each stage starts when the one before it is ready, so the schedule follows your business rather than a fixed calendar.

How long does an AI transformation take?
Stage	Typical duration	Starts when	What should be different
01i. AI Readiness Audit	2 weeks (3–6 for larger audits)	Day one	Problems identified, costed and ranked.
01ii. AI Foundation	About 4 weeks	Audit signed off	Company Brain ready to use.
02i. Team Training	2–3 months	Company Brain in use	Employees using AI in real workflows.
02ii–iii. Platform Training	Concurrent with team training	As relevant	Team fluent on chosen platform.
03i–ii. Company Brain and AI OS	3–4 months	Adoption measurable	Shared context, connected systems, adoption visible.
03iii. Private AI Workspace	Concurrent with above	Adoption measurable	Shared environment live, personal accounts retired.
03iv. AI Automation	Through end of engagement	From month 6–7 onward	Recurring work automated, measured against baseline.

Indicative durations, not fixed delivery promises. Stages can overlap, and audit size, implementation complexity and your starting point all affect the schedule.

Sometimes we can, when the business is ready and the resources are available. More work in a shorter window also changes the cost. But some of this shouldn't be rushed:

The foundation has to be right. Build on incomplete knowledge and every system downstream inherits the gaps.
Habits take time to change. One good demo doesn't mean someone will use the workflow on their own next month.
Adoption data needs a real business cycle. A few good days don't prove a workflow works.
Operational change needs trust. Before agents take on recurring work, we need to know how the system behaves, where it fails, and who owns the exceptions.

The point isn't to make the engagement last 18 months. It's to make the improvements last after it ends.

Discovery: from $10K under 100 employees
Ongoing work: $10K–$50K a month

Discovery starts from $10K for companies under 100 employees and is scoped individually above that. After discovery, ongoing work runs $10K to $50K a month. Where you land depends on four things:

Business complexity. One department with a documented process is a different job from six departments on different systems and approval chains.
How much gets implemented. A shared workspace and a handful of workflows is a smaller scope than redesigning how sales, operations and finance work together.
Business lines involved. Each service line, customer segment or operating model brings its own context, rules and workflows.
Speed. A shorter timeline can mean more people working inside your business at once.

What you're paying for

An embedded Phos AI Labs team that learns your operation, recommends what to change, builds the agreed systems, trains the people who use them and measures the results. You won't get a strategy deck at the end of the month, a project manager relaying requests to a team you've never met, or an hourly invoice every time you need a call.

What isn't included

Third-party software licences and platform subscriptions are separate unless your proposal says otherwise. If we recommend a third-party tool, you hold that contract directly.

At the end of each phase we review what was delivered, what we learned and what the next stage needs. If the original scope no longer makes sense, we'll say so.

Let's talk scope

How we handle your data

You're giving us access to how your business runs. Here's how we treat that access.

How we handle your data
Question	Our answer
Where do audit interviews go?	Any data stored is kept in the US with row-level security enabled. With most AI providers we use Zero Data Retention, meaning no data is stored at the provider level after a request is processed. Only your engagement team has access.
Which AI models and platforms do you use?	We work across all major models, including OpenAI, Anthropic Claude, and Google Gemini, among others. We use Zero Data Retention (ZDR) wherever the provider offers it, meaning your data is not stored by the model provider after a request is processed.
Is our data used to train AI models?	No. We only use platforms whose business terms exclude customer data from model training, with ZDR enabled wherever the provider offers it.
Who owns the Company Brain?	You do. It is your documentation, written for your business, stored in your accounts.
Who can see what?	Access in the Private AI Workspace is set by role. You decide who sees which knowledge, workflows and outputs.
What agreements are in place?	An NDA is not required but is available if you want one before booking a call. Data handling, confidentiality and ownership terms are covered in your engagement agreement.
What happens to our data when the engagement ends?	Nothing needs to be revoked or deleted. The entire engagement is built inside your own accounts, under your API keys and credentials. You own and control everything from day one.

Should you hire an AI lead or work with Phos AI Labs?

Both can work. The question is what your business needs right now.

An internal hire gives you a permanent owner, and for businesses with an established AI function and a clear plan, that can be the right move. But one person is often expected to be strategist, trainer, AI architect, engineer and operations lead at once.

Should you hire an AI lead or work with Phos AI Labs?
	Hire an AI lead internally	Work with Phos AI Labs
Investment	Salary, benefits, recruiting costs, and supporting tools	Discovery from $10K, then $10K–$50K a month
Time to start	Weeks to months of recruiting and onboarding before any work begins	Discovery starts immediately with your existing business and team
Expertise	One person's experience and certifications	A team of Claude Certified Architects, AI engineers, AI consultants, and automation specialists working together
Breadth	Strong in one area; strategy, engineering, training, and automation rarely sit in one hire	Strategy, enablement, AI agent development, workflow automation, and implementation under one engagement
Partner access	No direct model partner relationships	Anthropic Claude Partner Network member and OpenAI Select Partner, with direct access to both ecosystems
Accountability	Your internal reporting line	Deliverables and scope reviewed with leadership at the end of every phase
Long-term ownership	Stays in-house if the knowledge is documented	Documented in your Company Brain, with an internal owner trained to run it

The two aren't mutually exclusive. We can build the AI function, then train the person who will own it, so you get outside experience without making consultants a permanent bottleneck.

AI Consulting Firms vs In-House AI Teams: Pros and Cons

Someone using AI fifty times a day tells us very little. What matters is whether the work got faster or better, whether less time goes on tasks that shouldn't need a person, and whether the team gained capacity without adding headcount.

So we measure before we build. During the audit we record how much time a workflow takes, how often it runs, what that time costs and how confident we are it can improve. Every opportunity gets a value estimate before it becomes a recommendation.

What the numbers look like

Illustrative capacity value of two workflows
Workflow	Potential time recovered	Estimated annual capacity value
Weekly operations report	3 hours/week × 50 weeks	$11,250 at $75/hour
Proposal drafting	4 hours/proposal × 30 proposals	$12,000 at $100/hour

Illustrative examples, not Phos AI Labs client results. Recovered time is capacity; it becomes a cash saving only when the business realizes it.

After implementation, we track the real workflow. Did the three hours disappear from the weekly report, or is someone still spending two correcting the output? Does it work the same way across teams? If the result isn't there, we find out why.

What we measure

Time recovered: manual work the team no longer does.
Cost and capacity: what that's worth against real operating costs.
Adoption: whether people keep using the system after training ends.
Output quality: whether work meets the standard without constant correction.
Business impact: improvements in delivery, service, revenue opportunities and operations.

Some value is harder to reduce to one number, like better proposals, faster responses, or a CEO approving fewer routine decisions. We keep those visible without calling them guaranteed revenue. The aim is that leadership can see where AI is working, where it isn't, and what deserves the next dollar.

Our clients are typically companies with $5M or more in annual revenue, with no upper limit, across industries including marketing, manufacturing, finance & insurance, logistics & transportation, construction & real estate, legal, professional services, healthcare, hospitality & travel, retail, staffing & HR, energy & utilities.

We're probably a good fit if:

Your team already uses AI, but inconsistently, and nobody can show what it has changed.
Leadership will sponsor the work and give us access to the people who do it.
You want your team to own the result, not depend on us forever.

We're probably not the right fit if you want:

A single chatbot or tool built to a fixed spec.
A strategy deck with no implementation.

What an engagement looked like: LOW/CODE Agency

LOW/CODE Agency, a global software agency, went through the same process we run for clients.

LOW/CODE Agency

STARTING POINT

Every proposal needed the CEO’s involvement, and preparing for a client call took a full day of pulling information from four or five systems.

WHAT THE AUDIT FOUND

Interviews across five departments surfaced 77 pain points, 29 opportunities, 99.7 hours a week of recoverable time and $376,800 in estimated annual value.

WHAT WE BUILT

Two AI employees. A Sales AI Employee drafts proposals from past winning work, and an internal Chief of Staff builds a briefing before every client call from Gmail, Slack, tl;dv and project tools.

WHAT CHANGED

Routine proposals go out without the CEO, call prep is automatic, and 707 qualified leads were recovered from 2,400 dormant ones, each with a personalized re-engagement draft. Audit to AI Implementation took two months.

An astronaut in a Phos AI Labs suit reading a printed report with a chart

Now the AI handles the routine ones. I review the complex cases. That’s the job I should have had all along.

— Jesus Vargas, Founder & CEO, LOW/CODE Agency and Phos AI Labs

Read the full case study →

A consultancy that leaves you with a presentation has finished a project. One that leaves you with a system nobody can maintain has created a dependency. We aim for neither: the engagement should leave you with the knowledge, systems and people to keep the work moving.

The knowledge stays. Your Company Brain (company context, voice guide, customer archetypes, decision rules, workflow maps) plus onboarding guidance, so new hires don't have to piece processes together from three managers.
The systems keep running. Your team has the documentation, access and understanding to run what we implemented: the knowledge base, shared AI skills, connected workflows and adoption reporting. Phos AI Labs also offers separate products (Nexus, AI Employees, Sales Assistant). Where they or third-party software are part of the solution, ownership, licensing, data access and fees are agreed separately and made clear before handover.
Your people know what to do. Trained employees use the workflows without waiting on us, and we prepare an internal AI system owner who knows how the Company Brain is maintained, how to add workflows, how to spot weak outputs and when to escalate. When a workflow starts producing bad results, the first move shouldn't be to call a consultant.
The improvement continues. Each month, the internal owner reviews adoption and performance data, investigates underperforming workflows (outdated context, a weak prompt, a changed process), fixes what the team can fix and updates the shared resources.

If the business changes

You may open an office, add a service line, acquire a company, or finally automate the workflow everyone postponed. A new scope starts from what you've already built, not from another discovery exercise.

Where Phos AI Labs can help when the business changes
What changes	Where Phos AI Labs can help
New departments or locations	Extend the Company Brain, permissions and workflows to new teams.
A new market or service	Assess the new requirements and build the context they need.
More complex automation	Expand implementation using the adoption and performance data you already have.
Your internal owner leaves	Train a replacement and transfer the operational knowledge.

What success looks like

By the end of a full transformation, employees use AI where it genuinely helps, recurring reporting and admin no longer eat the same hours, and new hires learn AI workflows from company documentation.

Internal owners maintain and improve the system, leadership can see what's working, and the CEO is no longer the person every AI decision runs through. AI stops being something a few people experiment with and becomes part of how the company works.

Questions about working with Phos AI Labs

How long does a Phos AI Labs AI consulting engagement take?

A full engagement runs 9 to 18 months. The audit takes two weeks, the AI Foundation four weeks, then training and implementation follow. Focused single-department engagements are shorter.

What is the difference between AI consulting and AI implementation?

AI Consulting identifies where AI creates value and builds the strategy. AI Implementation builds the agents, automations and systems. Phos AI Labs handles both under one engagement.

How much does AI consulting and implementation cost with Phos AI Labs?

Discovery starts from $10K for businesses under 100 employees. Ongoing work runs $10K to $50K a month depending on scope, complexity and speed. Third-party software licenses are separate.

Is our data safe, and is it used to train AI models?

Data is stored in the US with row-level security. We use Zero Data Retention vendors where possible. Your data is never used to train AI models.

Which AI platform does Phos AI Labs recommend for businesses?

We recommend based on your workflows and existing tech stack. Options include ChatGPT for Teams, Claude for Teams, Google Gemini, Microsoft Copilot, or a custom solution. You hold all contracts directly.

Do we have to start with the audit?

Most companies do. If you've already done serious discovery and built a usable AI foundation, Phos AI Labs reviews what you have before recommending a next step. We won't sell you work you've already done well.

Can we start AI implementation in one department first?

Yes. A focused scope lets you test the approach, set a baseline and see results before expanding company-wide.

Our team already uses ChatGPT or Claude. Do we still need AI consulting?

Individual use and company-wide adoption are different problems. We assess whether usage is consistent, connected to your business data and producing measurable results, then close the gaps.

How soon will we see results from AI consulting with Phos AI Labs?

The audit delivers actionable findings within weeks. Early workflow improvements follow once the Company Brain is in use. Full operational impact depends on scope and adoption.

Do we keep the AI Readiness Audit findings if we do not continue?

Yes. The scorecard, pain points analysis and recommendations are yours whether or not you continue with Phos AI Labs.

Do we have to buy Phos AI Labs software products?

No. Nexus, AI Employees and Sales Assistant are separate offerings. We recommend them where they fit but they are never a requirement.

What happens to our AI systems after the engagement ends?

Everything is built in your accounts. We prepare an internal owner, document all workflows in your Company Brain and set up a monthly performance review before we leave.

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