CoPilot CONSULTING SERVICES

Talk to your data with a Copilot Agent. Get back answers you can trust.

You built a semantic model in Power BI that governs how your data comes together. P3 Adaptive builds a Copilot agent on top of it, so anyone on your team can ask a question in plain language and get a accurate answer and visualizations back. Generate meaningful reporting and data deep dives from an intelligence layer you control. This is on-demand data analysis at scale.

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You built the model. AI helps scale it.

Your semantic model already governs how your data comes together and who can see it. Getting answers out of it still runs through reports, dashboards, and the people who build them. P3 Adaptive puts a Copilot agent on top of that semantic model so your team asks questions directly and gets governed, accurate answers and visualizations in seconds. No report request, no waiting, no bottleneck.

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Enterprise governance in Copilot

Any modern LLM chatbot can read a data export and answer a question, but it doesn’t know your definitions, your data relationships, or who’s allowed to see what. P3 builds the Copilot agent directly on your Power BI semantic model, where metrics are defined once, sources are already connected, and access rules already live. Everyone gets accuracy no matter how they ask or how they slice it. Your single source of truth, now available in plain language.

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Your data shouldn’t leave your environment

We build your agent on Copilot or Microsoft Foundry inside your tenant. The models it uses are accessed securely within Microsoft infrastructure, so nothing gets shipped out to LLM providers and nothing is held by a third party. That’s the difference between AI capability you can take to your leadership team and AI capability security stops before it ever launches.

Learn More About Microsoft + AI capabilities

AI doesn’t just level the playing field; it tilts it to your advantage.

The Big Firm Nightmare

Large consultancies love selling transformation and infrastructure-first roadmaps — while your real business questions go unanswered for years.

FORTUNATE 500 APPROACH

  • Infrastructure-first approach — years of data plumbing before delivering tangible business value
  • Endless data strategy assessments, without actual building
  • Bloated enterprise architecture review committees
  • Vendor selection committees with dozens of stakeholders
  • IT approval gates for every request
  • Change management for “user adoption”
  • Compliance reviews before giving the first round of feedback
  • Timeline: 1–3 years to see impact

Your “Lean” Advantage

You’re lean. Nimble. Your systems aren’t as complex. You can prioritize at speed, scaling exponentially.

MID-MARKET COMPANY APPROACH

  • Identify high-impact business use cases
  • Build a working prototype in two weeks
  • Test with real users and real data
  • Iterate based on actual feedback
  • Scale what works, kill what doesn’t
  • Timeline: 2 weeks to business impact
Let’s find your data advantage

Schedule your free, no B.S., 30-minute consultation today

You’re not behind.

Almost every business leader everywhere says, “What are we doing about AI?” without understanding how or where it can provide the most immediate business value. That’s why most enterprise AI transformation initiatives are not much more than expensive productions in business theater.

Our experts are really good at automating the tedious tasks that nobody likes (think “invoice processing” and “expense report reviews”), and refocusing those hours on initiatives that require human judgment and creativity.

Let’s find your business accelerator

YOUR AI REALITY AWAITS

Let’s build some AI muscle.

Virtual Analyst Team

Tireless, always-on, enterprise-grade analytical manpower, without the enterprise-level payroll.

Marketing Firepower

Creative talent trained on your brand, helping an overworked marketing department create and curate content for your business.

Organizational Intelligence

Visibility, tracking and monitoring of business-steering functions, consolidated and at your fingertips.

Optimized Workflows

Improve or create processes that automate the tedious, eliminate the bottlenecks and improve impact.

Improve Forecasts

Advanced forecasting models, predicting trends and business outcomes with greater precision, automatically.

Revenue Acceleration

Identify hidden revenue opportunities and accelerate sales cycles around the clock.

Sean Cuddy

Commodity Components

“What impressed me the most about your team was the depth of capability. I didn’t have to hop around to multiple vendors. You had the knowledge base and consultants to take us through the entire lifecycle of our project.”

Kailey Tachick

Executive VP of Finance, Systems & Culture, Dairyland Electrical Industries

“Partnering with P3 was one of those clarifying moments about what great partnerships could be. We found people with fantastic expertise who were a pleasure to work with and delivered really good work. It sounds basic, but that combination is actually challenging to find, and we really appreciate the partnership.”

Happiness.
Guaranteed.

Two weeks to business impact that generates excitement. Or walk away without cost. No risk. No B.S. Just happiness. Guaranteed.

Find Out How

WHY P3

We threw out the traditional consultancy playbook.

Most large consultancies overcomplicate problems, so it takes years to see any impact — if any at all. We don’t operate that way. With us, there’s no B.S. No infrastructure overhauls. No enterprise budgets. Just real results using your existing systems. Just speed and happiness. Guaranteed.

Let’s chat

Schedule your free, no B.S., 30-minute consultation today

P3’S PROMISE

What you can expect from us:

We meet you where you are

No overhaul required. We build on your existing systems, using the data you have readily accessible to make it faster, cleaner, and smarter.

We move fast

Two weeks to a working prototype. Not a slide deck. Not a roadmap. A real result you can see and use.

We make it stick

Your systems, your people, your success. When we’re done, you’re not dependent on us, you’re just better equipped to win.

Ready to win?

You already have the systems and data to take on your competitors.
Together, we can turn that into your unfair advantage.

Let’s go

Copilot AI FAQ

Frequently asked questions about Copilot AI consulting

It’s help designing and building Microsoft Copilot agents that work with the data and systems you already run. Most of our work starts where your governed data already lives, usually a Power BI semantic model, so the agent answers business questions using the same definitions your reports use.

We’re a consulting company, not a software vendor. Sometimes the answer is a Copilot agent. Sometimes it’s a stronger semantic model, a fixed process, or “don’t build that yet.” You’ll get told which one you’re looking at

Fair question, because Microsoft put the word Copilot on several different products and none of them do the same job.

• Microsoft 365 Copilot: the assistant inside Word, Excel, Outlook, and Teams. It works on your documents, files, and email.

• Copilot in Power BI: the built-in feature in the Power BI service that summarizes reports and helps write DAX.

• Copilot Studio: the tool for building custom agents with your own instructions, data, and actions.

• A custom Copilot agent: what we build for you, in Copilot Studio or Microsoft Foundry, pointed at your semantic model and your business logic.

The first two come with licensing you may already own. The last one is the thing that answers “why did margin fall last month,” then answers the four questions that come after it.

It’s an agent your team talks to in plain language, answering from your Power BI semantic model. Someone asks why margin fell last month, gets a number and a visual back, then asks the follow-up without filing a report request and waiting three days for it.

The agent isn’t reading a spreadsheet export and guessing. It’s using the relationships, measures, and access rules already defined in your model. That’s the difference between an answer and an answer that merely looks plausible.

Yes, once it’s connected to the right data with the right permissions. Sales, finance, operations, inventory, customers, supply chain, anything your model already covers.

The connection is the easy part. What matters is whether the agent knows what your company means by “revenue” or “active customer.” Those definitions live in your semantic model, which is why we start there instead of pointing a chatbot at a data export and hoping for the best.

Because your semantic model already holds the things an AI needs and can’t infer on its own: how tables relate, what each measure actually calculates, which hierarchies matter, and who’s allowed to see what.

Skip that layer and you’re asking a language model to rediscover your business from scratch every time somebody asks a question. It will produce something. It won’t reliably produce the same number your CFO uses, and one wrong number in a board meeting sets the whole effort back a year.

Whatever your data and business logic support. Revenue, margin, forecast variance, inventory position, customer concentration, operational throughput, financial performance.

The follow-up is where it earns its keep. A user starts with what happened, asks why, narrows to one region, compares two segments, and keeps going, all without requesting a new report for every turn. Most organizations don’t have a data access problem. They have an analysis latency problem, and the gap between question and answer is where decisions quietly get made on instinct instead.

Power BI agents are where we start most often, because that’s where governed data already lives. It isn’t the whole list.

• Virtual analyst capacity: the analysis nobody currently has hours for, running constantly.

• Marketing firepower: content work trained on your brand, for a marketing team that’s already stretched.

• Organizational intelligence: the numbers that steer the business, consolidated and monitored in one place.

• Optimized workflows: automating the tedious, invoice processing and expense report review being the usual suspects.

• Better forecasts: models that predict trends and outcomes with more precision than a spreadsheet extrapolation.

• Revenue acceleration: finding opportunities sitting in data nobody has time to query.

Same method either way. Pick a real problem, build against real data, put it in front of real users in about two weeks, and keep what works.

Wherever your people already work. Teams is the most common home, because nobody has to learn a new place to go. It can also live in the Power BI service, in a browser, or inside an application you already run.

That choice matters more than it sounds. An agent that requires a new tab and a new habit gets opened twice and then forgotten, and the project gets blamed on the AI.

A general-purpose chatbot handed a data export absolutely can. That’s the fear, and it’s a reasonable one.

An agent built on a governed semantic model works differently. It isn’t calculating margin itself, it’s asking your model for margin, and that calculation was defined once by somebody who knew what it meant. The language model runs the conversation. Your model runs the math.

Where things still go sideways is ambiguity. Ask a vague question and you can get a confident answer to a question you didn’t mean. A good share of what we build is the instruction and guardrail work that makes the agent ask what you meant rather than guess.

Yes, and they should. Answers come back with the measures, filters, and time period behind them, so somebody can check the work instead of taking it on faith.

Worth saying out loud: the first few weeks of any agent rollout involve people quietly verifying answers against a report they already trust. That’s healthy behavior, not a lack of confidence, and the rollout should be built to survive it.

Plenty, and you should have the list before you start rather than after.

• It can’t answer questions about data that isn’t in the model. No data, no answer, and no amount of prompting changes that.

• It can’t settle bad definitions. If two departments calculate revenue differently, the agent inherits the argument.

• It doesn’t replace an analyst on open-ended, judgment-heavy work. It clears the repetitive majority so your analysts get the hard part.

• It won’t rescue a model with broken relationships and undocumented measures. Those get fixed first, or the agent is confidently wrong at scale.

• It can’t read your mind. Vague question, vague answer.

Anyone who hands you a list of capabilities with no matching list of limits is selling, not consulting.

Almost never because of the AI. They fail because the data underneath wasn’t ready, or because the project started with “we need an AI strategy” instead of a question somebody actually needed answered.

The second one does the most damage. A twelve month program produces a steering committee, a maturity assessment, and a slide deck. A two week build produces something a user either reaches for on Monday or ignores, and you learn which one immediately, while the budget is still intact.

We build inside your Microsoft tenant, on Copilot or Microsoft Foundry. The models are reached through Microsoft infrastructure, so your data isn’t shipped out to an outside LLM provider and isn’t sitting with a third party.

That’s usually the difference between AI capability you can take to your leadership team and AI capability your security team stops before it ever launches.

The exact architecture depends on your data sources, your tenant configuration, and your compliance requirements. We’d rather scope that honestly than hand you a diagram before we have seen your environment.

No. Your prompts, your responses, and your business data aren’t used to train Microsoft’s foundation models, and they’re not shared with other customers.

If your legal team wants that in writing rather than from a consultant, it’s covered in Microsoft’s product terms and Copilot data protection documentation. We’ll point them straight at the relevant sections.

Yes. Row-level security defined in your semantic model applies to the agent exactly as it applies to a report. A regional manager who asks the agent for company-wide numbers gets their region, not the company.

This is the strongest practical argument for building on the model rather than an export. The moment data leaves the model, every access rule your team carefully wrote stops existing, and nobody notices until the wrong person screenshots the wrong number.

Less than they’re bracing for. Typically: confirm the tenant settings that allow Copilot, give us access to the relevant workspace and semantic model, and review the architecture with us before anything goes live.

We’re not asking for an infrastructure overhaul, a platform migration, or a new vendor in the stack. If a project genuinely requires your team to rebuild something first, you’ll hear that in week one instead of discovering it in month six.

It depends on scope, and you get a number before you commit to anything. What you won’t get from us is a multi-year program quoted against a question you needed answered this quarter.

Most engagements start the same way: a small, fixed first phase aimed at a working result in about two weeks. You see something real before you decide how far to take it, which is a much better basis for a budget conversation than a proposal.

It depends on which Copilot is involved, and this is where a lot of confusion lives.

• Copilot in Power BI and Fabric runs on Fabric capacity. Copilot capacity now starts at F2, a much lower entry point than the F64 requirement people remember from launch.

• Copilot Studio agents are billed on message consumption, either through a prepaid capacity pack or pay as you go against Azure.

• Microsoft 365 Copilot is a separate per user seat and isn’t required for a Power BI agent.

Most clients already own more of this than they realize. Part of scoping is telling you what you actually need, which is sometimes less than the quote sitting on your desk.

Mostly consumption. Copilot usage bills against your Fabric capacity or your Copilot Studio message allocation, so the running number moves with how much your people use it.

That’s a good problem to have. Heavy usage means people trust the answers. We’ll show you how to watch the meter and where to tune it, so the renewal conversation holds no surprises.

Three things. A business question worth answering, data that already lives somewhere governed, and one person who genuinely cares whether this works.

That third one isn’t a throwaway. Projects with a named owner who wants the answer get shipped. Projects sponsored by a committee produce meetings about the project.

You don’t need an AI roadmap, a platform rebuild, a center of excellence, or a company-wide strategy before you begin.

Then we tell you. Sometimes the honest answer is that an agent isn’t the next thing you should build, and a stronger semantic model is.

More often the data is better than you fear and worse than it should be, which is normal and fixable. We evaluate what you have, identify the specific gaps that matter for conversational use, undefined measures, missing relationships, columns named in a way only three people understand, and fix those rather than starting over.

Nobody needs a perfect data estate to get value out of this. They need one solid model covering one real question.

Usually, and that’s the fastest path there is. A strong model means most of the hard work is already behind you.

We evaluate it first, because conversational use puts pressure on things reports never did. Measures that were never named clearly, relationships that only behave if you know which filter to apply, logic that lives in a report instead of the model. We tighten those, then build on top.

Not necessarily. If you already run Fabric, we build on that investment. If you don’t, we’ll tell you honestly whether it adds anything to your specific case instead of treating it as a prerequisite.

Plenty of useful agents run on capacity a company already owns. Requiring a platform purchase before anyone has seen a working result is how these projects stall.

No, and you shouldn’t want to. Reports are excellent at the job they were built for, which is monitoring something you already know you need to watch.

The agent covers the other case, the one nobody built a report for because nobody knew the question was coming. Margin drops, and the next four questions each depend on the answer to the one before it. Waiting three days per turn is how an investigation dies.

Keep the reports. Add the conversation.

That’s the right question, and it’s the one most AI projects skip entirely. Adoption comes down to two things: does the agent live where people already work, and did it answer the first question correctly.

Get both right and it spreads on its own, usually starting with one finance analyst who tells three other people. Get either wrong and no training program rescues it.

Training itself is minimal. If somebody can type a question in Teams, they can use this. What helps far more than a training session is a short list of good starter questions per team, because people freeze at an empty prompt box and unfreeze the moment they see five examples.

Sometimes you can, and if your team has the capacity and a clean model, we’ll say so rather than talk you out of it.

What trips teams up isn’t the tool. It’s getting the semantic model ready for conversational use, writing instructions that keep the agent honest when a question is ambiguous, passing a security review, and knowing which of the twenty available ways to wire this up doesn’t quietly fall over in month three.

Standing up your first agent is a weekend. Building one your CFO trusts is a different project.

Exactly what it sounds like. We aim at a working result you can see and use in about two weeks. If it doesn’t generate real excitement, you walk away and you don’t pay.

We can offer that because we’re not selling a discovery phase. We build against your real data from the first week, so there’s something concrete to judge quickly. Either it’s useful or it isn’t, and both sides find out fast instead of eighteen months in.

We can offer that because we’re not selling a discovery phase. We build against your real data from the first week, so there’s something concrete to judge quickly. Either it’s useful or it isn’t, and both sides find out fast instead of eighteen months in.

You decide. Expand it, scale it to more teams, or stop where it is.

Whatever gets built is yours. Your tenant, your model, your agent. We document it and hand it over, including the parts that would be inconvenient for us if you took them elsewhere. If you want us to keep going, we’re glad to, but the goal is that you’re better equipped, not dependent.

Probably the opposite. Lean companies are the best fit for this work, because you can decide something on Tuesday and have it running by the end of the month.

The large enterprise version of this project involves an architecture review board, a vendor selection committee, and a change management workstream. Yours involves a question and a person who wants it answered. Treat that as the advantage it is.

Manufacturing, distribution, finance and accounting teams, professional services, and a long tail of businesses that don’t fit a neat category. The two companies quoted on this page come from electrical manufacturing and industrial components.

Industry matters less here than people expect. What matters is whether you have data in a governed model and a question that currently takes too long to answer.

We came to AI from the data side. More than a decade building Power BI, Fabric, Azure, and analytics work that people actually use, which turns out to be the part that decides whether enterprise AI works at all.

Most AI consultancies are strong on models and vague about the data underneath. The data underneath is the whole game. An agent inherits every definition, relationship, and access rule in the model you give it, and it inherits the missing ones too.

The other difference is what we’ll tell you not to build. We’re a consulting company, not a vendor with a product to move. If AI isn’t the answer to your problem, you’ll hear that from us before you spend anything.

Bring a question, not a strategy. The most useful first conversation is somebody describing a thing that takes far too long to find out.

From there we look at your Power BI environment, the state of the semantic model, your security requirements, and where an agent gets you a real answer fastest. Then we build, and you get something to react to in about two weeks.

Ask the awkward follow-up question. Change direction. Get the visual. Keep going.