Copilot Studio consulting services
Senior US-based engineers scope, build, and hand over a custom agent in your own tenant: a use case narrow enough to succeed, grounding in the SharePoint libraries and Dataverse tables that actually hold the answers, connectors and actions where the agent needs to do something, a written test set it has to pass, and a credit forecast agreed before anyone buys capacity. One written price, typical delivery in 2–4 weeks with 30 days of hypercare.
Representative dashboard — sample data
What you get
Standing an agent up takes an afternoon. Making one people keep using takes decisions about scope, grounding, permissions, actions, promotion, and cost — and those are the parts a demo never shows.
We start by cutting the idea down: one audience, one repeated question set, source material that already exists and is already correct. Breadth is what makes a first agent unusable, and it is the cheapest thing to fix before any building starts.
Knowledge sources are attached deliberately — named SharePoint libraries, Dataverse tables, approved files — rather than pointed at the whole tenant. Precision beats recall when the agent has to be right in front of a customer or an auditor.
Generative answers cover most questions. Scripted topics go in where the answer must be exact, a disclaimer is mandatory, or an escalation has to happen — and nowhere else, because every branch is something somebody maintains later.
When the agent has to look up an order, file a ticket, or start a flow, the connection is scoped to the minimum it needs, writes are confirmed before they happen, and credentials live where your admins can see and revoke them.
The agent is built inside a solution that promotes from a development environment to test and then production. Changing it six months from now is a reviewed deployment rather than somebody editing the live version and hoping.
Consumption is metered per interaction, and answer types differ by an order of magnitude. We model expected volume by type during scoping, so capacity is sized against a number you can argue with instead of an invoice you can't.
How it works
A free consultation covers who will ask, what they ask, where the correct answers live today, and what the agent must refuse. You get a written scope with the knowledge sources, the actions in range, a credit forecast, one fixed price, and a delivery date.
The agent is created in a development environment, grounded in the agreed sources, given only the topics and actions it needs, and run against a written question set — including the awkward phrasings and the questions it should decline. Results are recorded and the content behind wrong answers is fixed.
The solution is promoted to production and published to one channel first. Analytics, escalation handling, and admin settings are configured, and you get documentation, a walkthrough for the people who will own it, and 30 days of hypercare.
Microsoft Copilot Studio is the low-code environment for building custom AI agents on the Power Platform: you describe the agent's job, attach knowledge sources it may reason over, add scripted topics and connector-backed actions, and publish it to Teams, a website, or another channel. Microsoft's Copilot Studio documentation (opens in new tab) covers the maker portal itself. What it cannot cover is the judgement — which use case, which sources, which actions, how much of it your content estate can currently support.
That judgement is the engagement. Deliverables are the agent, the knowledge configuration behind it, the topics and actions it uses, the connections and their permissions, the test set with recorded results, the environments and solution it moves through, a written credit forecast, and documentation of its known limits. Work is performed remotely by senior US-based engineers from our Orange County, CA base, serving every US state, at a fixed fee agreed after your free consultation.
These two are constantly confused, and buying the wrong one wastes a quarter. Microsoft 365 Copilot (opens in new tab) is a per-seat assistant that reasons over each licensed person's own mail, files, chats, and meetings — Microsoft lists it at $30 per user per month. Deploying it well is a tenant readiness, licensing, oversharing, and adoption problem, and that is a separate service: our Microsoft 365 Copilot rollout consulting page covers assessment, Purview remediation, pilot cohorts, and staged expansion.
A Copilot Studio agent is not per-seat and not personal. It is one thing you build once that answers a whole team's repeated questions, or takes a repetitive action, from sources you nominate — and it is billed on consumption rather than seats. Licensing an entire department so each person can ask about the PTO policy is a bad trade; building one agent over the HR library is a good one. If the seat-versus-agent argument is live on your team, whether Microsoft Copilot is worth the cost works through both sides with the numbers.
And if you would rather build it yourself, we would rather you did — our step-by-step guide to building your first Copilot Studio agent is the same method we follow, written out. This page is for teams who have read that, decided it is not where their engineering time should go, and want it delivered instead.
Grounding is the decision that determines whether an agent is trusted or quietly abandoned. Each source type answers a different kind of question and carries a different precondition, and the table below is the pattern library we scope from — your written scope is a filled-in version of it.
| Where the answers live | How the agent reaches them | What has to be true first |
|---|---|---|
| Policy and procedure documents in SharePoint | Named sites or specific libraries attached as knowledge, answered within the signed-in user's permissions | One current version of each document, and sharing on those libraries reviewed rather than assumed |
| Structured business records in Dataverse | Tables attached as knowledge, or read through an action when a specific record is requested | Tables with meaningful column names and security roles that already reflect who may see what |
| A line-of-business system with an API | A connector-backed action the agent calls, returning a live lookup instead of a stored answer | An account scoped to the minimum required access, with credentials your admins control |
| Reference material on your public website | Approved public URLs added as a knowledge source | The published content is accurate and maintained — the agent inherits whatever is stale |
| Files with no system of record yet | Uploaded directly to the agent as a stopgap while the content finds a proper home | An owner named for each file, because uploads age silently and nobody notices |
| Broad Microsoft 365 content across the tenant | Tenant graph grounding, used deliberately for questions that genuinely span the estate | Governance work done first, and a cost model — this is the most expensive answer type per interaction |
The recurring finding is that grounding exposes content problems rather than creating them. An agent that confidently quotes a superseded policy is repeating what your library says. Where the estate needs work before it can be grounded on, our SharePoint intranet and document management consulting covers the libraries and metadata, and Copilot data governance essentials explains the permission and labeling checks that come first. Where the answers are structured records rather than documents, the knowledge source is a set of Dataverse tables, and designing those properly — typed columns, relationships, global choices, security roles — is our Microsoft Dataverse consulting services engagement. Microsoft's guidance on Purview sensitivity labels (opens in new tab) is the mechanism most of that remediation uses.
An agent that only answers is a search box with better manners. The time savings arrive when it can act: create the ticket, look up the order, start the onboarding flow, write the row. That is also the point where it stops being a content project and starts touching business systems, so actions get scoped tightly — least-privilege connections, explicit confirmation before anything writes, and an audit trail your admins can read.
It is worth being honest about when an agent is the wrong tool. If the requirement is a fixed sequence of approvals with reminders and an audit trail, a flow does it more cheaply and more predictably than conversation ever will — that is Power Automate consulting. If the requirement is structured data capture with validation, a form-based app beats a chat interface; that is Power Apps consulting. We will say so during scoping rather than after invoicing, and agents frequently end up calling those flows and apps rather than replacing them.
Most agents we build are published into Microsoft Teams, where the questions were already being asked. Publishing is its own small piece of work: the channel connection, the admin approval path, the app setup policy that pins it, and the documented limit that an agent in a channel cannot ground on sources needing each user's own sign-in. Where that packaging is the substantial part of the job, or where the agent sits beside an app and an approval card in the same channel, it is scoped as our Microsoft Teams app development services.
Most agents that fail in year two were built directly in production by someone who has since left. The fix is ordinary application lifecycle management, applied to agents like any other Power Platform asset: the agent lives in a solution, the solution is authored in a development environment, and it is promoted to test and then production as a deployable artifact. Microsoft's Power Platform ALM guidance (opens in new tab) sets out the environment strategy and solution mechanics we follow.
Practically this means a change to the agent's instructions or a new knowledge source can be tested against the same question set before users meet it, connections are re-pointed per environment rather than hardcoded, and an agent can be rolled back. It also means handover is real: you own the environments, the solution, and the connection references, and the promotion steps are documented for whoever maintains it after we are gone.
Copilot Studio is metered, not per-seat, and this is where budgets go wrong. Microsoft's published list price for prepaid capacity is $200 per pack per month for 25,000 credits. The trap is that interaction types are not equivalent: a scripted topic answer, an answer generated from an attached knowledge source, an answer grounded across tenant-wide Microsoft 365 content, and an autonomous action each consume very different amounts, and the expensive ones are the easiest to enable without noticing.
So the forecast comes before the purchase. We estimate conversations per month for the pilot audience, split them across answer types, and show the arithmetic — including what changes if adoption doubles or if tenant grounding gets switched on. Prices and consumption rates are Microsoft's and can change, so validate the current figures against your own tenant's billing before committing; the point of the model is that you can re-run it, not that our number is permanent. For how our own fee is set and what is fixed before work starts, our fixed-fee process spells it out.
It fits organizations with one clearly repeated question set and documented answers behind it — an HR team retyping the same policy replies, a service desk answering the same twenty how-tos, a sales team hunting product specifications across libraries. It is scoped for small and mid-size organizations: one agent, one written price, a stated end date, not an open-ended AI programme.
It is a poor fit where the answers do not exist yet, where "mostly right" is a compliance event, or where the real requirement is a workflow rather than a conversation. In those cases we will say so in the consultation and point at the cheaper tool. If your organization is at the earlier question of whether to deploy Copilot at all, start with the Copilot rollout checklist.
The table shows scope and typical timeline rather than prices, because an honest number requires seeing your use case, your sources, and the state of the content behind them first.
| Phase | Scope | Typical timeline | Fee |
|---|---|---|---|
| Phase 1 — scope and ground | Use case narrowed and written down, knowledge sources agreed and checked for permission problems, development environment and solution created, the agent stood up and answering from real content | About the first week | Fixed fee — scoped after your free consultation |
| Phase 2 — topics, actions, and testing | Scripted topics where a guaranteed answer or escalation is required, least-privilege connections and confirmed write actions, refusal behaviour defined, and a written question set run end to end with results recorded and content fixed | Weeks 2–3 | Fixed fee — scoped after your free consultation |
| Phase 3 — publish and hand over | Promotion from development to test to production, publishing to the first channel, analytics and admin settings configured, credit forecast reconciled against pilot usage, documentation and a walkthrough for the owning team | Week 4 and close-out | Fixed fee — scoped after your free consultation |
Everything produced is yours from the first day: the agent, the solution, the environments, and the connections all live in your own Microsoft tenant, never in ours. Microsoft bills the Copilot Studio consumption directly to you, separately from our fixed fee.
Related services
Tenant readiness, governance remediation, and staged Microsoft 365 Copilot licensing — the rollout, not the custom agent.
Libraries, metadata, and permissions that make your content safe and precise enough to ground an agent on.
Dataverse tables and custom apps that hold the structured business data an agent's actions read and write.
The step-by-step method, written out, for teams who would rather do the build in-house.
The permission, labeling, and oversharing checks that decide what an agent is allowed to repeat.
Per-seat licences against consumption-billed agents, and how to tell which one your team needs.
FAQ
A working agent in your own tenant: one scoped use case, knowledge sources connected to the libraries or tables that hold the answers, the topics and actions it needs, a tested question set with recorded results, a publish target, and written documentation of what the agent can and cannot answer.
A rollout licenses people so Copilot works across their own mail, files, and meetings. This builds a custom agent that answers one team's repeated questions from sources you choose. Different work and a different budget line — many organizations do the rollout first and add agents afterwards.
Microsoft lists prepaid capacity at $200 per pack per month for 25,000 credits, and different interaction types consume very different amounts. We forecast expected volume by answer type during scoping, so the number of packs you buy is a calculation you can check rather than a surprise later.
An agent answers within the signed-in user's existing permissions, so an overshared library becomes an overshared agent. We review the source sites before grounding on them and point the agent at curated libraries, which means a permissions problem gets fixed rather than automated.
The agent ships inside a solution that moves between development, test, and production environments, so edits are tested before users meet them. You own the environments, the solution, and the connections, and we hand over the promotion steps with 30 days of hypercare.
Typical delivery is two to four weeks from kickoff, with the date stated in your written scope. The fixed fee reflects how many knowledge sources are connected, how many actions write into other systems, and how much source content has to be cleaned first. Nothing starts before you approve it.
Book a free consultation
Tell us what you’re trying to fix — a report, an approval process, an intranet, a Copilot rollout. We scope it as a fixed-fee project, you approve, and a senior engineer delivers in 2–4 weeks.
The fastest way to reach us is the form — tell us the task and we’ll reply within one business day.