AI Consulting Services

You have already decided to hire someone for AI. This page is about what you would actually be buying, what it costs you in time, and which of these engagements you should skip. Facet Interactive is an operating partner rather than an advisory firm: we build and run the Agent Loops that do the work, and we are measured on what they produce rather than on what we recommended. The reason we can describe these engagements this concretely is that we bought every one of them from ourselves first — and ran them in the wrong order at least once, which is the part of that experience worth paying for.

We were our own first client, and the sequence is what cost us

Facet did not design an agentic operating model for clients and then adopt it. We adopted it because a boutique firm could not otherwise carry the delivery volume it had already sold. Our engineering, content, research, quality review and reporting run through Agent Loops today, under human command — we published the full account of how that works, including the parts that did not go well.

The part worth your attention is the mistake. We picked a second process badly — one that looked obvious on a process map and turned out to rest on a judgment call nobody had ever written down — and we paid for it in rework rather than out of a client's budget. That is where the ranking method further down this page came from, and it is why the first thing we sell is a sequence rather than a build.

Humans stay in command: Agents carry the reading, the mapping and the monitoring. A named person owns every decision that reaches a customer or a production system, and work an agent produced clears exactly the same review as work a person typed.
We name what goes wrong: Hallucination, scope creep, runaway token cost and lost context are real and predictable. Each one has a specific control, and each control exists because we hit the failure on our own operations first.
It changes the commercial terms, not just the pitch: The slow, expensive half of a conventional consulting engagement is discovery billed by the hour. We put agents on that pass and keep our people on the conversations that need a person, which is why scoping here is time-boxed and quoted as a fixed fee.

Why hire an AI consulting partner at all

Because the models are the easy part, and they improve on their own. What does not arrive on its own is a decision about which parts of your business should stop being done by a person — and somebody accountable for what happens after that decision.

A subscription has no owner: A seat licence makes one person faster at a task they already hold. It does not map your process, coordinate a hand-off, or notice when it has quietly stopped working. Somebody has to own that, and it is a role rather than a tool.
The gap is operational, not technical: The companies whose AI spend disappeared did not pick the wrong model. They ran pilots that nobody was responsible for carrying into production, and the accountability ended when the invoice did.
You are buying an outcome, not a headcount: An internal AI hire gets you one person's judgment plus the experimentation risk. An engagement here gets you a ranked plan tied to money and time, and a team that has run the pattern before on its own operations.
One process is enough to change your year: This is the part most owners underestimate. You do not need a transformation programme, a data science team or a budget cycle — one process taken all the way to running, this quarter, is what puts you ahead of companies several times your size. The advantage is in finishing the first one, and finishing it is a decision entirely inside your control.

What an AI consulting engagement actually runs on

"Consulting" here is not a discovery phase that ends in a document. Most of the volume in the opening weeks is carried by agents reading your systems, and the output is a ranked, costed plan rather than an assessment.

What the loop actually does

These four passes are high volume, mechanically checkable, and almost always fine — which is exactly the profile of work agents are good at, and exactly the work that makes a conventional discovery phase expensive.

Read what you already have, all of it: Systems, documentation, tickets, exports, the process notes somebody wrote two years ago. A person does this by sampling, because reading everything was never affordable. An agent reads everything, which changes what the first conversation is about.
Map the process as it actually runs: The steps, the systems, the hand-offs and — the part that matters — the exceptions. Reconstructing the real path from ticket and system history is volume work with a checkable answer at the end of it.
Produce the first ranking: Every candidate process scored on volume, how mechanically checkable the output is, and what a wrong result would cost. One pass produces a first version of the whole table, so your leadership team argues with something concrete instead of starting from a blank page.
Watch the loop once it runs: Volume, error rate, exception frequency and cost per result, checked continuously. This is the surveillance nobody staffs, because an inspection that turns something up once a quarter cannot justify the hour it costs every morning — and that arithmetic is what changes when the inspecting is no longer a person's hour.

What a person still decides

None of the above is judgment. These four are, and they belong to you — we argue for a position and you rule.

Which process goes first: The ranking narrows it to two or three. Choosing between them turns on which team can absorb the change this quarter and which one has a person who genuinely knows how the work happens. That is a read on your organization, not on the data.
What you are willing to let run unattended: Which outputs a loop may act on alone and which stop for a named signature. This is a business decision about risk, it is written down before anything runs, and it is the one we will not make for you.
Which relationship ends: Automating a process frequently makes a tool, a vendor or an outsourced function redundant. Somebody in your company chose that arrangement, defended it at the time, and may still be measured on it. Whether to end it is a question about your own people before it is a question about the contract.
Whether to buy anything at all: If the ranking comes back showing low volume and high judgment in every candidate, the honest recommendation is to do nothing yet, and we will write that down.

What you are actually buying

Five engagements, and almost nobody needs more than one this year. Each is scoped and priced on its own. The table is here so you can find yours quickly and ignore the other four.

If what you want is not "which one do I buy" but why agentic at all — the argument for decomposing a process rather than buying a tool, and what that changes — that case is made in full on our Agentic AI Consulting page, which is the hub this page sits under. Two useful companions while you decide: our buyer's guide on how to choose an AI consulting partner and a plainer explainer on what agentic AI is and why it matters.

Which of these five do you actually need — and which four can you skip? Book the AI Consulting Scope: two weeks, fixed fee, run against your real systems rather than a questionnaire. You leave with the process map, the ranked table, and a written recommendation naming one engagement — including "none of them this year, fix the process definition first" if that is what two weeks on your own operation shows.

Frequently asked questions

What are AI consulting services, exactly?

AI consulting services move a business from using AI tools to operating on AI systems, with an outside partner owning the ranking, the build, and usually the ongoing operation.

The deliverable is an operating model, not advice: The goal is not a report on your AI opportunities. It is a specific job in your business getting done differently, with a name against it.
It spans decide, build and run: Sequencing, hands-on building, and keeping the result accurate afterwards. Firms that sell only one of the three hand you off at exactly the points where value leaks out.
It starts from your processes, not a template: The work begins with how your business actually runs, including the exceptions, because that is where every estimate either holds or falls apart.
It answers to your existing numbers: Throughput, cycle time, cost per result, error rate. The same measures you would apply to any operational change.

How is this different from giving everyone ChatGPT or Copilot?

A tool makes one person faster at a task they already own. A loop takes the task. That difference decides whether the gain shows up in an operating number or disappears into people's calendars.

Twenty people saving twenty minutes is nearly invisible: It is real, and it will not appear in a business metric. One process running at several times the throughput appears immediately.
Integration is the valuable part: The model is commodity. Wiring it into your systems, bounding what it may touch, and making it reliable enough to depend on is engineering.
Governance is completely different: Individual tool use is hard to audit. A loop has written boundaries, logged actions, and a record of what was proposed, what passed, and what a human caught.

Do we need to be technical to hire an AI consulting partner?

No, and most of the companies we work with are not. The conversation starts with the work you want done, how long it takes today, and what it should cost.

The engineering is our job: Autonomy boundaries, integrations and guardrails are ours to design and run. You do not need an internal AI team to start.
If the missing piece is leadership rather than build capacity: That is the fractional Chief AI Officer seat, not this engagement — a named executive who owns the portfolio and the stopping decisions.
What we need from you is one person who knows the work: Two or three hours a week of somebody who understands how the process really happens, exceptions included. It is the one resource we cannot substitute for.

How fast do we see something real, and how do you prove it?

The first loop runs against live work inside weeks because we scope to one process and refuse to widen until it holds.

Scope, then shadow, then carry: The loop first runs alongside your people with its output compared rather than shipped. Once the comparison holds, it starts taking work off the queue with human review on the way out.
Measured against a baseline you set: We record the number before we start, so the after is an actual comparison rather than an assertion.
Widening follows evidence, not a ramp: Coverage expands when the numbers earn it, which also means your spend is never ahead of your proof.
What ages here is the ranking, and your own business sets how fast: The table is a claim about your operation as it stood the fortnight we read it, and operations move — a system gets replaced, one team absorbs another team's work, a product line doubles and drags its process up the order. It also gets better as it goes: the first loop returns real volumes and real exception rates in place of the figures we estimated, which reorders what comes second. Both of those are somebody's job to notice. A ranking nobody re-reads does not go blank — it keeps pointing, at last year's bottleneck.

Why Facet specifically?

Because we run the thing we are selling, and have since before it was a service line.

Operating partner, not agency: We build, run and improve the loops, and stay accountable for what they produce.
A real track record: Founded in 2014 and named a CIOReview Top 10 Digital Transformation Services Company in 2021.
Boutique with reach: Forty people across nine timezones and six continents, plus a 500+ specialist network when a workflow needs depth we do not carry in-house.
Fixed fee, quoted before we start: We do not bill hourly, because hourly billing pays us to be slow at exactly the work we claim to accelerate.

Most of this page is not for you

There are five engagements in the table above, and the useful work of an AI consulting partner is telling you which four to ignore. That is an unusual thing for a services page to argue, and it is the argument, because the most expensive mistake available to you here is not picking the wrong vendor — it is buying a portfolio when what you needed was one process finished.

So the deliverable we lead with is the subtraction. Two weeks on your real systems produces a ranked table of your processes and a recommendation naming one, and you keep both whether or not you ever hire us. If the table says the answer is a trigger-action tool and a cheaper afternoon, it will say so in writing.

Which of these five do you actually need? Book the AI Consulting Scope — two weeks, fixed fee, run against your live systems. You leave with the decomposed process map, the ranked table with cost and effort against each candidate, and one named recommendation with the reasoning behind it. One process, finished, is enough to put you ahead of this by next quarter — and you can start it this month. If you would rather talk it through first, reach us directly.