How to choose an AI agents services company in 2026

Choosing an AI agents services company in 2026 is harder than it should be. The category is two years old as a real services category — it inherited buyers conditioned by SaaS pricing and consultancy procurement, and it inherited vendors whose backgrounds range from ex-McKinsey to ex-Zapier to "I read three papers and built a wrapper." The result is a market where the price for the same outcome can vary 8× and the quality variance is larger still.

This guide is written for the buyer who has decided, broadly, that managed AI agents are worth investigating — usually a founder, COO, or head of operations at a 5–60 person B2B company. It is the resource we wish existed when we started Logitelia. It does not tell you who to hire. It tells you how to evaluate, what to ask, what to refuse, and when to walk away. By the end you should be able to interview four providers and rank them confidently.

1. Why this decision matters now: the 2026 landscape of managed AI services

The AI agents services market is in the awkward adolescence between novelty and infrastructure. Two years ago, asking "should I hire an AI agents company?" was equivalent to asking "should I hire a consultant to run experiments?" Today it is closer to asking "should I outsource accounts payable?" — a real operational decision with real budget, vendors, contracts, and switching costs.

Three things have changed in the last twelve months that make this a higher-stakes decision than buyers expect.

First, the base models are now reliable enough that the gap between vendors is not "who has access to GPT-5." The gap is in evaluation discipline, observability, prompt-as-code hygiene, and operator quality. Two vendors using the same Claude or GPT model can deliver outcomes that differ by a factor of three. That gap shows up in week six, not in the sales demo.

Second, switching cost is real and growing. Once an agent is in production touching your CRM, your invoicing system, your support tooling — you have integrations, you have a workflow your team has adapted around, you have months of run history. Ripping that out is a 6–10 week project even when the vendor cooperates on exit, and most contracts give the vendor the upper hand on what you walk away with.

Third, the buying mistakes from the 2017–2019 RPA wave are repeating. Buyers automate the wrong things, sign 24-month enterprise deals for tools they will outgrow in six, and treat the vendor as the owner of an outcome the vendor cannot own without internal sponsorship. We unpack the RPA comparison in AI agents vs RPA; the short version is that AI agents have wider applicability but exactly the same governance failure modes if the internal owner does not exist.

The direct cost of a bad AI services engagement for a mid-sized company is roughly the following: €30,000–€80,000 in vendor fees over six months, 8–14 weeks of internal team time directing the build, integration work that has to be redone when the vendor changes, and an opportunity cost of the workflow staying broken while you wait for a fix that does not come. Net damage from a bad 9-month relationship: €100k–€200k all-in, plus the second-order cost of the internal team losing faith in AI projects generally. That number is why this decision deserves a structured process.

2. The 4 types of AI agent services companies

The market sorts into four archetypes. They are not equally good or bad — they serve different stages and budgets. Knowing which one you are evaluating saves you a lot of mismatched expectations and protects you from comparing apples and oranges in a procurement spreadsheet.

Boutique AI-native firms

5–30 people, founded between 2023 and 2025, AI agents are the entire business. Often a mix of senior engineers, ex-product people, and operators with a domain background. They run productized services on internal platforms they built themselves. Pricing tends to be €2,000–€8,000/month per workflow, flat. Most have one or two domain specialisations (revenue operations, finance operations, content production).

Strengths: senior people doing the work, opinions about what to build and what not to, fast iteration cycles, the platform compounds across clients so improvements ship to everyone. Weaknesses: limited capacity (often a waitlist), narrower vertical coverage, the company is young so 5-year case studies do not exist. Logitelia sits in this category; so do a handful of other firms in the EU and North America. The good ones are easy to identify — they answer technical questions specifically and they will run a paid pilot on your data without flinching.

Big consulting AI offshoots

Deloitte, Accenture, BCG, EY have all stood up "AI agents" or "agentic AI" practices in the last 18 months. Independent global consultancies (Slalom, Publicis Sapient, Capgemini) have followed. They will take your business if it is large enough — usually €250,000+ project minimum — and they will assign a team. Partners sit on the pitch and the steering committee. Day-to-day delivery is by senior associates and managers, often capable, often new to AI specifically.

Strengths: change management, executive air cover, integration with your existing enterprise stack, willingness to deal with regulated environments and complex procurement. Weaknesses: cost (typical engagement: €300k–€2M), pace (a workflow that a boutique ships in 8 weeks may take them 6 months), build-and-handoff model where you inherit something only their consultants understand, partner-to-doer ratio that means the senior person on the pitch is not the one in the code.

Freelancer plus AI tools

One to three people, typically an ex-developer or an ex-marketer who has gone deep on tools like n8n, Make, Zapier, Bubble, Retool, Lindy, Relevance AI. They wire together pre-built components to deliver workflows that look like agents from the outside. Pricing is €1,000–€3,500/month or project fees of €5k–€25k. Often very effective on narrow, well-defined automations.

Strengths: cheap, fast, pragmatic, will say yes to almost any scope. Weaknesses: key-person risk (the whole engagement lives in one person's head), no SLA, the tools they assemble each have their own pricing that you eventually inherit, limited ability to do evaluation, observability, or anything that looks like real engineering. Fine for a single low-stakes workflow; risky for anything load-bearing. We dig into when this fits in AI agents services vs no-code automation.

SaaS plus services hybrids

A product company that also sells managed services on top of its own platform. Examples: support-AI vendors that operate the AI for you, sales-AI vendors that also run the SDR motion. The product is the moat; the services arm is how they get to revenue while the self-serve adoption curve catches up.

Strengths: deep product expertise, mature platform, often the most polished tooling. Weaknesses: the services team is the cost centre, not the product team, so quality varies; lock-in is structural because the workflow only exists inside their platform; pricing tends to climb as you scale. Often the right pick when the SaaS platform alone solves 70% of your problem and you want help with the last 30%.

None of these is universally better. A 200-person company replacing back-office headcount in a regulated industry is better served by a consulting offshoot. A 12-person SaaS replacing one BDR is better served by a boutique. A 6-person agency wiring together a content-production pipeline is better served by a senior freelancer. Mismatching archetype to need is the single most common procurement mistake we see.

3. What a real "managed AI agent service" actually delivers (not just ChatGPT wrappers)

The single most useful filter when evaluating vendors is to distinguish "managed AI agent service" from "we glued GPT-5 to an API and charged you for it." The two look identical in a demo. They diverge sharply at week four.

A real managed AI service delivers six things that a ChatGPT wrapper does not.

An evaluated workflow, not a prompt. The vendor has built an evaluation suite — a set of test cases that the agent must pass before any change ships to production. When the model changes, when a prompt changes, when a tool changes, the eval runs. You should be able to see the eval pass rate over time. We cover this discipline in AI agent evaluations explained.

Observability into every agent run. Every action the agent took, every tool it called, every model output, with a timestamp and a cost line. You can replay any run end-to-end. Without this, you are flying blind, and so is the vendor.

Human-in-the-loop on real-world side effects. The agent drafts the email; a human approves before it sends. The agent codes the invoice to a GL account; a human approves before posting. The risk-tier model — what runs autonomously, what is queued for approval, what is escalated — is explicit and tunable, not buried in code.

Integrations that survive maintenance. Real connectors to your CRM, your help desk, your accounting system, your warehouse — with credential rotation, error handling, retry logic, and version pinning. Not a Zapier scenario that breaks the first time the upstream API changes.

A senior operator on your account. A named human who reviews the agent's activity at least weekly, owns exceptions, handles strategy, and is accountable for the outcome. Not "the team will get back to you" — a named person with a calendar.

A path to your data, your IP, your continuity. Logs exportable, prompts exportable, workflow graph exportable, evals exportable. The deliverable is a thing you could rebuild without them in 30 days. If you could not, you are renting the workflow.

Vendors who skip three or more of these are selling a wrapper. They can still produce value in narrow cases; you just should not pay services prices for it. For a deeper look at the full deliverable, see What does an AI agents services company actually deliver?.

4. Pricing models compared (subscription, outcome-based, project, hybrid)

The four pricing models you will encounter, ranked roughly by how aligned they are with your interests.

Productized subscription (flat monthly per workflow)

You pay a fixed fee per month per workflow — typically €2,000–€8,000 for B2B back-office work, more for revenue-side work that has a direct line to cash. Includes platform, agent runs, model costs, operator time, weekly review. Best when your volume is predictable and you want a clean P&L line. Worst when you genuinely need a one-off custom build the vendor cannot productize.

This is the model the boutique AI-native category has converged on, and it is the model that is most aligned with the buyer. The vendor's incentive is to keep the workflow running well so you do not churn; their margin comes from running it efficiently, not from billing more hours.

Project (build-and-handoff or build-and-support)

One-time fee to scope, build, and ship a workflow. €15,000–€80,000 is the common range for a single workflow at a boutique; €100k–€500k+ at a consultancy. Often paired with a smaller monthly support retainer (€1,500–€5,000) after go-live. Best when the workflow is genuinely one-of-a-kind and you have the internal capability to run it after handoff. Worst when neither of those is true — which is most of the time.

The structural problem with pure project pricing is that the vendor is paid for delivery, not for outcome. The fastest way to finish a project is to ship something that passes acceptance criteria and then never speak to it again. A small support retainer barely changes that incentive.

Outcome-based / pay-for-results

The vendor takes a share of incremental value — a bounty per qualified lead, a percentage of collected invoices, a fee per resolved ticket above a baseline. Sounds beautiful, breaks in practice. Attribution disputes are constant ("was that lead the agent's or the form's?"), the vendor cherry-picks easy clients, and you end up renegotiating every quarter when the baseline shifts.

Reasonable in two narrow cases: a clearly instrumented lead-gen funnel with no other touchpoints, or a collections workflow where the baseline is genuinely measurable. Pair with a floor (minimum monthly fee so the vendor does not abandon you in slow quarters) and a ceiling (cap so they do not optimise for the wrong outcome). For most buyers this is a trap rather than a deal.

Hybrid (subscription plus small variable)

Flat base fee plus a small performance kicker tied to a single measurable outcome. Predictable cost for you, predictable income for the vendor, small upside tied to something you both agree on. Becoming the dominant model among the better boutiques.

The expansion of this model is covered in AI agents services pricing models compared and in our existing Managed AI agents pricing guide.

5. The operator question: human-in-the-loop vs fully autonomous

The most consequential design decision in any AI agents engagement is where the human sits. Vendors disagree about this loudly. The disagreement is partly philosophical, partly marketing positioning, and partly an honest reflection of where their tech is. You should have an opinion before the first sales call.

The autonomy spectrum runs from "agent suggests, human does" to "agent does, human reviews after the fact." Most production workflows in 2026 sit somewhere in the middle: the agent does the work and queues an action that requires a human approval before it touches the outside world. The approval is fast — often a single click — and a senior operator handles maybe 50–200 per day per workflow. The reason is not that the models cannot draft the action; the reason is that the cost of a bad action is asymmetric to the cost of a one-second review.

Three tiers, with rough rules for which goes where in 2026.

Tier 1 — Fully autonomous. Reasonable for low-stakes, reversible, high-volume work: categorising tickets, extracting fields from documents, deduplicating CRM records, first-pass content drafts that a human will edit anyway, search-term mining. The cost of any single error is small and the volume justifies removing the human from the loop. Run with monitoring and sample-based audit, not unsupervised.

Tier 2 — Human-in-the-loop. The default for almost everything customer-facing or money-moving: outbound emails, invoice payments, refund decisions, support replies above a complexity threshold, contract redlines. Agent drafts, human approves. Throughput is still 3–10× a fully manual workflow because the human only has to approve, not generate.

Tier 3 — Human-on-the-loop with escalation. The agent acts autonomously but flags anything outside its policy for synchronous human review. Reasonable for known-bounded workflows (e.g. PPC bid adjustments inside a written guardrail, lead scoring, calendar coordination) where the cost of waiting for approval is higher than the cost of an occasional bad action that can be corrected.

Vendors who pitch full autonomy across all three tiers in 2026 are either selling vapor or testing on you. The honest framing — "we run tier 1 autonomously, tier 2 with approval, tier 3 with policy and escalation" — is a strong signal of operational maturity. Ask explicitly: which actions does the agent take without a human? You want a specific answer, not a posture.

6. 15 questions to ask before signing

These are the questions whose answers actually predict whether the engagement will work. Anything else is colour. We expand each in 20 questions to ask before hiring an AI agents services company.

1. Show me one of your current agents running on a slice of our real data — not your demo data. Any vendor who refuses or stalls is signalling that their agent does not generalise. A paid pilot of 1–3 weeks is reasonable. A 12-week "discovery" before any working artefact is not.
2. Who specifically will be the operator on our account, what is their seniority, and how many other accounts do they hold? The operator is the human accountable for your workflow. Cap on accounts per operator: 4–8 for boutiques, 8–15 for hybrids. Anything above that is a thin layer of attention.
3. What is your evaluation methodology? Show me an evaluation report for a workflow similar to ours. A real answer involves test sets, pass rates over time, regression handling. A vague answer about "we test things before we ship" means there is no real eval.
4. Walk me through observability. Can I see every action the agent took yesterday with timestamps and cost? If the answer involves "we can pull a report," the observability is bolted on. If you get a portal link with a live activity feed, the observability is real.
5. Which actions does the agent take without a human in the loop, and where exactly is the approval gate? You want a specific list. Vagueness here is the loudest possible red flag — they have not thought about it.
6. What happens to the workflow, prompts, evals, and integration code if we leave in 30 days? You should receive an export, in human-readable format, that lets you rebuild on another vendor or in-house. If you cannot, you are leasing your own process.
7. Where is our data stored, where is it processed by the model, and which sub-processors do you use? A vendor selling to EU buyers in 2026 should answer this in 60 seconds with named regions and named sub-processors. Hesitation here is disqualifying. See AI agents data residency in the EU.
8. What is your cadence — how often does the operator actually touch the account in a normal week? Acceptable: daily review of agent activity, weekly written summary, biweekly tuning session, monthly business review. Lower cadence means the workflow drifts and you find out late.
9. Show me three customer references in our size range and vertical with whom you have worked for at least 6 months, including the operator assigned to each. Operator continuity is the leading indicator of vendor health.
10. What does week 1, week 2, week 4 look like? A vendor who has shipped this before has a written plan. A vendor who is still inventing the playbook will improvise on your time.
11. What KPIs will we agree on in writing, and how do they map to a business outcome the CEO cares about? If the KPI is "agent uptime" or "tickets processed," the vendor is optimising for activity. If it is "first-response SLA met," "DSO reduced by N days," "qualified opportunities created" — the vendor is optimising for outcome.
12. What is your minimum contract length and your termination clause? Are they symmetric? 3-month initial term is fair. 30-day rolling after that, both directions. Any longer fixed term should come with a meaningful build component, not just lock-in.
13. Who owns the IP in prompts, workflow graphs, evals, and integration code you build for us? You, for the work product. They, for the underlying platform. If they will not assign IP in the work product, walk.
14. What is your policy when a model provider deprecates or significantly changes a model? A serious vendor has a migration plan, regression evals, and a written notification cadence. A serious answer mentions specific historical events (e.g. how they handled the GPT-4 sunset, the Claude 3 → 3.5 migration).
15. What would make you fire us as a client? Reveals the vendor's backbone. The best ones name specifics: "the scope keeps shifting weekly," "the internal owner is absent," "your team will not provide the eval ground truth." A vendor who has never fired a client will not push back when you are wrong.

7. Red flags that should kill the deal

Some signals are strong enough that you should walk away regardless of how good the rest looks. These are the ten that consistently predict a bad engagement, expanded in 12 red flags when evaluating an AI agents services company.

Demo on their data, refuses paid pilot on yours. The single loudest signal. The agent that works on the demo workflow may not survive contact with the real one.

Cannot name a single specific failure mode of their agent. Every production agent has failure modes. Vendors who claim theirs does not have not run it long enough to find out.

"Fully autonomous, no human needed" pitch in 2026. Either marketing fluff or a vendor that is about to ship a damaging action on your behalf.

Insists on owning the prompts, workflow, or evaluation suite you paid them to build. The 2026 equivalent of a Google Ads agency owning your account.

No named operator, "the team will handle it." Means a junior, and they will rotate off in three months without notice.

Pricing hidden until a sales call. If the model is reasonable, they will publish a range. Hiding it almost always means they price-discriminate based on how desperate you sound.

Cannot answer EU data residency questions in plain language. Either they are not ready for EU clients, or they are about to wing it on your contract.

White-label or subcontracts without disclosure. "Is anyone outside your company touching our workflow?" If they hedge, walk.

Contract length over 12 months on a fixed term with no clear build deliverable. Pure lock-in. The vendor's incentive shifts from delivery to retention the moment the contract closes.

Sales process led by a closer who cannot answer technical questions. If the AE defers every specific question to "the engineering team," you are about to sign a contract with people you have never met.

8. Contract clauses that protect you (IP, data, exit, performance)

The vendor's standard contract is written to protect the vendor. That is normal and fine — you just need to negotiate in the clauses that protect you. None of these are unreasonable; most credible vendors will accept them if you ask plainly.

IP assignment on work product. "Vendor irrevocably assigns to Client all right, title and interest in prompts, system messages, workflow graphs, evaluation suites, integration code, and any documentation created specifically for Client." Vendor retains rights to its underlying platform and generic tooling. Non-negotiable.

Data ownership and processing. "Client retains sole ownership of all input data, output data, and derived artefacts. Vendor processes data only as instructed and only for the purpose of providing the service." Add: no training of public models on your data, no sharing across other clients, deletion within 30 days of termination.

EU data residency. If you are in or sell to the EU, specify regions in writing. "Data shall be stored and processed within the European Economic Area. Vendor shall disclose any sub-processor outside the EEA and obtain Client's written consent before routing data through such sub-processor." Pair with a signed DPA. See AI agents and GDPR compliance.

Exit and portability. "Within 10 business days of termination, Vendor shall export and deliver: (a) all prompts and system messages, (b) workflow definitions in machine-readable format, (c) evaluation suites and ground truth, (d) integration code and credentials documentation, (e) all logs and historical agent runs, (f) any documentation created during the engagement." Specify format.

Notice period. 30 days written notice after the initial term, both directions. Some vendors try to make termination asymmetric (30 days for them, 90 for you). Make it symmetric.

No automatic renewal without notice. Renewal should require active confirmation, or at minimum a 60-day notice window in which you can cancel without penalty.

Performance review trigger. A clause that allows you to call a written performance review at month 4 with documented underperformance against agreed KPIs. Trigger 30-day cure, then termination without penalty if not cured.

Model and sub-processor change disclosure. "Vendor shall notify Client in writing at least 14 days before changing the underlying model provider, hosting region, or any named sub-processor that touches Client data." Closes the silent migration loophole.

Security and incident response. SOC 2 Type II or equivalent at maturity; for younger vendors, a written security policy plus annual penetration test plus a defined breach notification window (72 hours is standard). See AI agents security checklist.

Liability cap. Vendors will push for 1× annual fees as the cap. Reasonable for low-risk workflows; push to 2–3× for anything money-moving. Carve out gross negligence, IP infringement, and confidentiality breach from the cap.

Audit rights. The right to commission a third-party audit of the workflow, evals, and security posture once per year at your expense. Rarely used, very useful to have.

9. Onboarding: what week 1, week 2, week 4 should look like

The first month of a new AI services engagement tells you almost everything about whether the next 12 months will be good or bad. Watch for these specific events.

Week 1 — kickoff and access. The vendor comes to kickoff with a written onboarding plan and a workflow map, not "let's see what you need." Access requests are sent the same day for every system the agent will touch: CRM, help desk, accounting, document store, email. Sandbox accounts where appropriate. Security and DPA paperwork is in motion. By Friday of week 1, the vendor has documented your current process as they understand it and asked you to correct it — this is the artefact the agent will be evaluated against.

Week 2 — first working version on real data, in a sandbox. Not in production. The agent processes a sample of your real data (anonymised if needed), produces outputs, and you review the failures together. The vendor's ability to discuss the failures specifically — what went wrong, why, what they will change — is the single most useful signal you will get. If week 2 is still slides and "we are setting up infrastructure," the vendor is not ready.

Week 3 — evaluation suite and observability dashboard. The eval suite is built, ideally from real historical examples with known correct answers. The observability dashboard is shared with you — you can see every agent action without asking. The risk-tier policy is documented: what runs autonomously, what queues for approval, what escalates.

Week 4 — first production runs with human-in-the-loop. Tier 2 workflow goes live with approval gates. Volume starts small (10–20% of real volume) so the operator can catch issues before they compound. By the end of week 4 you have a written week-5-through-8 plan showing how volume ramps, what changes when, and what KPIs are measured.

If week 1 looks instead like a kickoff call, two weeks of silence, and a vague promise to share something next week, that is the cadence you are buying for the next year.

10. KPIs that actually matter for AI services engagements

The metrics a vendor leads with reveal what they think their job is. If they open the monthly review with "100,000 tokens processed" and "97% uptime," they think their job is "look busy." If they open with "47 hours of operator time saved" and "DSO down by 4 days," they think their job is "make the business healthier."

The hierarchy of metrics that matter, top down.

Business outcomes. The metric the workflow exists to move. Pipeline-qualified opportunities for an SDR agent. Days sales outstanding for an invoice-collection agent. First-response time and CSAT for a support triage agent. Articles published per month at acceptable quality for a content agent. If the vendor cannot tie their activity to one of these, the activity is detached from the business.

Efficiency outcomes. Operator hours saved per week, cost per processed transaction, throughput. These are the numbers that translate the workflow into euros. Often the easiest case for the CFO to follow.

Quality outcomes. Eval pass rate over time, human-edit rate (how often the operator changes the agent's draft before approving), escalation rate, false-positive and false-negative rates against ground truth. These are the numbers that tell you whether the agent is getting better or quietly degrading.

Activity metrics. Runs executed, tokens consumed, models used, integrations called. Useful for cost tracking and capacity planning. Useless as primary KPIs. If a monthly review leads with these, push back.

The framework for tying all of this back to spend is in AI agents ROI calculation. The short version: a healthy managed AI engagement returns 3–6× annualised on fees within the first year, and the path to that ROI should be visible in the monthly review within 90 days.

11. When NOT to hire a managed AI services company (be honest)

The hardest section of any buyer's guide is the section that talks the reader out of buying. We will be specific because the converse — buying when you should not have — is more expensive than not buying.

Do not hire if the process you want to automate is not documented and not done consistently by humans today. An AI agent automates a process; if the process exists only in three people's heads and they each do it differently, the agent will codify the worst version. Fix the process first, then bring the agent.

Do not hire if the volume is below roughly 50 transactions per month for the workflow in question. Below that, the cost of build and operate exceeds the cost of a human doing it manually, and the agent's evaluation set is too small to be statistically meaningful. The exception is if the work is high-skill-low-frequency and the bottleneck is the humans' availability, not their hours.

Do not hire if you do not have an internal owner with 2–4 hours per week to partner with the vendor. The vendor cannot own an outcome inside your business without an internal sponsor who removes blockers, opens accesses, decides edge cases, and translates results to leadership. Engagements without an internal owner fail regardless of vendor quality. This is the failure mode that wrecked half of the 2018 RPA wave.

Do not hire if you are buying because the board pressed you to "do something about AI." That budget will go to whichever vendor pitches best, the project will produce a demo not an outcome, and the next year's board conversation will be about why AI did not work. Better to wait six months for a real operational pain point.

Do not hire if the workflow is highly regulated to a point that every step needs a human signature anyway. The agent will help on the margins but the savings will be small relative to the build cost. Worth revisiting in 18–24 months as model auditability improves.

Do not hire if you have not first looked at whether a SaaS tool already solves the problem. Many "we need a custom AI agent" projects could be a €99/month subscription. The full version of this argument is in When NOT to hire an AI agents services company.

12. The Logitelia angle: how productized AI teams differ

We run Logitelia in the boutique AI-native category. Three things shape how we deliver and they are worth knowing because they describe the trade-offs we have chosen, not because they are universally right.

First, we are a services company, not a product company. We do not sell software. We sell a workflow that runs and a team that runs it. Our platform exists to make that team faster, not to be sold separately. The implication for buyers: you get our operators and our evaluation discipline; you do not get a self-serve dashboard you can take elsewhere. If you want a tool, several SaaS vendors are a better fit.

Second, our pricing is flat per workflow, EU-hosted by default, with full export on exit. We made these choices because they are what we would want as buyers. The implication: we are sometimes more expensive than a freelancer-plus-tools shop on day one and almost always cheaper than a consulting offshoot on month twelve. We are not the cheapest option in any category; we are designed to be the option you regret least at month nine.

Third, we are deliberately narrow on what we deploy. We run revenue-side workflows (research, prospecting, content, growth ops) and finance-side workflows (invoice collection, expense triage, basic reporting). We do not build custom agents in domains we have not shipped before, because the eval discipline that makes a workflow trustworthy does not transfer cleanly across domains and we will not pretend otherwise. The implication: we will tell you "this is not us" more often than most vendors, and we will point you at someone better.

If you want to dig into the model under the hood: What is an AI-native services company covers the operating model, and Build vs managed AI agents covers the build-vs-buy framing for the workflows we run.

13. Final decision checklist

You have interviewed three or four providers. You have asked the 15 questions. You have looked for the 10 red flags. To pick the winner, score each candidate against this checklist. Tie-breakers go to the vendor that gave the most useful answers in interview, not the most polished.

• Named operator with relevant domain experience and an account load under the cap for their archetype.
• Paid pilot completed on your real data, with documented failure modes and a tuning plan.
• Evaluation methodology shown specifically, not described abstractly. Sample eval report shared.
• Observability is real: live activity feed, replayable runs, cost line per run.
• Risk-tier policy is explicit: which actions run autonomously, which are gated, which escalate.
• Pricing model is flat per workflow or hybrid, not pure outcome-based or pure project for an ongoing service.
• Initial term is 3 months, then 30-day rolling, both directions symmetric.
• IP assignment on work product is in the contract. Exit export clause is specific about format and timeline.
• EU data residency confirmed. Sub-processors named. DPA signed.
• Week 1 onboarding plan shared in writing. First working version on real data committed to by week 2.
• KPIs include at least one business outcome the CEO can name, not only activity metrics.
• References in your size range and vertical contacted, said good things, and used the same operator you are being assigned.
• The vendor disagreed with at least one thing you said during the sales process.

If you cannot tick at least eleven of these thirteen, you have not finished interviewing.

14. Frequently asked questions

Where Logitelia fits

Logitelia's managed AI teams run revenue-side and finance-side workflows as a productized service: flat monthly fee per workflow, named senior operator, EU data residency, full export on exit, evaluation suites you can see. We are one credible option in the boutique AI-native category — there are others — and we encourage you to interview at least one consulting offshoot and one freelancer-plus-tools shop alongside us before you decide. If after reading this guide you want a 30-minute conversation to test the fit, book an intro call and we will give you an honest read in either direction.

The most important thing this guide can give you is a clear head before the sales calls. Vendors in this category sell well. Most of them sell to a buyer who has not done the diligence to ask the questions that matter. With the framework above you will not be that buyer.