How to Choose an AI Consulting Company: 10 Criteria and What Drives the Cost

The right AI consulting company doesn’t lock you into one product or model, can show you systems it has actually put into production, treats security and data protection as design requirements, and starts with a measurable pilot. Cost is driven mainly by scope, how ready your data is, integrations and security requirements. The 10 criteria and question list below help you compare proposals on the same basis.

What should a good AI consultancy deliver?

AI consulting is more than a slide deck or strategy document. A strong consultancy’s deliverables typically include:

• Opportunity and prioritization analysis: where AI will create real value in your processes, and where it won’t.
• Architecture and technology recommendation: model, infrastructure and integration options, with trade-offs made explicit.
• A working pilot: built on your real data and evaluated against success criteria defined upfront.
• A production and operations plan: monitoring, security, cost control and maintenance.
• Knowledge transfer: documentation and training so your team can understand and own the system.

What 10 criteria should you use to choose an AI consulting company?

1. Vendor neutrality: Do they push a single model provider or platform, or can they compare cloud and open-source options against your needs? A neutral advisor also accounts for switching costs.
2. Production track record: Demos are easy; keeping a live system running is hard. Ask for reference calls, a working demo and a concrete account of how live systems are monitored.
3. Security and data protection: They need a clear approach to masking personal data, cross-border transfers, access control and audit logging.
4. Data ownership and IP: The contract should state who owns the code, prompts, evaluation sets and configurations, and guarantee in writing that your data won’t be reused for other clients.
5. Team seniority: Ask whether the senior people in the sales meeting will actually work on your project. The proposal should name roles and time allocation.
6. Measurement discipline: “It seems to work” is not enough. Success metrics should be defined with you at the start and reported against evaluation sets.
7. Handover and training: At the end, can your team run the system? Documentation, source code access and training sessions should be in scope.
8. Support and SLA: Who responds after go-live, and how fast? Response times, scope and escalation paths should be in writing.
9. Pilot-first approach: A firm that proposes a limited, time-boxed pilot instead of a large upfront commitment lowers your risk.
10. Pricing transparency: Instead of a single number, expect work items, assumptions, exclusions and recurring costs (model usage, hosting, maintenance) listed separately.

What are the red flags?

• Firm outcomes and timelines promised before anyone has looked at your data.
• One product or model presented as the answer to every problem.
• No references, demos or production examples available.
• Security and compliance questions deferred to “later”.
• Restricted access to source code and configurations, or a vague handover.
• No definition of how success will be measured.
• Proposals that omit recurring costs like model usage and hosting.

Which engagement model fits you?

Many organizations start with a fixed-price pilot, move to T&M for the production build, and switch to a retainer once the system is live.

What factors drive the cost of AI consulting?

Two projects with the same headline can have very different budgets. The usual reasons:

• Scope: how many processes, user groups and languages are covered.
• Data readiness: scattered data, scanned PDFs or legacy systems increase cleaning and structuring effort.
• Integrations: the number and complexity of connections to ERP, CRM, document management or identity systems.
• Security requirements: role-based access, personal data masking, audit logs and penetration testing.
• Cloud vs. on-premises: self-hosted models bring hardware and operations overhead; cloud APIs create usage-based recurring costs.
• Model usage costs: query volume, context length and model size directly affect monthly spend.
• Support expectations: business-hours support and round-the-clock monitoring are very different commitments.

That’s why a sound proposal comes after a discovery phase and states its assumptions.

What should international buyers consider when working with a consultancy in Türkiye?

• Time zone: Türkiye is on GMT+3 year-round, which gives good overlap with European, Gulf and East African working hours and a partial morning overlap with the US East Coast.
• English delivery: Confirm that meetings, documentation, code comments and reports will be in English.
• Contracts: Agree on the contracting entity, governing law and IP assignment clauses early.
• Data transfer under GDPR: If personal data from the EU will be processed by a team in Türkiye, the transfer needs a valid legal basis, typically Standard Contractual Clauses and a data processing agreement. Architectures where data stays in your own EU cloud tenant and the consultancy works with controlled access can simplify this.

What should you ask in the first meeting?

• Which production projects do you have that resemble our use case? Can we speak to a reference?
• How do you choose models and infrastructure? Would we be tied to one provider?
• How do you handle personal data and cross-border transfers?
• How will you define and report the pilot’s success criteria?
• Who will work on the project, and in what roles?
• Who will own the source code, prompts and evaluation sets?
• What are your support and SLA terms after go-live?
• Which recurring costs does your proposal include, and how do you estimate them?

How clearly these questions are answered often tells you more than the number on the proposal.

How does working with BrotherhoodIO work?

BrotherhoodIO is an AI consulting and enterprise software company based in Ankara, Türkiye. We start with a AI assessment: a 30-minute conversation where we listen to your processes and data situation and talk honestly about where AI can create real value, and where it can’t. From there:

• We work pilot-first, defining success criteria with you upfront.
• We stay vendor-neutral, comparing cloud and self-hosted models against your needs.
• We design privacy-first, with KVKK and GDPR requirements, access control and data security built into the architecture.
• We deliver a full handover with source code, documentation and training, in English.

If you’re evaluating AI consulting proposals, an AI assessment is a low-risk way to sharpen your requirements. Contact us or call +90 532 380 40 41, and let’s identify the right first step for your organization.