How to Choose an AI Company or Consultant for Your Business

If you are evaluating an AI company or consultant, one of the easiest mistakes is choosing based on who gives the most impressive demo.

A polished chatbot can be built quickly.

The harder question is whether the person or team can take your real business problem from workflow → architecture → integration → production → measurement.

That is what I would evaluate before signing an AI project.

Don't start by asking which model they use

OpenAI, Claude, Gemini, local LLMs and open models are tools.

Model choice matters, but it should come after understanding the job.

If a consultant starts with:

“We will build this with Model X.”

before understanding your process, data, users, systems and risk, I would ask why the architecture has already been decided.

A stronger starting question is:

What business process are we trying to improve, and what must happen from beginning to end?

The answer may require an LLM.

It may require RAG.

It may require an AI agent.

It may mostly require integration and conventional software.

Sometimes the correct recommendation is to use less AI, not more.

1. Can they understand the business workflow?

Before discussing architecture, they should be able to map the current process.

For example:

Customer request → employee checks CRM → checks another system → makes decision → updates record → sends response

Then ask:

• Where is time being lost?
• Which steps are repetitive?
• Which decisions require judgment?
• Which data sources are authoritative?
• Which actions are risky?
• Where should a human remain involved?

If the proposed solution does not reflect the real workflow, the project can become an expensive demo disconnected from operations.

2. Do they know when not to use AI?

This is one of my favorite tests.

Ask:

Which parts of this project would you deliberately not build with AI?

A good answer might mention deterministic business rules, permissions, calculations, validation, authentication or straightforward workflow automation.

LLMs are powerful, but they are not replacements for every software component.

A consultant who tries to solve every problem with an agent may be optimizing for novelty rather than reliability.

3. Can they explain the architecture without hiding behind jargon?

You do not need to become an AI engineer to buy an AI system.

But the provider should be able to explain the major components clearly.

For example:

• Where does customer input enter?
• Where does company knowledge come from?
• Which model handles which task?
• What tools can the agent use?
• Which system is the source of truth?
• Where are permissions enforced?
• What happens when AI is uncertain?
• What happens when an API fails?
• What gets logged?

If the explanation is only a cloud of terms such as agents, embeddings, RAG, vector databases and fine-tuning, keep asking until the business flow is understandable.

4. Ask what happens when the AI is wrong

Do not ask only for the happy-path demo.

Ask:

Show me the failure path.

What happens if:

• The model misunderstands the user?
• Retrieval returns the wrong document?
• The CRM is unavailable?
• The user asks for an unsupported action?
• Required information is missing?
• Two systems disagree?
• The agent attempts the same action twice?

Production engineering is often about what happens when the ideal path breaks.

The answer should involve controls such as validation, retries, idempotency, escalation, human approval and monitoring where appropriate — not simply “the model is very accurate.”

5. Ask how permissions are enforced

This becomes critical when the system can take actions.

If an AI agent can update customer data, change a booking, send a message or trigger another business process, ask:

• How is the user authenticated?
• What is the agent allowed to access?
• Which actions require approval?
• Are permissions checked outside the LLM?
• Are actions logged?
• Can an action be reversed?

A prompt is not an authorization system.

Business permissions should be enforced by software around the model.

6. Ask how your data will be handled

You should understand what information enters the AI system and where it goes.

Questions can include:

• Which data is sent to external model providers?
• Which data stays in our infrastructure?
• Is sensitive information filtered or minimized?
• How long is application data retained?
• Who can access logs?
• Does the system require a private deployment?
• Would a local LLM or private RAG architecture be appropriate?

There is no single deployment model that is correct for every company.

The architecture should match the actual sensitivity, regulatory and operational requirements of the business.

7. Ask how RAG and company knowledge are designed

If the project uses your documents or internal knowledge, do not accept “we'll put it in a vector database” as the complete plan.

Ask:

• Which sources will be indexed?
• Which source wins when information conflicts?
• How are updates handled?
• How are access permissions preserved?
• Can answers show their sources when needed?
• How will retrieval quality be evaluated?

RAG is a retrieval architecture, not a guarantee of correctness.

8. Ask how they evaluate the system

A demo is not an evaluation strategy.

Before production, there should be a way to test the system against representative tasks.

Depending on the project, that might measure:

• Answer correctness.
• Retrieval quality.
• Tool selection.
• Tool argument accuracy.
• Successful task completion.
• Escalation behavior.
• Latency.
• Cost per task.
• Failure rates.

Ask what the acceptance criteria are before the project begins.

If nobody can define success, it becomes difficult to know whether the AI system is improving.

9. Ask about operating cost, not only development cost

The implementation invoice is not the full cost of an AI system.

You may also pay for:

• Model APIs.
• Hosting.
• Databases.
• Vector/search infrastructure.
• Messaging providers.
• Third-party APIs.
• Observability.
• Maintenance.

A good architecture should avoid using expensive AI calls for tasks that deterministic software can perform reliably.

Ask for the expected cost drivers and what happens to them as usage grows.

10. Look for production experience, not only AI vocabulary

AI projects are still software projects.

They need APIs, databases, queues, authentication, deployments, monitoring, testing, security and product decisions.

That is why I value evidence that a provider can build and operate complete systems — not only notebooks, prompts or isolated prototypes.

Useful evidence can include:

• Production products.
• Technical case studies.
• Open-source work.
• Architecture explanations.
• Real integrations.
• Systems they maintain over time.

The exact portfolio depends on the project, but look for evidence of shipping.

11. Ask who will actually build the system

The person selling the project and the person implementing it may not be the same.

Ask:

• Who owns architecture?
• Who writes the production code?
• Who handles infrastructure?
• Who is responsible for AI evaluation?
• Who responds when production fails?
• Will work be outsourced?

This matters especially when you choose between a large AI company and an independent consultant.

A company may provide a broader team and more capacity.

A senior consultant may provide more direct access to the person making architecture decisions.

Neither model is automatically better. The important thing is knowing who owns the outcome.

12. Ask who owns the code and infrastructure

Before development starts, clarify:

• Who owns the source code?
• Where is the repository?
• Who owns cloud accounts?
• Who controls API credentials?
• Who owns prompts and evaluation datasets?
• Can another engineering team maintain the system later?
• What documentation will be delivered?

Vendor lock-in should be a deliberate business decision, not a surprise discovered after launch.

13. Be careful with guaranteed AI accuracy claims

AI systems can be evaluated and constrained, but broad guarantees such as “100% accurate” should trigger more questions.

Ask:

100% accurate on what dataset, task and conditions?

A useful provider should be comfortable discussing uncertainty, supported use cases and failure boundaries.

14. Ask for a smaller first scope

A provider does not need to automate the whole company in phase one.

A good first project is often one workflow with:

• Clear inputs.
• Clear outputs.
• Accessible data.
• A measurable current cost or pain.
• Controlled risk.
• Enough value to justify the work.

Then measure it.

If it works, expand.

This reduces risk for both sides and produces real information for the next architectural decision.

15. Make sure the proposal contains a business outcome

Compare these two project descriptions:

“Build a multi-agent RAG platform using advanced LLM orchestration.”

and:

“Reduce the manual work required to qualify inbound sales enquiries by extracting the required information, checking company criteria, creating a structured CRM record and escalating qualified opportunities to sales.”

The second description tells you what the system is supposed to accomplish.

The technology can then be chosen around that outcome.

A practical vendor scorecard — without the hype

Instead of ranking providers by who uses the newest model, evaluate whether each candidate can clearly address:

Business — Do they understand the workflow and measurable outcome?

Architecture — Can they explain the system and why each component exists?

Integration — Can they work with your real software and data?

Safety — Are permissions, validation and failure paths designed explicitly?

Evaluation — Is there a repeatable way to test quality?

Operations — Can the system be monitored, maintained and scaled?

Ownership — Are code, infrastructure, documentation and responsibilities clear?

Communication — Can technical trade-offs be explained in business language?

You do not need a numerical score. The gaps themselves are useful discussion points.

Red flags I would investigate further

I would ask more questions when I see:

• Architecture chosen before the workflow is understood.
• Every problem being turned into an AI agent.
• No discussion of failure cases.
• No human handoff for uncertain or risky actions.
• Permissions controlled only by prompts.
• No clear source of truth for company data.
• No evaluation plan.
• No explanation of operating cost.
• A prototype presented as production-ready.
• Unclear code or infrastructure ownership.
• Claims of universal or perfect accuracy without a defined evaluation context.

None of these automatically proves a provider is bad, but each deserves a concrete answer before you commit.

The best first meeting should leave you with better questions

You should leave an AI discovery conversation understanding your own workflow more clearly than when you entered it.

The provider should help separate:

• What needs AI.
• What needs automation.
• What needs integration.
• What should remain human.
• What can be measured.
• What can wait until later.

That is more valuable than hearing a long list of model names.

Choosing an AI partner for your business?

Bring one real workflow to the first conversation rather than a general request to “add AI.”

Describe what happens today, which systems are involved, where time or money is being lost, and what outcome would make the project worthwhile.

Then evaluate whether the person across the table can turn that process into a clear, controlled and measurable technical plan.

Discuss your AI project with Fady Mondy.

My work starts from the business workflow and continues through architecture, AI, integration and production — with the goal of building the smallest reliable system that creates a measurable result.

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Related: AI Consulting, AI Development Services, AI for Business, Custom AI Cost, AI Automation, AI Agents, AI Integration, Private AI and Custom Software Development.