AI Consulting

What is AI consulting?

AI consulting is a structured engagement that helps an organization decide what AI to build, what to buy, and what to skip — based on its real data, team capability, and business priorities. Good AI consulting is the difference between an executive offsite full of slideware and a roadmap that actually ships.

We bring hands-on engineering experience to strategic decision-making. Our consultants have built the systems they recommend, so the advice is calibrated to what works in production, not what looks good in a board deck.

Key terms used on this page:

• AI readiness: The combined data, infrastructure, talent, and governance maturity needed to operate AI in production.
• Build vs. buy: The choice between commissioning a custom AI system, licensing a vendor product, or partnering with a specialist.
• AI governance: The policies, controls, and review processes that make AI use safe, auditable, and compliant.
• POC (Proof of Concept): A bounded build that tests whether a specific AI approach will work for a specific problem.

How does an AI consulting engagement work?

Our engagements are organized around four phases. We don't sell each phase separately — they're a single sequence designed to compound:

1. Assessment — We evaluate current capabilities, data assets, infrastructure, and organizational readiness for AI adoption. Output: a readiness scorecard and a list of unlocks.

2. Strategy — We define a practical AI roadmap aligned with business objectives and resource constraints. Output: a prioritized portfolio with rough cost, time-to-value, and risk for each item.

3. Implementation Planning — We architect solutions, select the right tools, and build a phased delivery plan. Output: technical architecture, vendor short list, and a delivery schedule.

4. Enablement — We train your teams and establish processes for long-term AI success. Output: governance framework, internal playbooks, and a hand-off plan.

Most engagements run 4 to 12 weeks depending on scope and the number of business units involved.

When should you hire AI consultants?

There are five common triggers:

• You have an AI mandate from leadership but no plan. A board or CEO has asked "what's our AI strategy?" and the team needs an answer that survives technical scrutiny.
• You've run AI pilots that didn't ship. POCs keep stalling at the demo stage and never reach production. This usually means the pilots picked the wrong problems or skipped the data work.
• You're evaluating vendors. Six AI vendors are pitching, each promising 10x results, and you need an objective read.
• You have data but don't know what's worth doing. Operational data is sitting in warehouses, lakes, or spreadsheets, and you suspect it could power something — but you don't know what.
• You're scaling a single AI win across the organization. One team built something that works, and now leadership wants the playbook everywhere.

Should you build, buy, or partner for AI?

This is the single most consequential decision in an AI roadmap. There is no universal answer — it depends on the problem, the data, and the team. Here is how we frame it:

Option	Best for	Speed	Differentiation	Cost (3 yr TCO)	Lock-in risk
Buy (SaaS / API)	Generic problems, weak internal team, urgent timeline	Days–weeks	None — competitors get the same thing	Recurring, scales with seats / volume	High — you build on the vendor's roadmap
Build in-house	Core differentiators, mature engineering org, distinctive data	6–18 months	Highest	Highest upfront, lowest recurring	Low
Partner with a custom shop (our model)	Differentiated workflows, no in-house AI team, want to own the IP	8–16 weeks per workflow	High — built on your data and processes	Predictable, paid-back in 3–9 months	Low — you own the code

The pattern we see most often: buy the commodity layer (foundation models, embeddings, infrastructure), partner on the differentiated workflows (your specific data and judgment), build in-house only when you have an engineering team that can sustain it.

How do you evaluate AI vendors?

We use a six-axis scorecard. Vendors should be able to answer all of these clearly; if they can't, that's the answer.

Axis	What we look for
Capability fit	Does the product solve our specific workflow, not a generic version of it?
Data handling	Where does our data go? Is it used for training? Can we deploy in our VPC?
Accuracy	What benchmarks exist on our data, not theirs? Can we run a paid pilot?
Cost trajectory	What's the all-in cost at 1x, 5x, 25x usage?
Integration	Native connectors to our actual stack, or vaporware?
Roadmap independence	If we change our mind in 18 months, what's the exit cost?

We've used this exact scorecard to disqualify vendors that interview well but collapse under the data-handling and roadmap-independence questions.

What does AI governance actually look like?

Most "AI governance" deliverables are a 40-page policy document nobody reads. We build operational governance — review processes, monitoring, and incident response — that survives contact with the engineering team:

• Use-case intake: A lightweight form that classifies new AI use cases by risk tier (internal/external, regulated/unregulated, decision-affecting/advisory).
• Pre-launch review: A short checklist covering data lineage, evaluation results, fallback behavior, human-in-the-loop requirements, and audit logging.
• Production monitoring: Drift detection, hallucination rate, and outcome metrics tied to business KPIs.
• Incident playbook: Who pages whom when an AI system misbehaves, and how the rollback works.

For regulated industries (legal, finance, HR), we map the framework to the relevant regulations — GDPR, LFPDPPP (Mexico), the EU AI Act, and sector-specific bar or financial-regulator guidance — so legal and compliance can sign off without rewriting it.

What should you expect to pay for AI consulting?

Our engagements typically run between USD 25,000 and USD 150,000, scaled to the company size and scope. A single-business-unit readiness and roadmap engagement sits at the low end; a multi-region, multi-business-unit transformation sits at the high end.

We charge hourly with a cap so the budget is predictable and the scope can flex without renegotiation. We do not take percentage-of-savings or success-fee structures — they create perverse incentives.

For pricing on the build phase that follows consulting, see our Pricing page.

Frequently asked questions about AI consulting

Do we need an AI strategy if we're already using ChatGPT?

Yes. Individual ChatGPT use is helpful productivity but not a strategy. A strategy answers what AI capabilities the business should own, what data and workflows justify investment, and how the organization governs use across teams.

How long does an AI strategy engagement take?

Four to twelve weeks depending on scope. Single-business-unit assessments land in 4 to 6 weeks; multi-business-unit roadmaps run 8 to 12.

Will you also build the systems you recommend?

Usually, yes — most clients continue with us through implementation. We do not require it. If you'd rather hand the roadmap to your internal team or a different shop, we'll structure the deliverable to support that.

How do you handle confidentiality during the engagement?

Mutual NDA before kickoff, named-user access only, and we never use client data to train any model. Our consultants work in your environment, not export data to ours.

What if our data isn't ready for AI?

That's the most common finding. We don't paper over it — we put data foundations in the roadmap as the first phase, with concrete deliverables and rough cost. Trying to do AI on broken data is the most expensive mistake we see.

Can you help us choose between AWS, Azure, and Google Cloud for AI workloads?

Yes. We'll evaluate model availability (Bedrock vs. Azure OpenAI vs. Vertex), cost at your projected volume, integration with your existing stack, and data residency requirements. The right answer is usually whichever cloud you're already on, unless model access drives the choice.

Do we need to hire an AI team in-house?

Usually not at first. The pattern we recommend: partner externally on early builds, hire a small AI platform team (1–3 people) to maintain and extend the systems, expand only after a portfolio of working systems justifies it. Hiring an AI team without a portfolio of problems is how organizations end up paying senior salaries for slideware.