Question 1

What services does an AI consultant actually provide?

Accepted Answer

An AI consultant assesses your existing systems and data, selects use cases and models their ROI, designs the architecture, builds and integrates the solution, sets up evaluation and monitoring, and hands the system to your team. Advisory-only firms stop after use-case selection. Engineering firms carry the work through deployment.

Question 2

Which firms are best for automating a business process?

Accepted Answer

Judge on three things: whether the firm integrates directly with your systems of record, whether it establishes a measured baseline before launch, and whether it hands over the source code. A firm that cannot show a live production integration has advised on automation rather than delivered it.

Question 3

Should we hire an outside firm or build an in-house team?

Accepted Answer

Build in-house when AI is your product and you expect to ship many systems. Bring in a partner when you need one workflow in production now. McKinsey reports roughly half of applied-AI talent demand goes unfilled, so hiring typically adds four to nine months before any code ships.

Question 4

How much does an engagement cost?

Accepted Answer

A single-workflow rescue engagement starts around $35,000, scoped after the audit. The audit is priced separately and can be booked on its own. There is no per-seat licence and no recurring platform fee — you own the system, the code, and the infrastructure it runs on.

Question 5

What does moving a pilot to production actually mean?

Accepted Answer

It means taking a proof of concept out of a controlled demo and into a live business system: real data integrations, security controls, monitoring, evaluation against ground truth, and measurable ROI. Most projects fail at this step — McKinsey finds 81% of organizations report no significant bottom-line impact from AI.

Question 6

Do we have to throw away what our previous vendor built?

Accepted Answer

Rarely all of it. The audit issues a keep, rebuild, or retire verdict for each component — prompts, data pipelines, integrations, interface. Prompt logic and domain rules usually survive; sandbox-grade infrastructure usually does not. You get the verdict before you commit to the build.

Question 7

How do you measure whether the AI is actually working?

Accepted Answer

We establish a baseline before launch: current cycle time, error rate, and cost per transaction. Model outputs are then scored against a ground-truth set on every release, and a human audits a sample in production. Accuracy figures are measured after go-live, never projected.

Question 8

We are a mid-size company, not a Fortune 500 — is this overkill?

Accepted Answer

No. The scope changes, not the discipline: one workflow instead of a department-wide programme, from around $35,000. Mid-size companies often reach production faster than enterprises, because there are fewer approval layers between the audit and the go-live decision.

Question 9

How should we choose between firms?

Accepted Answer

Ask for five things: production references you can call, the names of the engineers who will do the work, code ownership at handover, a written method for measuring accuracy, and a fixed first deliverable with a date. Any firm that hesitates on the fifth is selling discovery.

Your AI Pilot Worked. Production Never Came.

Four Signs Your AI Investment Is Stuck in Pilot Purgatory

The demo that never shipped

The legacy integration wall

No owner, no metrics

The vendor moved on

Three Systems That Move AI From Slide Deck to P&L

Pilot Audit & Production Readiness

Production Engineering & Process Automation

Governance, Measurement & Adoption

Why Pilots Die — and Why Ours Don't

In-House Team or Outside Partner: An Honest Comparison

From Stalled Pilot to Production in 6–8 Weeks

Who We Are: Architects of the Agentic Future

The Enterprise AI Tech Stack

Frequently asked questions

The Window Between Pilot and Production Is Where Competitors Pass You