Question 1

How much does an AI automation engagement cost?

Accepted Answer

Our MVP engagements start at $5,000-$8,000 for a single workflow shipped in 2-3 weeks. Multi-workflow programs typically run $15,000-$50,000 over a quarter. Ongoing operate-and-improve retainers start from $2,500/month. Fixed price before any work starts -- no open-ended billing.

Question 2

How do you decide between n8n, Make.com, and Power Automate?

Accepted Answer

We look at your stack, skills, security requirements, and scale. Microsoft shops typically go with Power Automate. RevOps teams go with Make.com. Engineering-led teams that want self-hosted, Git-versioned workflows go with n8n. We recommend based on fit.

Question 3

Where is my data processed? Is this safe for finance / PII?

Accepted Answer

We default to your tenancy -- Power Automate runs in your M365, n8n can be self-hosted, Make.com runs in EU or US. AI calls go to Azure OpenAI inside your Azure subscription. We sign an NDA and DPA before any work starts.

Question 4

What's the timeline from kickoff to a live workflow?

Accepted Answer

First workflow live in 2-3 weeks. Discovery + scoping is week 1, build is weeks 2-3, and we run a 1-week stabilization period before handover.

Question 5

What if we already have automations built by someone else?

Accepted Answer

We audit what's running, what's broken, and what's worth keeping. You get a written assessment with a stabilization plan. Many clients start with a fix-and-stabilize engagement before building new workflows.

Question 6

What's not a good fit for AI automation?

Accepted Answer

Tasks that change every time, require legal sign-off on each instance, or depend on subjective human judgment. We'll tell you if a use case isn't ready for automation before we scope it.

Question 7

We're a SaaS company in Raleigh -- can you build automation into our product's data pipeline?

Accepted Answer

Yes. n8n is a common choice for Raleigh SaaS data teams because it version-controls in Git, runs in Docker, and exposes a REST API -- it fits naturally into a software engineering workflow. Common patterns we've built for SaaS teams: customer usage analytics pipelines (product events to data warehouse), billing sync workflows (Stripe/Paddle to your internal billing system and CRM), and LLM-assisted support routing (classify inbound tickets by product area and route to the right engineering team). All workflows commit to your Git repository and can be deployed via CI/CD like any other code.

Question 8

Can you automate clinical trial data workflows for pharma or biotech clients?

Accepted Answer

We build around your data governance model. Common workflows for Raleigh pharma/biotech clients: document routing (protocol amendments from sponsor to CRO, with version tracking and approval audit trail), CRO data validation (automated checks on incoming data packages before they enter your EDC system), and audit trail generation (structured log of all data transformations in a format suitable for FDA submission). PHI-adjacent data stays in validated environments -- we design workflows to operate on de-identified or aggregated data wherever possible, and deploy inside your compliant cloud environment when it doesn't.

AI Automation Consultant in Raleigh

Trusted by RTP SaaS, pharma, and engineering teams

What Raleigh engineering teams get from an AI automation engagement

2–3 wks

Git

0 SPOFs

100%

How an RTP SaaS company built a customer usage analytics pipeline their engineering team fully owns

Automation built for RTP's engineering-led SaaS and biotech culture

Two-layer architecture: ops automation + agentic

SaaS data pipeline automation

Pharma and biotech workflow automation

Code-native, no SaaS lock-in

Developer-owned automation: versioned, testable, deployed as code

Week 1 — Technical audit and layer selection

Week 2–3 — Build, test, and commit to your repo

Ongoing — Expand and maintain as code

Other AI automation consulting locations

AI automation consulting questions for Raleigh teams