Blue Sage Data Systems
Applied AI · White-glove transformation

End-to-end AI transformation for mid-market firms — fully managed, deliberately structured.

Leaders don't have spare hours to lead an AI overhaul. Blue Sage runs it on your behalf — hands-on, end to end:

Outcomes

What changes after a Blue Sage engagement

A small list of the things that look different six months in.

Today · stuck
  • People doing work a machine should be doing — retyping submissions, re-keying invoices, filling the same forms a fourth time this week
  • Hours per week lost to data entry, status reports, and inboxes that never close
  • Senior staff stuck on operational busywork instead of the judgment calls they were hired for
  • "AI strategy" = three free trials and one tired IT director
  • Costs trending up, output flat, leadership unsure what to even ask for
Six months in · compounding
  • Software handles the retyping, re-keying, and form-filling quietly in the background, with a human approving the exceptions
  • Those same hours go back to the work that actually moves the business — underwriting, loan review, machine setup, customer calls
  • Senior staff back on the judgment, supervised by people, supported by tools, accountable for the outcome
  • A written plan with a name on every initiative, a number on every milestone, and a calendar everyone can see
  • Output up, operating costs down, leadership clear-eyed on the next three things to fund
The system

How a 90-day engagement runs

01 / Plan

Stakeholder interviews. Workflow inventory. ROI ranking.

Two weeks with the people who do the work and the people who own the P&L. End of week 2 you have a written plan with a name on every initiative.

02 / Build

Production builds. Real systems. Real volume.

We integrate with the AMS, the EHR, the ERP, the TMS — whatever you already run. No throwaway demos. The AI either runs in production or it doesn't ship.

03 / Hand off

Hands-on training. Runbooks. Monthly cadence.

Your team owns what we built when we leave. The monthly cadence keeps the change compounding instead of decaying.

Proof

We've done this at scale

Past credentials, present discipline.

AI rollout, in production

At Lime I drove the Claude Code rollout across the entire engineering org — MCP integrations wired into Jira, GitHub, Datadog, and Confluence; company-wide trainings on a recurring cadence; one-on-one mentorship for engineers working through their first real changes; and weekly adoption metrics tracked until the curve held above 70%.

The same four-phase playbook — plan, build, train, measure — Blue Sage runs for clients now. Executed at scale before Blue Sage was a brand.

Engineering credibility
  • ·Built Lime's ML production systems on SageMaker, XGBoost, and Snowflake — promotion-conversion and customer-churn forecasting in production at scale
  • ·11+ years of Silicon Valley consulting — Uber, PagerDuty, LegalShield, Juvo, Lime
  • ·Two decades shipping production code on systems serving hundreds of millions of requests
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