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● AX CONSULTING

End it at “AI delivers results,” not “we adopted AI.”

We start by defining where AI creates the most value, then prove it with a working PoC — not slides.

0wk
to first PoC
0%
PoC built in-house
impact
we start from
● THE PROBLEM

If this sounds familiar.

If even one of these is familiar, this service closes that gap.

Stuck at slides

Great strategy, no one to build it.

PoC graveyard

The demo worked, but it dies against real data, permissions, and cost.

Lost the goal

“Adopt AI” became the KPI, with no measure of what improved.

● SCOPE

What, how far, what we leave behind.

We diagnose your work, data, and organization, then define where AI delivers the most value. We start from a results hypothesis, not the technology.

How far

  • Diagnosis & prioritization — assess workflow, data maturity, org readiness; rank the backlog by impact × difficulty
  • Target architecture — an executable design connecting model, data, and infrastructure
  • PoC design & build — we build it ourselves and validate against production data
  • Operational handover — permissions, cost, monitoring, and org adoption

Deliverables

  • AI transformation roadmap · prioritized backlog
  • Architecture design
  • Working PoC and validation report
  • SoW and phased execution plan
● HOW WE WORK

How we work.

01
DiagnoseInterview work·data·org → impact map
2 wk
02
DesignTarget architecture + prioritized backlog
1 wk
03
Build PoCValidate the highest-impact case on real data
3–4 wk
04
DecideGo/No-Go on operational KPIs, SoW for next phase
● EVIDENCE — NOT COPY

A record of what we shipped.

Entertainment·media group · content metadata automation
10k+/mo
auto-processed
−0%
human review
multilingual
in parallel

Manual tagging·translation — validated in a 4-week PoC and moved to production.

We replaced a manual content tagging·translation pipeline with automated tagging and translation, validated in a 4-week PoC and put into production. People only review exceptions.

View case →
● STACK & DOMAIN

Stack & domain.

LLM orchestrationRAGEval pipelineAWS BedrockSageMaker

We design with the reality of entertainment·media (content/rights) and healthcare (regulation/privacy) — why our PoCs survive in production.

● FAQ

Frequently asked.

Can we take only the consulting and have someone else build it?+
Yes. But handoff loss disappears when the same team continues building — that's our model.
Our data is thin — is this still possible?+
We assess data maturity first. If it's lacking, pipeline work becomes the top backlog item.
Won't the PoC cost be sunk?+
We build PoCs to production-code standards. On a Go, you extend it directly; on a No-Go, you keep validated learning.

Not sure where to start with AI?

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