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● AX CONSULTING

AI transformation that ends in results, not adoption theater.

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

4wk
to first PoC
100%
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 standards, AI governance ownership, and organizational change management

Deliverables

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

How we work.

  1. DiagnoseInterview work·data·org → impact map
    2 wk
  2. DesignTarget architecture + prioritized backlog
    1 wk
  3. Build PoCValidate the highest-impact case on real data
    3–4 wk
  4. DecideName accountable owners, make the Go/No-Go call on operational KPIs, and confirm the next phase in the SoW
● EVIDENCE — NOT COPY

A record of what we shipped.

Entertainment·media group · content metadata automation
10k+/mo
auto-processed
−70%
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 →
● RELATED INSIGHTS

Go deeper from assessment to cutover.

Let's start from where you're stuck right now.

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● 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.
We don't know where to start.+
Defining that is step one. We interview your work, data, and org to draw an impact map, then prioritize by where the payoff is biggest.
Our data is thin — is this still possible?+
We assess data maturity first. If it's lacking, pipeline work becomes the top backlog item.
How long does it take?+
Two to three weeks to diagnose and design, and usually about four weeks to the first PoC — validated on your real operational data.
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.

Define an executable starting point for AI transformation.

We review the environment and challenge first, then define the right execution scope together.

● Already trusted by teams across finance · healthcare · media · public
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