Enterprise Local LLM Readiness Assessment

A 25-point self-assessment for IT and security leaders in Southeast Asia and Japan

Simplico Technology Consultancy | hello@simplico.net

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How to Use This Assessment

Work through the five dimensions below. For each statement, score your organisation:

Score	Meaning
2	Yes — fully in place
1	Partial — in progress or inconsistent
0	No — not yet addressed

Add up your scores at the end. The scoring guide on the final page tells you where you stand and what to do next.

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Dimension 1 — Compliance and Data Sovereignty

These questions determine whether your regulatory environment makes local LLM deployment necessary rather than optional.

#	Statement	0	1	2
1	We have identified which data categories are subject to PDPA, APPI, PIPL, 等保2.0, or sector-specific regulations in our operating markets.	☐	☐	☐
2	We have a documented policy on whether regulated data may be sent to third-party cloud APIs.	☐	☐	☐
3	Our legal or compliance team has reviewed the data processing terms of the cloud AI services currently in use.	☐	☐	☐
4	We can produce an audit trail showing where personal or sensitive data has been processed in the last 12 months.	☐	☐	☐
5	We have assessed whether any current AI usage constitutes a cross-border data transfer under applicable law.	☐	☐	☐

Dimension 1 subtotal: ___ / 10

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Dimension 2 — Infrastructure Readiness

These questions assess whether your hardware and network environment can support on-premise inference.

#	Statement	0	1	2
6	We have GPU-capable server infrastructure in-house or in a private data centre we control.	☐	☐	☐
7	Our internal network can support low-latency inference requests from the teams that would use the LLM.	☐	☐	☐
8	We have a process for hardware procurement and can acquire new server infrastructure within a reasonable lead time.	☐	☐	☐
9	Our IT team has experience operating Linux-based server workloads and container environments.	☐	☐	☐
10	We have a backup and disaster recovery plan that covers AI inference infrastructure.	☐	☐	☐

Dimension 2 subtotal: ___ / 10

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Dimension 3 — Use Case Clarity

These questions determine whether you have defined workloads ready for local LLM deployment.

#	Statement	0	1	2
11	We have identified at least one specific internal use case for LLM deployment (e.g. document Q&A, contract review, production log analysis).	☐	☐	☐
12	We understand whether our primary use cases require retrieval-augmented generation (RAG) over internal documents, or direct prompt-based inference.	☐	☐	☐
13	We have estimated the expected query volume for our target use cases on a monthly basis.	☐	☐	☐
14	We have defined what "good output" looks like for our use cases and can evaluate model responses against that standard.	☐	☐	☐
15	We have prioritised our use cases and identified which one we would deploy first in a proof of concept.	☐	☐	☐

Dimension 3 subtotal: ___ / 10

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Dimension 4 — Integration Complexity

These questions assess how much work is required to connect an LLM to your existing systems.

#	Statement	0	1	2
16	The systems we want to connect to an LLM (ERP, MES, document stores) have accessible APIs or structured data exports.	☐	☐	☐
17	We have internal development capacity to build or maintain integration connectors between the LLM and our business systems.	☐	☐	☐
18	Our internal knowledge base or document library is organised and accessible in a format suitable for vector indexing.	☐	☐	☐
19	We have defined which user groups or applications will consume the LLM API and have a plan for access control.	☐	☐	☐
20	We understand the data classification of documents that would be ingested into a RAG pipeline and have a policy for what may be indexed.	☐	☐	☐

Dimension 4 subtotal: ___ / 10

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Dimension 5 — Organisational Readiness

These questions assess whether your organisation has the stakeholder alignment and internal capability to sustain a local LLM deployment.

#	Statement	0	1	2
21	A senior sponsor (CTO, CIO, or equivalent) has committed to evaluating or deploying local LLM capability.	☐	☐	☐
22	Our security and compliance teams have been engaged in the AI infrastructure conversation and are aligned on the on-premise approach.	☐	☐	☐
23	We have a budget allocation or approval process in place for AI infrastructure investment in the current or next fiscal year.	☐	☐	☐
24	We have at least one internal person who can own the LLM deployment technically — or we have a clear plan to engage an external partner.	☐	☐	☐
25	We have a plan for communicating the new AI capability to end users and managing adoption.	☐	☐	☐

Dimension 5 subtotal: ___ / 10

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Total Score

Dimension	Subtotal
1 — Compliance and Data Sovereignty	___ / 10
2 — Infrastructure Readiness	___ / 10
3 — Use Case Clarity	___ / 10
4 — Integration Complexity	___ / 10
5 — Organisational Readiness	___ / 10
Total	___ / 50

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What Your Score Means

40 – 50 — Ready to Deploy

Your organisation has the compliance awareness, infrastructure, use case definition, and stakeholder alignment to move into a production local LLM deployment. The main value of a partner at this stage is execution speed and depth of expertise — avoiding the 3–6 months of trial and error that most in-house teams experience.

Recommended next step: Request a scoped deployment proposal. Bring your top two use cases and your infrastructure specs. We can return a delivery plan within a week.

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25 – 39 — Strong Foundation, Identified Gaps

You have meaningful progress across most dimensions but have specific gaps that will slow or block a deployment. The most common patterns at this score range:

• Compliance reviewed but not translated into a data handling policy for AI
• Use cases identified but not prioritised or scoped
• Infrastructure available but no RAG-ready document library

Recommended next step: A focused gap assessment — typically a two-hour conversation — to identify which gaps to close internally and which to hand to a partner. This prevents a deployment that stalls three months in.

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Below 25 — Start with a Proof of Concept

Your organisation is earlier in the journey. That is not a blocker — it means the right starting point is a time-boxed proof of concept on a single use case, which simultaneously validates the technology and builds the internal alignment needed for a full deployment.

Recommended next step: Identify your single most valuable and least complex use case. A two-to-three week PoC on that use case is the fastest way to move the internal conversation forward without a large upfront commitment.

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Dimension-Level Flags

Your total score matters, but so does the pattern of your subtotals. Watch for these:

Dimension 1 below 6 — Regulatory exposure is high. Any existing cloud AI usage should be reviewed before expanding. Local deployment becomes urgent, not optional.

Dimension 2 below 4 — Infrastructure gaps may require hardware investment before deployment. Factor procurement lead times into your timeline.

Dimension 3 below 4 — Use case ambiguity is the most common cause of failed LLM deployments. Spend time here before committing budget to infrastructure.

Dimension 4 below 4 — Integration complexity will define your deployment timeline more than any other factor. Assess your ERP and document system APIs early.

Dimension 5 below 6 — Without senior sponsorship and security alignment, even a technically successful deployment will struggle to scale. Address this in parallel with technical planning.

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About Simplico

Simplico is a technology consultancy based in Bangkok serving enterprise clients across Southeast Asia and Japan. Our local LLM harness service covers model selection, infrastructure configuration, RAG pipeline, guardrails, observability, and integration with ERP, MES, and document systems — deployed fully within your network perimeter.

We work with organisations at every stage of this assessment — from early-stage PoC through to full production deployment across multiple business units.

To discuss your assessment results or request a scoped proposal:

hello@simplico.net

Reference this assessment in your email and we will prioritise your response.

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© Simplico. This assessment may be shared internally within your organisation. For redistribution rights, contact hello@simplico.net