AI Readiness Scorecard

Score your AI programme against a named published rubric, with an evidence trail and a remediation backlog.

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Approval, procurement, audit, and regulated-workflow review all ask the same thing: where is the evidence? A working AI system is necessary but not sufficient. We build a scorecard against a named, published rubric — NIST AI RMF, Google's ML Test Score, a HIPAA / GxP-aligned readiness checklist, or an equivalent industry reference — with the engineering evidence wired into every line item. Not opinion, not a deck.

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AI readiness scorecard against a published rubric

What Lands at the End

What You Keep

The Scorecard is for an approval committee, internal audit, regulated-workflow review, or board-level readiness question that needs a defensible score against a named external rubric. Entry needs a named rubric, a defined scope of system or workflow, and access to existing artefacts. It runs 2–5 weeks, fixed-price.

Scorecard deliverable

The Filled Scorecard

Per-item

Each item of the named published rubric scored on a defined scale, with a written rationale.

Evidence map deliverable

The Evidence Map

Reviewable

Rubric item to evidence ID, so every score points to a test log, eval output, runbook, or lineage note.

Remediation deliverable

The Remediation Backlog

Prioritised

Gaps listed explicitly and ranked by approval impact and engineering effort, with dependency notes.

Re-score deliverable

The Re-Score Plan

Optional

When to rerun and what evidence to refresh, so the next rubric pass is cheap and the trajectory is visible.

Rubric scored against an evidence map

What a Scored Rubric Is

The deliverable is not a slide. It is a document set built around an external reference text the reviewer can hold in their other hand: each rubric item scored against a defined scale with a written rationale, each score pointing to one or more evidence IDs, gaps listed explicitly rather than buried, and a remediation backlog prioritised by approval impact and engineering effort. Any qualified reviewer can replay the scoring against the same published rubric using the same evidence map and arrive at the same scorecard within an agreed tolerance.

Rubrics We Work From

NIST AI RMF
AI RMF GenAI Profile
Google ML Test Score
HIPAA Security Rule References
GxP-Aligned Readiness Checklists
FDA Pre-Cert Guidance (as reference)

Not Sure This Is the Right Pack?

If the ask is "build us the evals or the regression harness", that is the Production AI Monitoring Harness — the Scorecard uses harness output as evidence, it does not build the harness. If the ask is "compare these LLMs for our task", that is the LLM Selection Pack. If the ask is "certify us", certification is out of scope for any service we offer. If the system is too expensive or slow to serve, that is the Inference Cost-Cut Pack.

Regulated-workflow team reviewing a readiness scorecard

How We Know This Works

GxP-readiness, EU AI Act, and continuous-validation writing on the rubric-and-evidence-map discipline this pack codifies.

Validation-Ready AI for GxP Operations in Pharma

Validation-Ready AI for GxP Operations in Pharma

Sep 19, 2025

Validation-ready AI under GAMP 5: classification for ML, continuous validation lifecycle, V-model evidence, and controls for AI-specific risks.

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Pharma's EU AI Act Playbook: GxP-Ready Steps

Pharma's EU AI Act Playbook: GxP-Ready Steps

Sep 24, 2025

How the EU AI Act maps onto GxP work in pharma: risk tiers, GPAI duties, codes of practice, and audit-ready execution without a parallel quality system.

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View case studies See all
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Featured Articles

What GxP compliance asks of AI software, how computer-system validation works, and what approval-grade evidence looks like.

Computer System Validation in Pharma: What Engineering Teams Need to Implement

Computer System Validation in Pharma: What Engineering Teams Need to Implement

May 7, 2026

Computer system validation in pharma: when full CSV applies, when CSA's risk-based path is enough, and what each delivers for AI/ML systems.

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Approval-Grade Evidence: Engineering AI for Audit, Procurement, and Regulated Review

Approval-Grade Evidence: Engineering AI for Audit, Procurement, and Regulated Review

Jun 12, 2026

Approval-grade evidence for AI is an engineering output, not a policy document. What goes in the pack, who signs it, and how rubrics map to artefacts.

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Browse all articles See all
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2019
Founded in
95%+
Client Satisfaction Rate
20+
Successful Projects Delivered

Client Testimonials

TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.

Guido Meardi - CEO

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Check V-Nova

TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.

Alex Farrant - Director

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Check CloudRF

TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.

Forrest Smith - CEO & Co-Founder

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Check Kineon

I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.

Gil Hagi - CEO

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Check Tasty

We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.

Anonymous - CEO

AI Readiness Scorecard FAQ

What makes a scorecard defensible rather than opinion?

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Two things: it scores against a named, published external rubric the reviewer can hold in their other hand, and an evidence map ties every score to an artefact — a test log, eval output, runbook, or lineage note. Without a named rubric and an evidence map, the artefact collapses to opinion.

What rubrics do you score against?

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NIST AI RMF and the AI RMF GenAI Profile, Google's ML Test Score, FDA Software Pre-Cert / GxP guidance as reference (not certification), and HIPAA Security Rule mapping references, among equivalents. Which rubric applies is part of the entry scoping.

Can another reviewer reproduce the scoring?

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Yes. Any qualified reviewer can replay the scoring against the same published rubric using the same evidence map and arrive at the same scorecard within an agreed tolerance. The buyer can also demand a re-score in twelve months and watch the trajectory move.

Do you build the eval or regression harness too?

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No. The Scorecard uses harness output as evidence; it does not build the harness. Building the eval, regression, and drift harness is the Production AI Monitoring Harness.

Do you certify us?

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No. Certification is out of scope for any service we offer. Readiness scoring is engineering evidence the legal, medical, and regulatory roles you already have can read and challenge — refer to legal and regulatory partners for certification itself.

Approval committee reviewing a readiness scorecard

Start a Conversation

The life sciences crosswalk routes regulated-readiness scoring through this pack. The scorecard is engineering evidence the legal, medical, and regulatory roles you already have can read and challenge — not certification, audit sign-off, or legal interpretation. For the wider discipline this pack delivers, see AI governance and trust.

If you have a named published rubric, a defined scope of AI system or programme to be scored, and access to the existing engineering artefacts, contact us and tell us the rubric, the scope, and what your approval workflow needs the scorecard to defend against.

Start a conversation Name the rubric
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