LLM Selection Pack

Build the LLM eval suite and risk-and-comparison report your approval committee can re-run on every model swap, scored on your own tasks.

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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 — the eval reports, model comparisons, lineage, and approval workflow around it are what unblock the deal. We design and build those artefacts on your candidate models and your tasks, without claiming a certification we are not in the business of issuing.

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LLM evaluation and approval evidence

Three Things Land at the End

What You Keep

The pack is for an approval committee, procurement team, audit owner, or regulated-workflow review that needs structured evidence to decide between candidate LLMs. It runs on your own tasks, not generic vendor benchmarks. There is a strict entry gate: a candidate model list, a task set, representative inputs, and a named approval owner — if any of those is missing we help you assemble it before the pack starts. It runs 3–6 weeks, fixed-price.

Eval suite deliverable

The Eval Suite

Programmable

Task definitions, scoring rubric, slice metrics, and a paired-comparison protocol on your candidate models and your tasks.

Report deliverable

The Risk & Comparison Report

Decidable

Model-vs-model results, failure modes, lineage notes, and recommended approval conditions a committee can sign against.

Re-run deliverable

The Re-Run Script

Re-runnable

Runs the eval suite end-to-end on the supplied inputs, so the next model swap is a rerun, not a fresh engagement.

LLM model-comparison evaluation in progress

What the Eval Suite Is

For the buyer, the eval suite is a programmable harness an approval committee can demand a re-run from at any time: task definitions tied to the work the LLM is being approved for, not generic benchmarks copied from a vendor deck; a scoring rubric the committee can read and challenge; slice metrics that surface where a candidate underperforms; a paired-comparison protocol so model-vs-model results are decidable rather than narrative; lineage notes; and recommended approval conditions — aligned to recognised public frameworks where applicable. The report is its first output; every subsequent rerun is one more.

What This Pack Covers

Task-Specific LLM Evals
Model-Comparison Evidence
Scoring Rubrics
Slice Metrics
Paired-Comparison Protocols
Lineage Notes
GenAI Model-Risk Reports
Approval-Condition Recommendations

Not Sure This Is the Right Pack?

If a deployed AI system has quality regressions or no release gate, that is the Production AI Monitoring Harness. If the question is "score our programme against NIST AI RMF or ML Test Score", that is the AI Readiness Scorecard. If the chosen LLM is too expensive or slow to serve, that is the Inference Cost-Cut Pack; if it does not yet run on the target you need, the AI Porting & Deployment Pack.

Approval committee weighing model options

How We Know This Works

Model-comparison reasoning, RAG architecture, and LLM-lifecycle discipline. These pieces pre-date the packaged pack and stand as bridged proof.

GPT-3 vs GPT-4: architecture, scale, and what actually changed

GPT-3 vs GPT-4: architecture, scale, and what actually changed

Oct 27, 2023

A working comparison of GPT-3 and GPT-4: dense vs mixture-of-experts, context length, training data, post-training, and what the differences mean in…

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Retrieval Augmented Generation: Examples and Guidance

Retrieval Augmented Generation: Examples and Guidance

Apr 23, 2023

RAG prototype to production: where prototypes break, fine-tuning vs RAG vs prompts, hallucination monitoring, latency/cost targets, pipeline reliability.

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Featured Articles

How to run a task-specific LLM evaluation, what a framework is made of, and how to turn it into sign-off-grade evidence.

How to Run a Task-Specific LLM Evaluation That Survives a Procurement Review

How to Run a Task-Specific LLM Evaluation That Survives a Procurement Review

Jun 12, 2026

A methodology for designing a task-specific LLM eval against your actual workflow that produces the evidence pack a procurement committee can defend.

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What an LLM Evaluation Framework Is — Components, Layers, and How It Works

What an LLM Evaluation Framework Is — Components, Layers, and How It Works

Jun 12, 2026

An LLM evaluation framework is five layers — task definition, dataset, scoring, run conditions, evidence capture

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Turning an LLM Evaluation Into Sign-Off-Grade Evidence: A Procurement Team's Checklist

Turning an LLM Evaluation Into Sign-Off-Grade Evidence: A Procurement Team's Checklist

Jun 12, 2026

How a procurement team converts raw LLM evaluation results into a defensible evidence artefact that survives an approval committee in one round.

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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

LLM Selection Pack FAQ

How is this different from validating a deployed system?

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The Selection Pack produces a paired-comparison harness on your own tasks so an approval committee can decide between candidate LLMs — selection, swap, or approval-to-deploy. Validating an already-deployed system for regressions and release gates is the Production AI Monitoring Harness. The two share eval-harness DNA, not scope.

What does the eval suite actually contain?

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Task definitions tied to the work the LLM is being approved for (not generic vendor benchmarks), a scoring rubric the committee can read and challenge, slice metrics, a paired-comparison protocol so model-vs-model results are decidable, lineage notes, and recommended approval conditions — aligned to recognised public frameworks (HELM-style, lm-eval-harness conventions) where applicable.

Can the committee demand a re-run on the next candidate model?

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Yes. The reproducible re-run script runs the eval suite end-to-end on the supplied inputs, so the next time a vendor revs the model, a prompt changes, or a new candidate enters the shortlist, the team triggers a rerun rather than a fresh engagement.

Do you certify the model or give regulatory advice?

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No. The output is the structured evidence your buyer, auditor, or compliance owner signs against — not a certification, legal advice, or regulatory interpretation. AI security red-teaming as a primary deliverable is not currently productised here.

How is this different from readiness scoring against a rubric?

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The AI Readiness Scorecard reports against a named published external rubric; the Selection Pack produces a paired-comparison harness on your own tasks. One scores a programme against an external reference; the other decides between candidate models on the work they will actually do.

Approval committee reviewing structured LLM evidence

Start a Conversation

The AI-infrastructure / SaaS crosswalk routes LLM-eval and approval-evidence work through this pack. For the wider discipline this pack delivers, see AI governance and trust. For the broader argument about how benchmark methodology should work, see LynxBenchAI, our pre-benchmark methodology sub-brand on this same origin — the Selection Pack is the TechnoLynx engagement buyers commission against their own LLMs and tasks.

If you have a candidate LLM shortlist, a task set you can describe, representative inputs, and an approval owner, contact us and tell us the candidate models, the task shape, and what your approval workflow needs the evidence to look like.

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