VeUP
VeUP Frontier Practice

Every engagement lands on Bedrock. The model has to earn it.

Claude, GPT, open weights and first-party Nova all arrive through one governed channel — and which one ships is settled by benchmark on the customer's own labelled data, workload by workload. Pick a provider to see the proof behind it.

Every engagement lands here
Amazon Bedrock
one governed model channel — metered, guardrailed, swappable
46
customer accounts in the frontier estate
198
linked AWS accounts underneath them
29
where VeUP is the AWS payer of record
9
published case studies that name their models
What counts as proof here

A published case study is the only production claim on this site — named services, measured outcomes, acceptance criteria. Everything else on these pages is the estate around it: accounts where a model family is under evaluation or in the conversation. Under evaluation is not in production, and this site never blurs the two.

The exhibit

Five candidates. One labelled dataset. The open model won.

Multimodal content moderation at platform scale for a global consumer social platform. This is what model-agnostic looks like when it is real: VeUP's own partner model was in the run and did not win it.

The bench, as run
  • Qwen 3 VL
    Open-weight
    Selected
  • Meta Llama 4 Maverick
    Open-weight
    Evaluated
  • Meta Llama Guard 4
    Open-weight
    Evaluated
  • Claude Opus
    Anthropic
    Evaluated
  • Amazon Nova Premier
    Amazon
    Evaluated
  • Amazon Rekognition
    Amazon
    Incumbent

One labelled dataset built from the platform's own content. Every candidate invoked through Amazon Bedrock, scored on the same images, under the same prompts, in a Phase 1 evaluation deliverable — with Amazon Rekognition held as the incumbent router fallback.

What shipped
54% → 99.58%
NSFW accuracy — prompt engineering alone, no fine-tuning
~$1
per 1,000 images, end to end
11/11
SOW acceptance criteria passed
Read the case study
Bedrock wins. Bedrock is really the path, really clearly.
CEO of the platform (anonymized; on-call quote permission, replay-verifiable)
The method

How the model actually gets chosen.

01

Land on Bedrock

One governed model channel inside the customer's own AWS account. Every call metered, tagged and budget-enforced; guardrails and observability wired before the first prompt ships.

02

Build the labelled set

From the customer's own data, not a public leaderboard. On the moderation engagement that meant a labelled image set; on the PM SaaS it meant 12,000 real production prompts.

03

Score every candidate identically

Same data, same prompts, same harness — frontier labs, open weights and first-party models side by side. The result is a deliverable the customer keeps, not a slide.

04

Ship the winner, qualify the alternate

The winner takes the workload; a second model is qualified as the tested fallback. Because everything runs through one channel, changing your mind later is a config change with a receipt.