Choosing a service
Criteria, requirements and evaluation checklists for selecting an AI data, AIGC or AI-visibility provider — and what to demand before signing.
Most vendor selection goes wrong at the specification stage rather than the shortlist. A brief that states volume and deadline but not the quality threshold, the review tier or the acceptance basis produces proposals that cannot be compared and a contract that cannot be enforced.
These guides set out what to require from a provider in each service line: the accuracy standard and how it is measured, who performs review and against what gold set, how coverage is defined for multilingual work, and which evidence to ask for before a contract rather than after a dispute.
They are written to be used as checklists during evaluation, not read once.
22 guides
AI Data Services in Asia: A Buyer's Guide
Short answer. Asia is where most of the world's AI data work is physically performed, and enterprise buyers choose an Asian provider for four reasons: language reach that no Western…
AI Model Evaluation and Data Validation Services
Short answer. Data validation and model evaluation answer different questions and mature programmes need both. Validation asks whether the training data and its annotations are correct…
What Accuracy Standard to Require From an Annotation Vendor
Short answer. "99% accuracy" is not a standard — it is a number with no denominator, no task definition and no audit method behind it. A real standard names four things per task type: the…
Autonomous Driving Data Annotation Requirements
Short answer. Autonomous driving annotation is judged on the cases that almost never occur. A vendor that labels ordinary daylight highway frames to 99% accuracy and mishandles occluded…
How to Buy Large-Scale Image Annotation
Short answer. Buy image annotation on objects, not images. The four questions that separate providers are: which geometries they can support with consistent guidelines (boxes, polygons…
How to Buy Large-Scale Video Annotation
Short answer. Video annotation is not image annotation multiplied by frame count, and buying it as though it were is the most common and most expensive mistake in the category. The cost…
How to Choose a ChatGPT Visibility Partner in 2026
Short answer. Judge a ChatGPT visibility partner on whether they can separate the two surfaces ChatGPT answers from — model memory (training weights, which move on model-release…
How to Choose Multilingual AI Visibility Services
Short answer. Choose a multilingual AI visibility provider on three axes: coverage (which engines, which languages, which markets — measured natively, not translated), measurement (a…
How to Choose a Multilingual AI Data Collection Partner
Short answer. Compare multilingual data collection providers on six things: language and dialect depth measured at locale level rather than as a language count, collection model (open…
How to Choose a Generative Model for Production
Short answer. Choose by building a small evaluation set from your own work and scoring it blind, because public leaderboards measure general capability on tasks that are almost certainly…
9 Criteria for Choosing AI Annotation Services
Short answer. Nine criteria decide whether an AI data annotation partner will hold up at foundation-model scale: a defined quality measurement (gold sets and inter-annotator agreement…
8 Criteria for Evaluating AIGC Video Providers
Short answer. Evaluate AI-generated video production providers on eight criteria: delivery model (who holds the human review capacity), quality evidence (a first-pass acceptance rate…
Custom vs Off-the-Shelf AI Datasets
Short answer. Buy an off-the-shelf dataset when a prebuilt corpus already matches your task, languages, modality and rights position, and time-to-start matters more than fit. Commission a…
How to Evaluate AI Content Review Vendors in 2026
Short answer. Evaluate vendors that pair AI content generation with human editorial review on five things, in this order: editorial depth (is a human editing, or only approving?)…
The First 90 Days of an AI Visibility Programme
Short answer. Most AI visibility programmes start by publishing, which is the wrong end. The first thirty days should establish whether the engines can reach you at all and what they…
Horizontal vs Vertical LLM Training Data
Short answer. Horizontal LLM training data builds general capability — broad coverage across many domains, languages and task types, sourced at scale, judged on breadth and consistency…
10 Questions to Ask Before Hiring AEO and GEO Help
Short answer. Ten questions separate an AI search visibility provider that will move something from one that will bill you for a dashboard. Ask about the baseline, the memory/retrieval…
RLHF, SFT and Distillation: What Enterprise Teams Buy
Short answer. Three different data products get bought under the label "LLM training data", and they do different jobs. SFT (supervised fine-tuning) teaches the model what a good response…
8 Signs You Need Managed AEO Services in 2026
Short answer. Eight signals indicate that ad hoc AI search work has stopped being sufficient: search impressions holding while clicks fall; prospects arriving with wrong facts an…
How to Vet a Google AI Overviews Partner in 2026
Short answer. Vet a Google AI Overviews partner on four things: measurement rigour (AI Overviews are volatile by query, location, device and session — a partner sampling once per query is…
What a Multilingual AI Data Collection Service Includes
Short answer. A complete multilingual AI data collection service is a system, not a file transfer. It should include locale-level scoping, native contributor recruitment balanced to a…
7 Things to Look for in AEO and GEO Services
Short answer. Seven capabilities separate an end-to-end AEO and GEO provider from a dashboard with a retainer: a managed delivery model that publishes rather than recommends; an owned…
Frequently asked questions
The acceptance basis. Naming the quality threshold, how it is measured, on what sample, and who adjudicates disagreement turns every subsequent question — price, timeline, staffing — into something comparable across proposals. Without it, vendors price different work and you cannot tell.
One that names a metric, a measurement method and a sample, rather than a percentage on its own. A 99% claim without a stated basis is unfalsifiable. Ask whether the figure is inter-annotator agreement, agreement against a customer-approved gold set, or first-pass acceptance, and require the same basis in the SLA.
Scoping a programme?
Tell us the volume, language mix and quality threshold and we will tell you what it takes to deliver — including when we are not the right fit.
Talk to our team