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A Top 5 global AI outsourcing company by Outsource Accelerator, above Scale AI.
Service

AI Consulting

Corpshore AI advises on AI data strategy, dataset design, annotation taxonomy, and delivery operations, helping teams turn model requirements into a defensible data pipeline and measurable evaluation.

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Senior
practitioners lead directly
18+
countries of delivery experience
97%+
accuracy standard designed in
50-70%
cost advantage vs US-domestic
What we deliver

Scoped, staffed, and QA-gated

  • AI data strategy and roadmap
  • Dataset and taxonomy design
  • Evaluation and QA design
  • Delivery-operations advisory
Data strategyTaxonomy designEval designQA designOps advisory
AI Consulting at Corpshore AI
Pain points we solve

The problems this service addresses

Decisions about what to collect and how to label it are made without tracing back to what the model actually needs, so effort goes to the wrong data.
A poorly designed taxonomy produces inconsistent labels no amount of review can fully fix, and it is expensive to reverse once production has started.
There is no objective definition of when a dataset is good enough, so quality is a subjective judgment rather than a measured gate.
An existing pipeline produces inconsistent labels or stalled throughput, and the team needs an outside diagnosis of where it breaks down.
A data strategy designed by one party degrades in handoff to another, losing the intent between plan and execution.
Vendor selection is hard to judge without a framework for evaluating quality, cost, and fit against real requirements.
Case scenarios

Where teams use this work

Illustrative examples of how this service fits real programs. They are representative use cases, not named clients.

A team defining an AI data pipeline from scratch

A team is deciding what data to collect, how to structure the taxonomy, and how to measure quality, and needs those early decisions made against model requirements because they are expensive to reverse later.

A company with an underperforming pipeline

A team has inconsistent labels or stalled throughput and needs an outside diagnosis of where quality breaks down, plus an evaluation and QA design to fix it, whether they keep the setup or move it.

A product team designing a label schema

A team needs a taxonomy that is unambiguous for annotators and clean for the model to consume, validated against a real sample before production so agreement holds from the start.

A buyer evaluating a data vendor

A team needs a framework to assess a vendor's quality, cost, and fit against real requirements, plus acceptance thresholds a dataset must clear before it trains a model.

How we deliver

From scope to delivery, end to end

Step through the stages of a ai consulting engagement.

1. Understand model requirements

Start from what the model actually needs, so decisions about what to collect and how to label it trace back to a real objective rather than convention.

Stage 1 of 5
What to expect

How the engagement runs

  • It is most valuable early, when you are defining what to collect, how to structure a taxonomy, or how to measure quality.
  • Senior data-operations practitioners who have stood up multi-hub delivery lead the work directly, not a deck handed to juniors.
  • Engage it standalone to design your pipeline and hand execution to your own teams, or bundle it with delivery so strategy and execution stay aligned.
  • Taxonomies and schemas are validated against a real sample before production, so agreement is designed in rather than discovered late.
  • You provide your model requirements, existing pipeline or constraints, and goals; Corpshore provides the strategy, design, and evaluation framework.
  • The output is a concrete plan you can act on, with acceptance thresholds and a QA design, whether you build in-house or with Corpshore.
Insights

What doing this well requires

  • The decisions that shape a data program, what to collect and how to structure it, are the ones most expensive to reverse, which is why they pay to get right early.
  • A well-designed taxonomy is the single biggest driver of inter-annotator agreement, so schema design pays back across the whole program.
  • Quality should be defined as a measured gate against gold sets and agreement, not a subjective judgment about whether the data feels ready.
  • Strategy and execution degrade in handoff, so keeping them under one operator preserves the intent that a plan-then-throw-over-the-wall model loses.
  • Advice from people who have run the pipelines they are designing beats advice from people who have only diagrammed them.
97%+
accuracy via QA cascade
35+
languages, native in-region
15,000+
seats across 18+ countries
50-70%
cost advantage vs US-domestic
Quality

Every unit passes a three-tier QA cascade

Tier 1

Annotator + peer review

Trained in-region annotators label to a versioned taxonomy. Every unit gets a structured peer check before it moves.

Catches ~80% of errors
Tier 2

Expert QA lead

Domain QA leads audit sampled and flagged work, resolve edge cases, and feed corrections back into annotator guidance.

Catches ~15% more
Tier 3

Programmatic + consensus

Automated consistency checks, gold-set benchmarking, and consensus scoring gate the batch before delivery.

Locks in 97%+ accuracy
FAQ

AI Consulting, answered

AI data strategy and roadmap, dataset and taxonomy design, evaluation and QA design, and delivery-operations advisory. The through-line is turning model requirements into a defensible data pipeline, so decisions about what to collect and how to label it trace back to what the model actually needs.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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