About Concave AI

Built for AI Models with Data Infrastructure layer

An ML-engineered data infrastructure company for AI model training. We build the RLHF, NLP, SFT, and evaluation data layer that determines whether your AI model works in the real world with quality metrics you can verify, not just trust.

Start a Conversation → Our Quality Standards

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ML engineered

Every quality decision is made to understands what the annotation data needs to do downstream in a training pipeline.

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Published Quality Metrics

Cohen's kappa, inter-annotator agreement, gold standard pass rates, batch error logs shipped with every delivery. You verify the quality claim yourself.

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The Name

What Concave means and why it matters

A concave shape curves inward it focuses everything that enters it toward a single point of precision. That is exactly what we do with raw, unstructured data: we curve it inward through expert human judgment and AI-assisted systems until it converges on high-quality, precisely labeled training data.

Concave

Latin: concavus curved inward, hollow

In optics, a concave lens focuses light to a precise point. In data annotation, we focus raw human feedback, model outputs, and unstructured text to a single, precisely measured quality output the training data your model actually needs.

There is a version of the AI failure story that every ML engineer knows. The model is trained, deployed, and proceeds to hallucinate, contradict, and validate wrong answers confidently with equal confidence. The team reaches for a bigger model, a different architecture, more compute. None of it helps. Because the problem was never in the model.

The problem was in the data. Specifically: who annotated it, how consistently, under what guidelines, with what domain knowledge, measured by what metrics. These are the questions that determine whether a fine-tuned model is genuinely aligned or just statistically plausible.

"The quality of the intelligence you build is exactly the quality of the intelligence you put in."

Concave AI was built to close the gap between the annotation quality that frontier AI labs gets through expert-vetted, measured, published and what was available to AI companies at business affordable prices. That gap was, in 2026, still enormous. We are closing it.

We are not a data labeling BPO that pivoted to AI. We are not a crowdsourcing platform. We are an ML-engineered AI data company that treats every labelling decision as a training signal and every delivery as a model quality intervention.

Why We Exist

The solution we build

AI companies were left without a credible, technically rigorous training data infrastructure partner. They were either paying to vendors or accepting inconsistent quality from generic providers.

01 / The problem

Generic vendors claim quality they cannot measure

Training data providers claim "98% accuracy" a number that is unmeasurable, unverifiable, and meaningless in practice. Inter-annotator agreement, gold standard monitoring, anomaly detection: these systematic controls simply do not exist at most providers.

02 / The gap

The industry measures speed and cost. Nobody measures quality.

Training data vendors readily quote speed and per-unit cost. Ask for the Cohen's kappa on a delivered batch the metric that actually determines signal vs. noise and most have no answer. The industry optimised for volume and turnaround, leaving quality to be discovered after training.

03 / Our answer

Engineering rigour, transparent pricing, published metrics

We apply engineering rigour with project-specific guidelines, three-tier QA, published kappa scores at a pricing that works for AI startups and scale-ups. We operate with native-language coverage across 8 Indic languages, and being independently owned means your training data stays yours.

The Problem We Solve

Three data failure modes. All preventable.

Every AI model quality problem traces back to one of three data failures. Our pipeline is specifically designed to prevent all three.

Failure Type 1

Sycophancy baked in at the data level

When preference data rewards agreeable-sounding responses over accurate ones, the model learns to validate user beliefs rather than provide truthful answers. This is a data-level problem no RLHF training can fix, we inject sycophancy traps and measure susceptibility on every project.

Failure Type 2

Domain errors from miscalibrated pipelines

A legal AI trained on data that confuses jurisdiction with precedent. These are not edge cases they are the standard outcome of generic crowdsourcing applied to specialist domains. Our domain-calibrated pipelines exist for exactly this reason.

Our Solution

Engineered data infrastructure + published proof

Domain-calibrated pipelines, RLAIF pre-scoring for clear-cut cases, three-tier QA running concurrently, and gold standard injection catching drift in real time. Every delivery ships with a data card showing exactly what you received and how it was produced so you never have to guess whether the data is good enough.

What We Stand For

Five principles that govern every project

Transparency over claims

If you cannot measure it, we do not claim it

Every delivery includes a QA report: actual Cohen's kappa by annotator pair and task category, gold standard accuracy, and a batch error log. You verify the quality yourself, the numbers are computed from pipeline export data by automated scripts, not self-reported.

ML-native quality design

Built by someone who trains models, not just manages annotators

Our quality systems are designed by ML engineers who understand what downstream training pipelines need. We build automated QA that audits for ML failure modes which reward hacking, sycophancy, distribution shift not just labelling consistency.

Domain calibration is non-negotiable

Generic pipelines produce generic results

A healthcare AI trained on data that misreads a clinical note will fail at the moments that matter. We maintain domain-calibrated pipelines - legal, financial, agriculture, automotive, genai for every industry we serve.

Complete vendor neutrality

Your training data never sees a competitor

Concave AI is independently owned soyour training data never passes through any other pipeline. We operate with native-language coverage across 8 Indic languages, and your proprietary training cycles and roadmap stay yours.

The feedback loop is the product

We stay until your model actually improves

Two weeks after every delivery, we ask for your benchmark result. If the data produced the expected improvement, we document it if not, we investigate and re-deliver at no cost, this is what turns one project into a partnership.

How We Work

The model behind the quality

Every project runs through a layered pipeline with AI pre-scoring for clear-cut tasks, a domain expert review layer for judgment calls, and three-tier QA running concurrently. The result is AI speed on volume with human precision on judgment, at 40–60% faster turnaround and 35–45% lower cost than pure-human annotation.

See the full process →

Our Promise

If the data does not improve your model, we fix it

Every project includes a two-week model performance check-in. If our data contributed to a measurable benchmark improvement we document it as a case study. If it did not produce the expected improvement we investigate the cause and re-deliver at no additional cost.

This is not a legal guarantee buried in contract terms. It is a professional commitment that exists because we are confident enough in our quality systems to back them with our time and effort.

Talk to our ML team →

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Every delivery: Data + QA Report + Data Card

Not just a data file. A complete, auditable package with kappa scores, gold standard accuracy, batch error logs, annotator demographics, and known limitations.

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Day 14 benchmark follow-up - every project

We ask for your model's benchmark result after training on our data. That result improves the next batch. Most training data providers deliver and disappear. We stay.

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Free re-delivery if quality falls below threshold

If a delivered batch falls below our guaranteed kappa threshold of 0.70, we investigate and re-deliver the affected portions at no cost. This has never happened, but the policy exists.

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Complete data confidentiality - Privacy First

Encrypted S3 storage, signed NDAs for all annotators, named-access-only policies, and privacy-first data handling. Your training data never leaves your encrypted bucket.