Datalier - Unified Data Platform

The Data Infrastructure Layer for AI model training

Concave AI is building the unified data plaatform that turns raw enterprise data into governed, versioned, production-ready model data embedding various layer across ingestion, preparation, RLAIF labeling, curation, observability, and reusable data distribution. To stop AI models from failing in production while automating the training process for AI teams. We are building the infrastructure for Artificial Intelligence models to be more reliable, governed and sustaining.

Start with a free audit → Talk to us about the platform

Platform architecture · 6 layers

L1 Data Ingestion Active

L2 Data Preparation Active

L3 RLAIF Labeling Active

L4 Curation & Versioning Building

L5 Observability Roadmap

L6 Governed Marketplace Roadmap

Layers 1–3 active in service delivery · Layers 4–6 in productization

● L1–L3 · Active

✓ κ 0.72+ on delivery

Whats New - Coming Soon

Unified Data platform
Embedding Multiple Infrastructural Layers

85% of AI models fail in production because of bad training data, not bad architecture. Yet ML teams still spend 80% of there time of fine-tuning data and stitch together 4-5 fragmented tool chains for collection, cleaning, labeling, monitoring, governing and ensuring data quality, with no single system connecting them. Concave AI is building the unified data platform that replaces this fragmented tool chains, manual pipelines and unmeasured data quality.

The unified data platform consists six infrastructural layers embedded into one system which ingests data from any source, prepares and redacts automatically, labels with RLAIF automation handling 80-90% of the work while human-in-the-loop manage edge cases, allowing engineers to curate and version every dataset with full lineage and reproducibility, followed by a observability to monitor model performance and triggers re-labeling loop whenever quality drifts, and governed layer of data marketplace to get access to pre-built prossuction ready data.

We currently deliver three layers as production-ready infrastructure with ingestion, preparation, and quality measured labeling. The remaining three layers versioning, observability, and marketplace are being productised into the API-first platform launching soon.

Architecture

The platform architecture

The product is organized into six infrastructure layers that work together as one end-to-end workflow — from raw data to certified, reusable model training data.

Layer 01 · Active

Data Ingestion Layer

Connects files, APIs, storage, and enterprise sources into the platform. Validates schema and format before any processing begins. Supports encrypted, audited intake for compliance-sensitive data.

Layer 02 · Active

Data Preparation Layer

Cleans, validates, normalizes, and structures incoming data before labeling begins. Applies task-specific schemas, annotation guidelines, and calibration runs to ensure consistent input quality.

Layer 03 · Active

Automated Labeling Layer (RLAIF)

Uses AI to pre-label, score, or generate preference data — reducing manual workload by up to 60%. Human annotators review edge cases and high-risk items. Kappa tracked live throughout.

Layer 04 · Building

Curation and Versioning Layer

Creates dataset releases with clear version history, diff tracking, and rollback capability. Ensures every dataset snapshot is reproducible and traceable — built for teams that iterate on training data.

Layer 05 · Roadmap

Observability Layer

Monitors live model outputs, drift, performance changes, and quality degradation. Triggers re-labeling loops automatically when quality thresholds are breached. Closes the model feedback loop.

Layer 06 · Roadmap

Governed Data Marketplace Layer

Publishes certified datasets for internal reuse or cross-team distribution. Enforces access control, usage policy, and certification standards. Enables organisations to build reusable data products.

Capabilities

What the platform enables

A consistent, auditable path from raw data to production-ready model data assisting at every stage of the AI development cycle.

Turn raw enterprise data into model-ready training data

Run self-serve labeling operations with role-based workflows

Use RLAIF to accelerate pre-labeling and preference generation

Maintain dataset versions for full reproducibility

Track lineage and data provenance across the full lifecycle

Monitor drift and quality changes over time in production

Reuse certified datasets internally or across teams

Support governance, access control, and full auditability

What to expect

A structured data workflow, automation & faster iteration

Customers should expect a structured data workflow with published quality metrics on every delivery, faster iteration through AI-assisted labeling, human validation where quality matters, and traceable dataset releases that can grow into a self-serve platform as the product matures.

Observability monitors drift

Quality and performance degradation automatically triggers re-labeling loops keeping training data current and improving the model performance.

Version-tagged, reproducible dataset releases

Every output is versioned so you can roll back, compare, or branch without losing history.

AI-assisted labeling, human QA on every task

Model-assisted pre-labeling accelerates throughput; trained reviewers catch edge cases before delivery.

Full lineage from raw input to model-ready output

Traceable audit trail linking source files, annotator actions, and QA decisions to each exported label.

Iteration loop as the platform matures

Self-serve controls unlock progressively — versioning, observability dashboards, and governed sharing as each layer productizes.

Roadmap

Current state and what comes next

Now · Active

Verified Training Data Services

Training data delivery, QA, evaluation, and RLAIF workflows. Managed service across RLHF, NLP, image, video, and evaluation data types. Published quality metrics and benchmark follow-up on every project.

Next · Building

Self-Serve Platform MVP

Self-serve dataset operations, versioning, lineage, and API access. Teams will be able to launch labeling projects, track versions, and integrate with MLOps systems directly — without going through a managed workflow.

Later · Vision

Governed Dataset Marketplace

Certified dataset marketplace and reusable enterprise data products. Organisations will be able to publish, access-control, and distribute verified training datasets internally or across teams at scale.

Why this matters

AI teams need data operating Infrastruture

AI teams need a reliable, sustaining and auditable data operating infrastruture. That not only turns raw data into production-ready model data, but can help engineers focus on building models rather than juggling with data while improving itself as models and users evolve. You look after models we take care of data.