RedSpeaker
AI-Ready Data Intelligence

Build better AI. Feed it better data.

Custom external intelligence datasets for AI training, evaluation, and agent workflows.

Training Data
Evaluation Sets
Agent Context
Discuss your AI data gap What we build
01 Define AI task
02 Data recipe
03 Eval cases
04 Agent validation
The problem

Models are everywhere. Good datasets are not.

Every AI team has access to powerful foundation models. The bottleneck isn't compute or architecture - it's the domain-specific, task-aligned data needed to train, evaluate, and trust them in production.

83%
of AI projects that fail in production cite data quality or relevance - not model capability - as the root cause.
6-18 mo
Typical timeline for AI teams to build reliable domain evaluation sets in-house. RedSpeaker compresses this dramatically.
∞ noise
Public datasets and scraped web data don't reflect your AI task. Generic data trains generic models. You need data built around the job.
1 gap
The AI data gap. The distance between what your model can do and what it actually does - closed by the right data layer.
By the numbers

Manual path vs. RedSpeaker

Manual path
Slow, fragmented, expensive
Time to first eval set4-6 months
Data quality reviewAd hoc
Hard negativesSparse
Source contextMinimal
Output formatCustom scripts
Cost basisHigh & time-heavy
RedSpeaker
Scoped, structured, eval-ready
Time to first eval set2-3 weeks
Data quality reviewStructured
Hard negativesBuilt-in
Source contextPackaged
Output formatEval-ready
Cost basisScoped & predictable
How it works

Scoped data, delivered fast.

We build structured datasets around your specific AI task - from definition to delivery. No raw feeds. No guesswork.

01
Define the AI task
We map exactly what your model or agent is doing - context, failure modes, domain boundaries.
→
02
Build the data recipe
We design the schema: what signals matter, what sources are authoritative, what edge cases must be covered.
→
03
Create the dataset
We produce labeled examples, hard negatives, source context, structured outputs - AI-ready, not raw.
→
04
Deliver eval-ready output
You receive a structured package ready for fine-tuning, validation, or agent workflow testing immediately.
What we build

Three types of AI-ready data.

⬡
Training Datasets
Curated, domain-specific examples with labels, structured outputs, and source annotations. Built around your model's actual task - not generic corpora.
Fine-tuningRLHFInstruction tuning
◈
Evaluation Sets
Rigorous benchmark packs with hard negatives, edge cases, and difficulty gradients. Test what your model actually gets wrong - before users find out.
BenchmarkingRed-teamingRegression tests
◉
Context Packs
Structured intelligence packages for AI agents - processed, annotated, and grounded. Turn external intelligence into reliable agent-ready context.
RAGAgent workflowsTool calling
What we are not

A clear lane.

RedSpeaker is a purpose-built AI data layer. Not a data broker, not a risk product, not an OSINT platform.

✕ Not this
Raw data broker
We don't sell unstructured feeds or bulk scrapes. Everything we deliver is structured, labeled, and task-aligned.
✕ Not this
OSINT / risk scoring
We are not a threat intelligence or risk scoring product. Our output is AI training and evaluation data - not decision intelligence.
✕ Not this
Generic data marketplace
We don't catalog off-the-shelf datasets. Every package is built specifically for your model task, from definition to delivery.
Get started

Discuss your AI data gap.

Tell us your AI task. We'll tell you how we'd close the gap - and how fast.