/labsv1.3.0
all systems operational
TR-001 / 2026-01
Technical Brief
Generally available
frontier AI research & eng.
§ 0.0 — Manifesto

Human intelligence infrastructure for frontier AI.

2,000,000+ verified professionals on the wire
n_total
2.0M+
registered_professionals
n_advanced
500k+
advanced_degree_holders
n_domains
100+
professional_categories
ttl_deploy
<24h
avg_expert_deployment
// summary

AIApply Labs is a programmatic interface to 2,000,000+ verified professionals, researchers, and domain experts — exposed as endpoints for RLHF, evaluation, data generation, red teaming, and expert sourcing. Built for AI research teams that need human judgement at training-loop latency.

POST /v1/evals● live
$ curl https://api.aiapply.labs/v1/evals \
  -H "Authorization: Bearer $LABS_KEY" \
  -d '{ "task": "reasoning_v3",
        "n": 1024,
        "experts": "phd_physics" }'

{ "job":   "ev_8d4a…",
  "raters": 64,
  "status": "queued" }
§ 1.0 · Thesis
three constraints · one re-framing
// premise

Frontier models are bottlenecked by access to high-quality human expertise — not compute, parameters, or synthetic data.

bottleneck.detected
human judgement
priority: P0
affects: every domain a frontier model now touches
C1
Synthetic data has a ceiling

Self-generated corpora compound their own errors. No new signal enters the loop.

C2
Crowd labor is low-signal

Unverified raters produce noise on tasks that require credential or expertise.

C3
Internal teams don't scale

Research staff can't span medicine, law, finance, code, science, language at depth.

// re-framing

AIApply Labs operates the human layer of model development as infrastructure — typed inputs, typed outputs, defined SLAs, observable quality metrics, programmatic access.

§ 2.0
Capabilities

Six endpoints. One human-feedback runtime.

POSTlabs.rlhf·p50: 4h · p95: 22h

Reinforcement learning from human feedback

Preference ranking, supervised fine-tuning, and reward modeling pipelines sourced from credential-verified experts.

inputs
  • · model_outputs[]
  • · rubric
  • · expert_pool
outputs
  • · preference_pairs
  • · reward_signals
  • · rationales
§ 3.0
System properties

What separates AIApply from a labeling vendor.

P1

Live network

2M professionals already active on platform. No recruiting lag, no cold-start sourcing.

P2

Credentialed pool

Education, licensure, employment history, and skills verified before tasking.

P3

AI-native runtime

API-first, typed schemas, webhooks, batch + streaming jobs, observability hooks.

P4

Sub-24h deployment

Median expert panel assembly in under 24 hours including calibration tasks.

§ 4.0
Network distribution

Network distribution by professional domain.

source: internal verified roster · last updated: 2026-01-15 · n=2,000,247

#
domain
share_of_pool
n
01
technology
22.5%
450,000
02
business
17.5%
350,000
03
engineering
15.0%
300,000
04
healthcare
11.0%
220,000
05
creative
10.5%
210,000
06
science
9.0%
180,000
07
finance
8.5%
170,000
08
legal
6.0%
120,000
Σ
total
2,000,000
§ 5.0
Sample data

Try a real sample. Pick a domain — we’ll send 500 expert-authored examples.

JSONL · provenance-tagged · rater metadata included · no NDA required

labs.aiapply.dev— /samples
aiapply@labs ~$ curl -O sample.jsonl --interest=?
What are you interested in?
↑↓ navigate1–5 select↵ confirm
§ 6.0
Applications

Deployed across the model lifecycle.

UC-01[eval]
Model evaluations
→
UC-02[rlhf]
RLHF & preference ranking
→
UC-03[eval]
Agent testing
→
UC-04[eval]
Code review
→
UC-05[eval]
Reasoning verification
→
UC-06[data]
Scientific analysis
→
UC-07[data]
Financial research
→
UC-08[data]
Medical knowledge tasks
→
UC-09[eval]
Enterprise workflow evaluation
→
UC-10[safety]
Red teaming & safety
→
§ 7.0
Pipeline

Expert → dataset → model improvement.

00
source_experts
Query 2M-professional pool by domain, credentials, availability.
01
qualification
Skill assessments, calibration tasks, identity verification.
02
task_assignment
Matching engine routes by expertise, language, latency target.
03
quality_review
Multi-rater overlap, inter-rater κ, expert adjudication.
04
dataset_generation
Provenance-tagged JSONL with rater metadata and rationales.
05
model_improvement
Continuous evaluation loop wired to your training infra.
  experts ──▶ qualify ──▶ assign ──▶ review ──▶ dataset ──▶ model
              │                       │           │           │
              └── calibration ────────┴── κ ──────┘           └── eval loop
§ 8.0 · Access
frontier AI research & eng. only
// request

Request research access.

/docs ↗
01 · scoping
Technical scoping call

30 min with a research engineer.

02 · sandbox
Sandbox API keys

Hit every endpoint in < 1h.

03 · calibration
Calibration batch

Run on your eval suite.