The Human Judgment Layer Your AI Pipeline Depends On
140+ trained raters. 98.5% QA accuracy. A managed floor built for LLM evaluation, RLHF labeling, agent benchmarking, and audio scoring.
You Cannot Crowdsource Your Way to Quality Judgment
Every LLM that reaches production had humans behind it. Rating responses, flagging failures, teaching the model what good actually looks like. That pipeline does not disappear with a better model. It scales with every release.
The problem is not finding raters. It is finding raters trained to your rubric, supervised to a QA standard, and accountable to a delivery pipeline you can trust. A crowd gives you volume. A managed floor gives you quality you can ship on.
LLM-as-judge collapses on nuance. It agrees with humans on clear-cut cases and fails silently on the ambiguous ones — exactly where your model needs help most.
Six Evaluation Services. One Managed Pipeline.
Every project runs through our 4-layer QA pipeline before delivery. Self-review, peer check, lead audit, client-spec validation. No exceptions, no shortcuts.
RLHF & Preference Labeling
Pairwise response comparison and ranking for LLM fine-tuning. Raters trained on your rubric specifically. Consistent judgment at volume with full audit trails per batch.
Core ServiceAgent Benchmark Evaluation
Did the agent actually complete the task? Human scoring of multi-step completions, tool use, and decision quality where automated scoring misses real failures.
High DemandAudio & CSAT Evaluation
Human scoring of AI-generated or recorded audio for naturalness, accuracy, and customer satisfaction. Calibrated raters, structured rubrics, consistent output across long runs.
Active ProjectsLLM Output Labeling
Large-scale annotation of model outputs for quality, safety, factual accuracy, and instruction-following. Built for high-volume pipelines, turnaround targets met consistently.
Core ServiceMultilingual Evaluation
English-core pool with French, Chinese (Mandarin), and German capacity. For models being tested across EU locales or multilingual RLHF programs. EU AI Act enforcement is August 2026.
Premium LaneRed-Team & Safety Evaluation
Structured adversarial testing — hallucination, refusal failures, instruction drift, safety gaps. Structured per-session failure taxonomy reporting included in every engagement.
SpecialistFrom Rubric to Delivery in Days, Not Months
Our rater pool is trained and infrastructure is live. You share your rubric, we run the pilot, we scale what works.
Rubric Alignment
You share your evaluation criteria. We build rater training materials and run internal calibration before a single item is touched.
Free Pilot
200 to 500 evaluation items at zero cost. You see real output from real raters on your actual data before committing to anything.
4-Layer QA Review
Every output is traceable. Disagreements flagged with reasoning. Self-review, peer check, lead audit, client-spec validation on every batch.
Scale to Production
Quality confirmed, we move to volume. Pricing locked for the project duration. No rate surprises mid-pipeline, no renegotiations mid-project.
Ready to see the quality before committing?
A new RLHF project can be rubric-aligned, piloted, and in production delivery within 5 to 7 business days of first contact. No months of procurement, no NDAs before you see a single output.
A Managed Team Is a Different Product From a Crowd
Most annotation marketplaces give you access to whoever is online. You get volume. You accept inconsistency as a cost of doing business.
The managed floor difference
TechAI Remote is not a marketplace. Every rater on your project is trained on your specific rubric, reviewed by a lead auditor, and held to a 4-layer QA pipeline before a single item reaches you.
You are not buying individual raters. You are buying a quality-controlled pipeline that runs unsupervised, delivers consistently, and flags problems before they become your problem.
We are independent and conflict-free. No ties to any AI lab, no competing interests with your data. In a market where the largest annotation provider just lost its neutrality through acquisition, that independence matters.
Straightforward Pricing. No Surprises.
Every engagement starts with a free pilot. Scale from there based on what your pipeline actually needs.
- ✓Up to 500 labeled items
- ✓Your rubric, your format
- ✓48hr turnaround standard
- ✓4-layer QA pipeline applied
- ✓QA report with accuracy breakdown
- ✓JSON, CSV, or custom schema delivery
- ✓500 to 50,000+ items per batch
- ✓Dedicated project lead assigned
- ✓RLHF, agent eval, audio, red-team
- ✓Full disagreement reporting per batch
- ✓Weekly QA reports and calibration checks
- ✓NET-30/60/90 invoicing
- ✓Multilingual capacity on request
- ✓Dedicated rater pod, your pipeline only
- ✓Continuous throughput, no batch delays
- ✓All six evaluation service types
- ✓Real-time QA dashboards
- ✓Full multilingual coverage EN/FR/ZH/DE
- ✓Priority turnaround SLA
- ✓MSA and custom NDA on request
All projects start with a free pilot. Pricing varies by task type and complexity. Contact us for RLHF pair rates, audio scoring rates, or multilingual pricing.
Built for AI Teams at Every Stage
Whether you are a frontier lab, a Series B AI product company, or a managed evaluation vendor needing overflow capacity, the workflow is the same. Free pilot, confirm quality, scale to production.
Managed Evaluation Vendors
You win contracts from AI labs and need external rater pods to fulfill them. TechAI Remote operates as a sub-vendor managed pod. 140 raters, self-hosted infra, rubric-trained and QA-supervised. We slot into your delivery pipeline without disrupting it.
AI Product Companies
Series B and Series C companies building legal AI, medical AI, coding assistants, and conversational agents. You have eval budget but no internal rater org. We run your human evaluation pipeline from rubric to delivery without you building a team from scratch.
Foundation Model Labs
Multilingual RLHF, preference ranking at scale, red-team adversarial panels. English-core with French, Chinese, and German capacity. EU AI Act Article 53 enforcement is August 2026 — the window for multilingual adversarial eval preparation is now.
AI Safety Organizations
Structured adversarial evaluation, red-team panels, failure taxonomy reporting. We run systematic sessions with structured per-session output — not ad-hoc testing, but a reproducible safety evaluation pipeline with traceable results.
Start With a Free Pilot
200 to 500 evaluation items. No cost, no commitment. You see the quality from real raters on your actual data before any contract is signed.