We’re hiring: 𝐔𝐒-𝐁𝐚𝐬𝐞𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐇𝐂𝐂 𝐑𝐢𝐬𝐤 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐂𝐨𝐝𝐞𝐫𝐬
AIxBlock is building a contractor network of experienced 𝐇𝐂𝐂 𝐫𝐢𝐬𝐤 𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐜𝐨𝐝𝐞𝐫𝐬 for upcoming healthcare AI data projects.

𝐑𝐨𝐥𝐞 𝐭𝐲𝐩𝐞: Remote contract
𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝: Active AAPC CRC credential
𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: 2+ years in HCC risk adjustment coders
𝐑𝐚𝐭𝐞: Around $𝟑𝟎–$𝟑𝟓/𝐡𝐨𝐮𝐫, depending on experience

Coders will review medical records, validate AI-suggested diagnoses, assign ICD-10-CM codes, and document MEAT evidence under CMS V28 standards.
Because this work may involve PHI, onboarding includes credential verification, KYC, HIPAA compliance, and BAA signing.

Apply here for full details: https://aixblock.io/jobs/46

📣 𝐖𝐞’𝐫𝐞 𝐞𝐱𝐩𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐨 𝐈𝐧𝐝𝐢𝐚 🇮🇳 - 𝐅𝐚𝐜𝐞 𝐌𝐨𝐭𝐢𝐨𝐧 𝐕𝐢𝐝𝐞𝐨 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 | 𝐎𝐂𝟎𝟑
AIxBlock is recruiting 𝐟𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐞𝐫𝐬 𝐢𝐧 𝐈𝐧𝐝𝐢𝐚 to join a global 𝐅𝐚𝐜𝐞 𝐌𝐨𝐭𝐢𝐨𝐧 𝐕𝐢𝐝𝐞𝐨 collection project for a tech-giant enterprise client.
This is a one-time remote task:
a few minutes of setup + a 𝟏𝟎–𝟐𝟎 𝐬𝐞𝐜𝐨𝐧𝐝 𝐫𝐞𝐜𝐨𝐫𝐝𝐢𝐧𝐠.
𝐖𝐡𝐚𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐝𝐨
📱 Record using a 𝐥𝐚𝐩𝐭𝐨𝐩, 𝐩𝐡𝐨𝐧𝐞, 𝐨𝐫 𝐭𝐚𝐛𝐥𝐞𝐭 𝐜𝐚𝐦𝐞𝐫𝐚
👃 Move your head as if touching 𝟕–𝟖 𝐢𝐦𝐚𝐠𝐢𝐧𝐚𝐫𝐲 𝐝𝐨𝐭𝐬 on the screen with your nose
💡 Record under varying lighting conditions, either 𝐢𝐧𝐝𝐨𝐨𝐫𝐬 𝐨𝐫 𝐨𝐮𝐭𝐝𝐨𝐨𝐫𝐬
🏠 For indoor videos, a home office or office-like background is acceptable
𝐖𝐡𝐨 𝐜𝐚𝐧 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐞
✅ Must be a real human (no AI)
✅ Must be 𝟏𝟖 𝐲𝐞𝐚𝐫𝐬 𝐨𝐫 𝐨𝐥𝐝𝐞𝐫
✅ Must be located in an eligible country
We’re especially looking for contributors based in 𝐈𝐧𝐝𝐢𝐚 as we expand our collection there.

We’re especially looking for contributors based in 𝐈𝐧𝐝𝐢𝐚 as we expand our collection there.
Other eligible countries are listed in the job post.
🔗 Apply here: https://aixblock.io/jobs/42
#Hiring #IndiaJobs #RemoteWork #Freelance #AIData

🎆 𝐇𝐚𝐩𝐩𝐲 𝟒𝐭𝐡 𝐨𝐟 𝐉𝐮𝐥𝐲 𝐟𝐫𝐨𝐦 𝐀𝐈𝐱𝐁𝐥𝐨𝐜𝐤 🇺🇸
Today, we celebrate independence, freedom, and the spirit of innovation that continues to move the world forward.
At AIxBlock, we’re proud to support enterprise AI teams with a trusted, real-world data platform built around 𝐝𝐚𝐭𝐚 𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧𝐭𝐲.
Wishing everyone a safe, joyful, and meaningful Independence Day.
𝐇𝐚𝐩𝐩𝐲 𝟒𝐭𝐡 𝐨𝐟 𝐉𝐮𝐥𝐲!
#FourthOfJuly #IndependenceDay #AIxBlock #EnterpriseAI #SovereignData

AIxBlock is not a “label anything” platform.
We focus on 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐚𝐧𝐝 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈.
Speech.
Text.
Audio.
Video.
Healthcare.
Physical AI.
Custom collection.
OTS datasets.
The goal is not more data.
The goal is usable data.

“𝐓𝐫𝐮𝐬𝐭 𝐮𝐬” 𝐢𝐬 𝐧𝐨𝐭 𝐚 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲.
Enterprise AI teams need infrastructure that reduces risk by design.
That means:
client-owned storage
controlled access
secure delivery workflows
clear data paths
no unnecessary vendor-side copies
The strongest protection is not a promise.
It is architecture.
—
AIxBlock supports self-hosted data delivery for enterprise AI teams.
#SovereignData #DataSecurity #EnterpriseAI #PrivacyByDesign

𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐧𝐞𝐞𝐝𝐬 𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐝𝐚𝐭𝐚 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞.
Not just powerful models.
Not just more datasets.
But control over:
where data lives
how data flows
who can access it
whether copies are retained
how delivery is audited
For enterprise AI, 𝐝𝐚𝐭𝐚 𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧𝐭𝐲 𝐢𝐬 𝐧𝐨𝐭 𝐨𝐩𝐭𝐢𝐨𝐧𝐚𝐥.
It is the foundation for trust.
—
AIxBlock helps enterprise teams collect and deliver real-world data through self-hosted workflows.
#SovereignData #EnterpriseAI #DataGovernance #AIData

𝐘𝐨𝐮𝐫 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐭𝐫𝐮𝐬𝐭𝐞𝐝 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞.
Who sourced the data?
Who accessed it?
Where was it stored?
Was a copy retained?
Can the workflow be audited?
These questions are no longer secondary.
They are enterprise AI requirements.
—
AIxBlock helps teams source and deliver real-world data with sovereignty, validation, and control.
#SovereignData #EnterpriseAI #AIData #DataQuality

𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐨𝐧 𝐩𝐚𝐩𝐞𝐫 𝐢𝐬 𝐞𝐚𝐬𝐲.
Compliance in the pipeline is hard.
That is where enterprise AI data breaks.
A PDF can say:
data is secure
contributors are verified
quality is checked
rights are clear
copies are deleted
But enterprise buyers need more than claims.
They need systems that prove:
where data came from
who touched it
how it was validated
where it was stored
what was accepted
what was rejected
what changed over time
That is why governance has to move into the infrastructure layer.
Policies matter.
But architecture enforces.
—
AIxBlock supports audit-ready data delivery with self-hosted options, contributor verification, and layered QA workflows.
#AICompliance #DataGovernance #EnterpriseAI #AIData #DataSecurity

Healthcare AI needs more than public text.
It needs data with context, structure, and clear usage boundaries.
AIxBlock’s OTS and private data coverage can include 𝟏𝟎𝐌+ 𝐦𝐞𝐝𝐢𝐜𝐚𝐥 𝐫𝐞𝐜𝐨𝐫𝐝𝐬 𝐰𝐢𝐭𝐡 𝐜𝐨𝐫𝐫𝐞𝐬𝐩𝐨𝐧𝐝𝐢𝐧𝐠 𝐫𝐚𝐝𝐢𝐨𝐥𝐨𝐠𝐲 𝐫𝐞𝐩𝐨𝐫𝐭𝐬, depending on availability, licensing terms, and permitted use cases.
For healthcare AI, the question is not only:
“Can we access the data?”
It is:
“Can we use it responsibly?”
#HealthcareAI #MedicalAI #AIData #DataGovernance

A model trained in one clean room is not ready for the world.
The world has clutter.
And clutter changes everything.
Objects move.
Lighting changes.
People interrupt.
Backgrounds vary.
Rooms are small.
Angles are imperfect.
Tasks are not performed the same way twice.
For Physical AI, this is not “noise.”
This is the dataset.
Real-world task data needs variation across:
homes
warehouses
workplaces
factories
offices
retail spaces
outdoor environments
Because deployment does not happen in a perfect capture studio.
It happens wherever people actually perform the task.
—
AIxBlock helps teams collect Physical AI data across varied environments, layouts, lighting, objects, and task styles.
#PhysicalAI #Robotics #RealWorldAI #ComputerVision #AIData

Fraud does not happen at signup.
It happens mid-project.
That is why one-time KYC is not enough.
A contributor may pass qualification.
Then later:
share credentials
hand off tasks
use automation
submit proxy work
change devices
lower quality over time
If your only control is “we verified them once,” you are not controlling the real risk.
You are hoping it does not happen.
For high-stakes AI data, integrity has to continue during work.
That can include:
KYC where required
device checks
session controls
review workflows
behavioral monitoring
task-level QA
The goal is not to make work harder for good contributors.
The goal is to protect the dataset from bad actors.
—
AIxBlock uses multi-layer contributor verification to reduce fraud, proxy work, and identity mismatch risks.
#DataIntegrity #AIData #EnterpriseAI #DataSecurity #DataQuality

Simulation shows what should happen.
Real-world data shows what actually happens.
Physical AI needs both.
Simulation is useful because it is scalable, repeatable, and controllable.
But deployment introduces friction:
unexpected object placement
lighting variation
human hesitation
motion blur
partial occlusion
surface differences
task shortcuts
environment noise
That is where 𝐬𝐢𝐦-𝐭𝐨-𝐫𝐞𝐚𝐥 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 matters.
The question is not only:
“Did the model work in simulation?”
The better question is:
“Does it still work when real-world conditions change?”
Real-world task datasets help answer that question.
They expose whether a model can handle deployment conditions, not just ideal ones.
—
AIxBlock supports real-world Physical AI datasets for simulation-to-real validation.
#PhysicalAI #Robotics #Simulation #ComputerVision #AIData

𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐎𝐓𝐒 𝐠𝐚𝐩 𝐢𝐬 𝐧𝐨𝐭 𝐯𝐨𝐥𝐮𝐦𝐞.
It is relevance.

Enterprise teams do not struggle to find generic datasets.
They struggle to find:
real-world data
rare languages
rare domains
clear usage rights
production-like conditions
That is where OTS data becomes strategic.
Not because it is available.
Because it is hard to find anywhere else.
—
AIxBlock helps enterprise teams access selected OTS and private datasets for real AI use cases.
#OTSData #EnterpriseAI #AIData #RealWorldData