๐๐๐
๐๐น๐ผ๐ฐ๐ธ: 6 years. 3 chapters. One focus: We stopped chasing โmoreโ and doubled down on what ships.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ญ โ ๐๐ป ๐๐ต๐ฒ ๐๐ฟ๐ฒ๐ป๐ฐ๐ต๐ฒ๐ (๐ฎ๐ฌ๐ญ๐ต โ)
4 years collecting, transcribing, and labeling speech + text across 100+ languages and real-world noise.
We delivered projects for ๐๐ผ๐ฟ๐๐๐ป๐ฒ ๐ฑ๐ฌ๐ฌ companies and tech unicorns and learned: quality is a system, not a promise.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ฎ โ ๐ช๐ฒ ๐ฏ๐๐ถ๐น๐ ๐๐ต๐ฒ ๐๐๐๐๐ฒ๐บ
We asked a bigger question: what if we could build the infrastructure serious AI teams need?
Backed by an ๐๐จ ๐ถ๐ป๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ, we built data engines, training and deployment tools, distributed computing, workflow automation, and self-hosted deployments.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ฏ โ ๐ง๐ผ๐ฑ๐ฎ๐: ๐ฐ๐น๐ฎ๐ฟ๐ถ๐๐
AIxBlock is an ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฎ๐ฟ๐๐ป๐ฒ๐ฟ for speech and large language models.
We deliver datasets for training, fine-tuning, and evaluationโbuilt for privacy, provenance, and production-grade quality.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ญ โ ๐๐ป ๐๐ต๐ฒ ๐๐ฟ๐ฒ๐ป๐ฐ๐ต๐ฒ๐ (๐ฎ๐ฌ๐ญ๐ต โ)
4 years collecting, transcribing, and labeling speech + text across 100+ languages and real-world noise.
We delivered projects for ๐๐ผ๐ฟ๐๐๐ป๐ฒ ๐ฑ๐ฌ๐ฌ companies and tech unicorns and learned: quality is a system, not a promise.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ฎ โ ๐ช๐ฒ ๐ฏ๐๐ถ๐น๐ ๐๐ต๐ฒ ๐๐๐๐๐ฒ๐บ
We asked a bigger question: what if we could build the infrastructure serious AI teams need?
Backed by an ๐๐จ ๐ถ๐ป๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ, we built data engines, training and deployment tools, distributed computing, workflow automation, and self-hosted deployments.
๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ ๐ฏ โ ๐ง๐ผ๐ฑ๐ฎ๐: ๐ฐ๐น๐ฎ๐ฟ๐ถ๐๐
AIxBlock is an ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฎ๐ฟ๐๐ป๐ฒ๐ฟ for speech and large language models.
We deliver datasets for training, fine-tuning, and evaluationโbuilt for privacy, provenance, and production-grade quality.
๐3๐3๐ฏ3๐2๐ฅ1
Most teams donโt lose time on modeling. They lose time on data that doesnโt match the spec.
When we work with speech / LLM teams, we run with 4 non-negotiables:
1. Fit-to-spec beats generic. Some models need messy, real-world coverage. Others need scripted prompts, clean reads, or strict scenario design. We deliver the right mix based on what your model actually needs.
2. Quality is a system, not a checkbox. Every project runs with clear guidelines, gold standards, multi-tier review, and automated checksโmeasurable, repeatable, scalable across languages.
3) Subject Matter Expert judgment belongs in the loop.
When tasks arenโt โgeneric labeling,โ we bring domain experts to design rubrics, define edge cases, create gold examples, and audit outcomes so the dataset reflects real domain truth, not crowd guesswork.
4. Privacy isnโt a policy page. Itโs architecture.
When required, we support architectural exclusivity: data flows straight into your storage from day one.
When we work with speech / LLM teams, we run with 4 non-negotiables:
1. Fit-to-spec beats generic. Some models need messy, real-world coverage. Others need scripted prompts, clean reads, or strict scenario design. We deliver the right mix based on what your model actually needs.
2. Quality is a system, not a checkbox. Every project runs with clear guidelines, gold standards, multi-tier review, and automated checksโmeasurable, repeatable, scalable across languages.
3) Subject Matter Expert judgment belongs in the loop.
When tasks arenโt โgeneric labeling,โ we bring domain experts to design rubrics, define edge cases, create gold examples, and audit outcomes so the dataset reflects real domain truth, not crowd guesswork.
4. Privacy isnโt a policy page. Itโs architecture.
When required, we support architectural exclusivity: data flows straight into your storage from day one.
๐ฏ6โค3๐3๐3๐ฅ2
If youโre building speech AI or LLM features, โwe need dataโ is too vague.
Hereโs a cleaner way to think about AIxBlockโs products โ based on what your model actually needs:
โ Audio & Speech Data (custom, end)
Scripted or spontaneous voice, any accent, verbatim transcription with timestamps and diarization. Optional IPA and emotion labels.
โก Sound & Environment Audio
Real-world non-speech audio for detection and classification: ambience, industrial, household sounds, acoustic scenes, and events.
โข Text Data for LLMs (multilingual)
Conversation annotation, intent/entity labeling, SFT prompt-response pairs, RLHF data, plus safety and evaluation.
โฃ OTS Call Center Audio (ready to license)
Large-scale real call audio when you need to train now.
โค Self-hosted platform (when governance matters)
Deploy on your own infrastructure for sovereignty, compliance, and auditability.
Sourcing data now? Share your constraints and weโll suggest the fastest path.
Hereโs a cleaner way to think about AIxBlockโs products โ based on what your model actually needs:
โ Audio & Speech Data (custom, end)
Scripted or spontaneous voice, any accent, verbatim transcription with timestamps and diarization. Optional IPA and emotion labels.
โก Sound & Environment Audio
Real-world non-speech audio for detection and classification: ambience, industrial, household sounds, acoustic scenes, and events.
โข Text Data for LLMs (multilingual)
Conversation annotation, intent/entity labeling, SFT prompt-response pairs, RLHF data, plus safety and evaluation.
โฃ OTS Call Center Audio (ready to license)
Large-scale real call audio when you need to train now.
โค Self-hosted platform (when governance matters)
Deploy on your own infrastructure for sovereignty, compliance, and auditability.
Sourcing data now? Share your constraints and weโll suggest the fastest path.
๐ฅ5๐3โค2๐1๐ฏ1
Free training data is usuallyโฆ not training-ready
So weโre doing something different:
AIxBlock is releasing ๐ฟ๐ฎ๐ฟ๐ฒ, ๐ต๐ถ๐ด๐ต-๐พ๐๐ฎ๐น๐ถ๐๐ ๐ข๐ง๐ฆ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐๐ for AI training โ ๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ.
What youโre getting:
โ Off-the-shelf datasets you can use immediately
โก Meticulous collection + labeling by our in-house data team
โข Scale support from ๐ด๐น๐ผ๐ฏ๐ฎ๐น ๐๐ผ๐ฟ๐ธ๐ณ๐ผ๐ฟ๐ฐ๐ฒ ๐ผ๐ณ ๐ญ๐ฌ๐ฌ,๐ฌ๐ฌ๐ฌ+ ๐ฐ๐ผ๐ป๐๐ฟ๐ถ๐ฏ๐๐๐ผ๐ฟ๐ across countries
These datasets were previously part of our ๐ฝ๐ฟ๐ถ๐๐ฎ๐๐ฒ, ๐ฝ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐ฑ๐ฎ๐๐ฎ ๐ฎ๐๐๐ฒ๐๐ (some sold for ๐บ๐ถ๐น๐น๐ถ๐ผ๐ป๐ ๐ผ๐ณ ๐ฑ๐ผ๐น๐น๐ฎ๐ฟ๐).
Now weโre releasing them as a gift to the open-source AI communityโbecause access to world-class data shouldnโt be gated.
Want the list?
๐๐ผ๐บ๐บ๐ฒ๐ป๐ โ๐๐๐ง๐โ and weโll DM it to you.
So weโre doing something different:
AIxBlock is releasing ๐ฟ๐ฎ๐ฟ๐ฒ, ๐ต๐ถ๐ด๐ต-๐พ๐๐ฎ๐น๐ถ๐๐ ๐ข๐ง๐ฆ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐๐ for AI training โ ๐ณ๐ผ๐ฟ ๐ณ๐ฟ๐ฒ๐ฒ.
What youโre getting:
โ Off-the-shelf datasets you can use immediately
โก Meticulous collection + labeling by our in-house data team
โข Scale support from ๐ด๐น๐ผ๐ฏ๐ฎ๐น ๐๐ผ๐ฟ๐ธ๐ณ๐ผ๐ฟ๐ฐ๐ฒ ๐ผ๐ณ ๐ญ๐ฌ๐ฌ,๐ฌ๐ฌ๐ฌ+ ๐ฐ๐ผ๐ป๐๐ฟ๐ถ๐ฏ๐๐๐ผ๐ฟ๐ across countries
These datasets were previously part of our ๐ฝ๐ฟ๐ถ๐๐ฎ๐๐ฒ, ๐ฝ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐ฑ๐ฎ๐๐ฎ ๐ฎ๐๐๐ฒ๐๐ (some sold for ๐บ๐ถ๐น๐น๐ถ๐ผ๐ป๐ ๐ผ๐ณ ๐ฑ๐ผ๐น๐น๐ฎ๐ฟ๐).
Now weโre releasing them as a gift to the open-source AI communityโbecause access to world-class data shouldnโt be gated.
Want the list?
๐๐ผ๐บ๐บ๐ฒ๐ป๐ โ๐๐๐ง๐โ and weโll DM it to you.
โค6๐ฅ2๐ฏ2๐1๐1๐1
๐๐ต๐ฟ๐ถ๐๐๐บ๐ฎ๐ ๐ด๐ถ๐๐ฒ๐ฎ๐๐ฎ๐ ๐ ๐๐ถ๐บ๐ถ๐๐ฒ๐ฑ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐ ๐ฑ๐ฟ๐ผ๐ฝ
Weโre sharing a dataset pack we donโt usually publish. Only available for the holiday giveaway.
Christmas giveaway ๐ ๐ฅ๐ฎ๐ฟ๐ฒ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐ ๐ฑ๐ฟ๐ผ๐ฝ
Not a โlink you can find anywhere.โ
Weโre only sharing this pack during the holidays.
If you work on ASR / SpeechLMs, you already know: most โfree speech datasetsโ arenโt training-ready.
This one is: ๐ต๐ญ,๐ณ๐ฌ๐ฒ ๐๐ฟ๐ฎ๐ป๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ ๐บ๐ฎ๐ฝ๐ฝ๐ฒ๐ฑ ๐๐ผ ~๐ญ๐ฌ,๐ฑ๐ฌ๐ฌ ๐ต๐ผ๐๐ฟ๐ ๐ผ๐ณ ๐ฟ๐ฒ๐ฎ๐น ๐ฐ๐ฎ๐น๐น-๐ฐ๐ฒ๐ป๐๐ฒ๐ฟ ๐ฎ๐๐ฑ๐ถ๐ผ.
1. Real call-center conversations
2. Scale that matters ~๐ญ๐ฌ,๐ฑ๐ฌ๐ฌ ๐ต๐ผ๐๐ฟ๐ worth of transcripts.
3. ๐ต๐ญ,๐ณ๐ฌ๐ฒ ๐๐ฆ๐ข๐ก transcript files
4. Word-level timestamps included
5. ASR confidence scores included
6. PII carefully redacted
7. ๐ง๐ฎ๐ด๐ด๐ฒ๐ฑ ๐ฏ๐ ๐ฑ๐ผ๐บ๐ฎ๐ถ๐ป, ๐๐ผ๐ฝ๐ถ๐ฐ, ๐ฎ๐ฐ๐ฐ๐ฒ๐ป๐. So you can benchmark properly.
โ
Want the dataset list + access details? Comment โDATAโ and weโll DM it. If youโre building ASR/SpeechLMs: whatโs the #1 dataset gap you keep hitting?
Weโre sharing a dataset pack we donโt usually publish. Only available for the holiday giveaway.
Christmas giveaway ๐ ๐ฅ๐ฎ๐ฟ๐ฒ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐ ๐ฑ๐ฟ๐ผ๐ฝ
Not a โlink you can find anywhere.โ
Weโre only sharing this pack during the holidays.
If you work on ASR / SpeechLMs, you already know: most โfree speech datasetsโ arenโt training-ready.
This one is: ๐ต๐ญ,๐ณ๐ฌ๐ฒ ๐๐ฟ๐ฎ๐ป๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ ๐บ๐ฎ๐ฝ๐ฝ๐ฒ๐ฑ ๐๐ผ ~๐ญ๐ฌ,๐ฑ๐ฌ๐ฌ ๐ต๐ผ๐๐ฟ๐ ๐ผ๐ณ ๐ฟ๐ฒ๐ฎ๐น ๐ฐ๐ฎ๐น๐น-๐ฐ๐ฒ๐ป๐๐ฒ๐ฟ ๐ฎ๐๐ฑ๐ถ๐ผ.
1. Real call-center conversations
2. Scale that matters ~๐ญ๐ฌ,๐ฑ๐ฌ๐ฌ ๐ต๐ผ๐๐ฟ๐ worth of transcripts.
3. ๐ต๐ญ,๐ณ๐ฌ๐ฒ ๐๐ฆ๐ข๐ก transcript files
4. Word-level timestamps included
5. ASR confidence scores included
6. PII carefully redacted
7. ๐ง๐ฎ๐ด๐ด๐ฒ๐ฑ ๐ฏ๐ ๐ฑ๐ผ๐บ๐ฎ๐ถ๐ป, ๐๐ผ๐ฝ๐ถ๐ฐ, ๐ฎ๐ฐ๐ฐ๐ฒ๐ป๐. So you can benchmark properly.
โ
Want the dataset list + access details? Comment โDATAโ and weโll DM it. If youโre building ASR/SpeechLMs: whatโs the #1 dataset gap you keep hitting?
โค7๐7๐6๐6๐ฅ3๐ฏ1
This question is trending on Reddit: Why do many LLMs struggle inside enterprises?
Models and tools matter. RAG and fine-tuning help access knowledge. But what we see in production is that models still lack workflow and edge-case context without domain-native training data.
This is where AIxBlock works.
#AIxBlock #LLMTrainingData #EnterpriseAI #AIData #LLMOps
Models and tools matter. RAG and fine-tuning help access knowledge. But what we see in production is that models still lack workflow and edge-case context without domain-native training data.
This is where AIxBlock works.
#AIxBlock #LLMTrainingData #EnterpriseAI #AIData #LLMOps
โค4๐ฅ3๐2๐1
๐ก๐ฒ๐ ๐ฌ๐ฒ๐ฎ๐ฟ ๐๐ถ๐๐ฒ๐ฎ๐๐ฎ๐ ๐ ๐๐ถ๐บ๐ถ๐๐ฒ๐ฑ ๐ฑ๐ฟ๐ผ๐ฝ
Weโre dropping a ๐๐ฅ๐๐ ๐ง๐ต๐ฎ๐ถ ๐ฐ๐ฎ๐น๐น-๐ฐ๐ฒ๐ป๐๐ฒ๐ฟ ๐ฐ๐ผ๐ป๐๐ฒ๐ฟ๐๐ฎ๐๐ถ๐ผ๐ป๐ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐.
Swipe for whatโs inside.
โ
Comment โ๐ง๐๐๐โ and weโll DM the dataset details for free.
Follow ๐๐๐ ๐๐น๐ผ๐ฐ๐ธ for more dataset drops.
Weโre dropping a ๐๐ฅ๐๐ ๐ง๐ต๐ฎ๐ถ ๐ฐ๐ฎ๐น๐น-๐ฐ๐ฒ๐ป๐๐ฒ๐ฟ ๐ฐ๐ผ๐ป๐๐ฒ๐ฟ๐๐ฎ๐๐ถ๐ผ๐ป๐ ๐ฑ๐ฎ๐๐ฎ๐๐ฒ๐.
Swipe for whatโs inside.
โ
Comment โ๐ง๐๐๐โ and weโll DM the dataset details for free.
Follow ๐๐๐ ๐๐น๐ผ๐ฐ๐ธ for more dataset drops.
โค3๐3๐2๐1
๐๐ฎ๐ฝ๐ฝ๐ ๐ก๐ฒ๐ ๐ฌ๐ฒ๐ฎ๐ฟ ๐๐
2026 starts with clarity.
High-performing models start with high-quality data.
AIxBlock is now all in on ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ณ๐ผ๐ฟ ๐๐ฝ๐ฒ๐ฒ๐ฐ๐ต ๐ฎ๐ป๐ฑ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐.
2026 starts with clarity.
High-performing models start with high-quality data.
AIxBlock is now all in on ๐ฒ๐ป๐๐ฒ๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ณ๐ผ๐ฟ ๐๐ฝ๐ฒ๐ฒ๐ฐ๐ต ๐ฎ๐ป๐ฑ ๐น๐ฎ๐ฟ๐ด๐ฒ ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐.
โค3๐2๐2๐ฅ1๐1๐ฏ1
Dear 2026,
grant me the patience to answer โ๐๐ต๐ฒ๐ฟ๐ฒ ๐ฑ๐ถ๐ฑ ๐๐ต๐ถ๐ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ผ๐บ๐ฒ ๐ณ๐ฟ๐ผ๐บ?โ
for the 47th time (with real provenance, not vibes),
the courage to share a ๐ฝ๐ฟ๐ผ๐ฝ๐ฒ๐ฟ ๐๐ฎ๐บ๐ฝ๐น๐ฒ ๐ฝ๐ฎ๐ฐ๐ธ (raw + cleaned) without over-polishing,
and the discipline to write ๐น๐ฎ๐ฏ๐ฒ๐น๐ถ๐ป๐ด ๐ด๐๐ถ๐ฑ๐ฒ๐น๐ถ๐ป๐ฒ๐ + ๐ค๐ ๐ฑ๐ผ๐ฐ๐ like a grown-up.
If itโs not too muchโฆ
may all enterprise buyers in 2026 share a ๐ฐ๐น๐ฒ๐ฎ๐ฟ ๐๐ฐ๐ผ๐ฝ๐ฒ + ๐๐ถ๐บ๐ฒ๐น๐ถ๐ป๐ฒ without โweโll get back to you ASAP.โ ๐๐
Amen
#AIData #EnterpriseAI #DataQuality #DataGovernance #Procurement
grant me the patience to answer โ๐๐ต๐ฒ๐ฟ๐ฒ ๐ฑ๐ถ๐ฑ ๐๐ต๐ถ๐ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ผ๐บ๐ฒ ๐ณ๐ฟ๐ผ๐บ?โ
for the 47th time (with real provenance, not vibes),
the courage to share a ๐ฝ๐ฟ๐ผ๐ฝ๐ฒ๐ฟ ๐๐ฎ๐บ๐ฝ๐น๐ฒ ๐ฝ๐ฎ๐ฐ๐ธ (raw + cleaned) without over-polishing,
and the discipline to write ๐น๐ฎ๐ฏ๐ฒ๐น๐ถ๐ป๐ด ๐ด๐๐ถ๐ฑ๐ฒ๐น๐ถ๐ป๐ฒ๐ + ๐ค๐ ๐ฑ๐ผ๐ฐ๐ like a grown-up.
If itโs not too muchโฆ
may all enterprise buyers in 2026 share a ๐ฐ๐น๐ฒ๐ฎ๐ฟ ๐๐ฐ๐ผ๐ฝ๐ฒ + ๐๐ถ๐บ๐ฒ๐น๐ถ๐ป๐ฒ without โweโll get back to you ASAP.โ ๐๐
Amen
#AIData #EnterpriseAI #DataQuality #DataGovernance #Procurement
๐4๐ฅ2๐2๐ฏ2
๐จ Data labeling isnโt dead - itโs leveling up.
The โeasy taggingโ work is getting automated.
Whatโs in demand now: ๐ฑ๐ผ๐บ๐ฎ๐ถ๐ป-๐ฎ๐๐ฎ๐ฟ๐ฒ ๐ต๐๐บ๐ฎ๐ป ๐ท๐๐ฑ๐ด๐บ๐ฒ๐ป๐ for Speech + Conversational AI.
At AIxBlock, we donโt run generic click-tasks. We run ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ, ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ณ๐ฎ๐ฐ๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ designed around how modern voice/LLM systems are trained and evaluated.
๐ข๐ฝ๐ฒ๐ป ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐๐ฝ๐ฒ๐:
๐ญ. ๐๐๐ฑ๐ถ๐ผ ๐ฅ๐ฒ๐ฐ๐ผ๐ฟ๐ฑ๐ถ๐ป๐ด & ๐ง๐ฟ๐ฎ๐ป๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป
Native-language speech + transcription
๐ฎ. ๐ง๐ฒ๐ ๐ & ๐๐ถ๐ฎ๐น๐ผ๐ด๐๐ฒ ๐๐ป๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป
Tag intents/entities + label outcomes
๐ฏ. ๐๐๐ฑ๐ถ๐ผ ๐๐ผ๐น๐น๐ฒ๐ฐ๐๐ถ๐ผ๐ป
Capture voices/environment sounds to spec
๐ฐ. ๐๐ ๐๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฅ๐๐๐
Rank outputs + give structured feedback
If youโre an expert in your domain and you care about quality, ๐๐ฒ ๐ต๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ด๐ฟ๐ฎ๐ฑ๐ฒ ๐๐ ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐ณ๐ผ๐ฟ ๐๐ผ๐.
The โeasy taggingโ work is getting automated.
Whatโs in demand now: ๐ฑ๐ผ๐บ๐ฎ๐ถ๐ป-๐ฎ๐๐ฎ๐ฟ๐ฒ ๐ต๐๐บ๐ฎ๐ป ๐ท๐๐ฑ๐ด๐บ๐ฒ๐ป๐ for Speech + Conversational AI.
At AIxBlock, we donโt run generic click-tasks. We run ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ, ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ณ๐ฎ๐ฐ๐ถ๐ป๐ด ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ designed around how modern voice/LLM systems are trained and evaluated.
๐ข๐ฝ๐ฒ๐ป ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐๐ฝ๐ฒ๐:
๐ญ. ๐๐๐ฑ๐ถ๐ผ ๐ฅ๐ฒ๐ฐ๐ผ๐ฟ๐ฑ๐ถ๐ป๐ด & ๐ง๐ฟ๐ฎ๐ป๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป
Native-language speech + transcription
๐ฎ. ๐ง๐ฒ๐ ๐ & ๐๐ถ๐ฎ๐น๐ผ๐ด๐๐ฒ ๐๐ป๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป
Tag intents/entities + label outcomes
๐ฏ. ๐๐๐ฑ๐ถ๐ผ ๐๐ผ๐น๐น๐ฒ๐ฐ๐๐ถ๐ผ๐ป
Capture voices/environment sounds to spec
๐ฐ. ๐๐ ๐๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฅ๐๐๐
Rank outputs + give structured feedback
If youโre an expert in your domain and you care about quality, ๐๐ฒ ๐ต๐ฎ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ด๐ฟ๐ฎ๐ฑ๐ฒ ๐๐ ๐ฑ๐ฎ๐๐ฎ ๐ฝ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐ณ๐ผ๐ฟ ๐๐ผ๐.
โค3