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Giga
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Giga
@GigaAI
Reprogram each of the world’s largest companies using AI, reaching every person on Earth.
San Francisco, CA Katılım Temmuz 2023
2 Takip Edilen6.3K Takipçiler
🚨 GigaML has cut voice AI hallucination rates from 4–5% to under 1% in production without adding any latency.
The fix runs a reasoning model detector in parallel with audio playback, using the gap between text generation speed and speaking speed as the detection window.
Tested across 1.2M live conversational turns.
Giga@GigaAI
Introducing hallucination correction. We have reduced hallucination by 70%. Giga's hallucination rate is at ~1%. Better than the best frontier models. Deploy AI your customers can trust.
English
One engineering decision in GigaML's hallucination correction system is easy to overlook but changes everything: correction hints are deleted after each use.
When the detector catches a hallucination, it creates a note about what went wrong. That note is used to repair the current response. Then it is gone. It does not carry into the next turn.
In early experiments, leaving that metadata in the conversation state caused the model to start hedging on everything. "I believe..." "If I'm not mistaken..." The model was interpreting its own correction history as a reason to be less confident about all future answers. Hedging rates doubled.
This maps to well-documented research. Models that are challenged mid-conversation flip their answers nearly half the time and lose accuracy by up to 27%, even when their original answer was correct. Correction signals that persist in context degrade the turns that follow.
The fix is precise and temporary: patch the spoken output, then clear the signal. The agent corrects one error and resumes with full confidence. Across 1.2 million live turns, false positives stayed under 0.3%.
Giga@GigaAI
Introducing hallucination correction. We have reduced hallucination by 70%. Giga's hallucination rate is at ~1%. Better than the best frontier models. Deploy AI your customers can trust.
English
Giga retweetledi
“Most people fine-tune models for two reasons: cost and speed.”
@varunvummadi CEO of @GigaAI:
“Fine-tuning reduces cost, increases speed, and improves throughput.”
“Some industries like healthcare and finance also prefer it because they don’t want to rely on closed-source models.”
“But when we looked at the data, two use cases dominated: support and coding.”
“So we decided to focus on support and double down there.”
English
Giga retweetledi
🧵 Understanding this concept deeply is fundamental to what makes @GigaAI so special. It's one of the biggest reasons I joined (beyond getting the chance to work with @eshamanideep and @varunvummadi).
villi@villi
The “forward deployed” concept will cause harm to many startups that go down this path. When you are building the product, park yourself at customers’ office. But, the goal should be to build a great product that does not require you to forward deploy a person for the customer to be successful. If you solve problems with people, you will default to throwing bodies at problems and will build an inferior product. Your goal should be the opposite.
English
Giga retweetledi
Come see @eshamanideep (our CTO) talk about @GigaAI at @ycombinator HQ on December 10th! I'll be there as well.
We're hiring the most ambitious engineers across the stack to build the future of AI support agents for the largest B2C companies in the world.
Link below
English
Giga retweetledi
If you are wondering how Giga gets enterprise customers live in weeks, not months.
Check out our founders’ chat with @harjtaggar where they talk about how we turns plain English into code to deliver speed and scale at the enterprise level.
Harj Taggar@harjtaggar
Something that stunned me about @gigaai is they've moved away from the FDE playbook that's become the default for fast growing AI startups. Instead they've built AI to covert plain English from the customer into Python code to make the product work for their use cases i.e. an AI FDE. It's a huge technical feat and is how they can onboard enterprises in weeks vs months.
English
Check out @varunvummadi and @eshamanideep’s conversation with @harjtaggar, where they share how Giga’s unique approach enables unmatched speed, scale, and performance for enterprise customers like @DoorDash:
Y Combinator@ycombinator
Giga (@gigaai) is building the next generation of customer support — real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world’s largest enterprises. The team recently raised $61M to power emotionally intelligent, human-quality conversations at enterprise speed and scale. In this interview with YC's @harjtaggar, co-founders @varunvummadi and @eshamanideep share how they’re reimagining enterprise support from the ground up, what it takes to build AI for high-compliance industries, and why emotionally intelligent agents are the future of customer experience. 02:25 – What Giga Does and Who It Serves 05:10 – Building Emotionally Intelligent AI Agents 08:15 – Real-Time Responses at Enterprise Scale 11:45 – Designing for Compliance and Security 15:00 – Human-Quality Conversations at Machine Speed 18:20 – Lessons from Early Customer Deployments 22:10 – Powering the Next Generation of Support 26:45 – What It Takes to Build for the Enterprise 30:15 – The Future of Customer Experience 33:40 – Advice for Founders Building in AI
English
Giga retweetledi
Giga (@gigaai) is building the next generation of customer support — real-time AI agents that can understand emotion, resolve issues instantly, and scale across the world’s largest enterprises.
The team recently raised $61M to power emotionally intelligent, human-quality conversations at enterprise speed and scale.
In this interview with YC's @harjtaggar, co-founders @varunvummadi and @eshamanideep share how they’re reimagining enterprise support from the ground up, what it takes to build AI for high-compliance industries, and why emotionally intelligent agents are the future of customer experience.
02:25 – What Giga Does and Who It Serves
05:10 – Building Emotionally Intelligent AI Agents
08:15 – Real-Time Responses at Enterprise Scale
11:45 – Designing for Compliance and Security
15:00 – Human-Quality Conversations at Machine Speed
18:20 – Lessons from Early Customer Deployments
22:10 – Powering the Next Generation of Support
26:45 – What It Takes to Build for the Enterprise
30:15 – The Future of Customer Experience
33:40 – Advice for Founders Building in AI
English
Giga retweetledi
Voice AI startup Giga raises $61 million to take on customer service automation
fortune.com/2025/11/05/voi…
English
Giga retweetledi
We have raised a $61M Series A to automate customer operations.
The world’s leading companies like DoorDash trust Giga to supercharge customer experience with AI.
English
@andyfang Next up: we’re pushing toward 98% resolution and expanding how AI agents transform enterprise-scale customer support.
Check out the full story: giga.ai/doordash
English
As @AndyFang, Co-founder of DoorDash, put it best:
“At DoorDash, we operate at massive scale across services, platforms, and languages. Giga leveraged usage data to deliver measurable improvements — fewer escalations, faster resolution paths, and more efficient workflows as we grow globally.”
English
Excited to share our partnership with DoorDash.
Together we went from kickoff to real impact — in weeks, not quarters.
Highlights:
- Time to value: weeks, not quarters
- Quality at scale: 90%+ DWR in production
- Built for scale: 10B+ lifetime orders, 500K+ merchants, 8M+ Dashers, and hundreds of thousands of daily assistance requests
English