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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 Edilen6.6K Takipçiler
Giga retweetledi
Growth has traditionally meant hiring more people, adding more software, or launching more campaigns.
GigaML's Scout takes a different approach by focusing on the conversion point that matters most to your business. It keeps running experiments, learning from results, and compounding thousands of small improvements into meaningful business outcomes!
Giga@GigaAI
Introducing Scout. Tell it the KPI you care about, like funded deposits, and it builds the agents, learns from every conversation, tests each change, and keeps improving that number on its own. You set the goal. Scout gets you there.
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Scout can help you set your goals with plain natural language, so even greg can use Giga
greg@greg16676935420
I have no idea what a KPI is and at this point I’m too afraid to ask Does anyone know what it even stands for?
English
Giga retweetledi
The future is heading here: agents configuring agents. Expertise, even of this meta kind, is becoming accessible through AI.
Giga@GigaAI
Introducing Scout. Tell it the KPI you care about, like funded deposits, and it builds the agents, learns from every conversation, tests each change, and keeps improving that number on its own. You set the goal. Scout gets you there.
English
Giga retweetledi
Autoresearch is cool until you start climbing the wrong hills. Every business is different, and the hills you climb are unique. Giga’s KPIs lets you climb a hill which matters to your business.
Read more at giga.ai/scout
Giga@GigaAI
Introducing Scout. Tell it the KPI you care about, like funded deposits, and it builds the agents, learns from every conversation, tests each change, and keeps improving that number on its own. You set the goal. Scout gets you there.
English
@GigaAI Can Scout optimize multiple KPIs at the same time?
English
English
Every company tracks dozens of dashboards. The next step is software that continuously improves the numbers on them.
GigaML's Scout lets you define success in plain English, then builds, tests, learns, and adapts around that metric.
Every iteration is measured against the outcome your business actually values, creating a system that keeps getting stronger over time.
Giga@GigaAI
Introducing Scout. Tell it the KPI you care about, like funded deposits, and it builds the agents, learns from every conversation, tests each change, and keeps improving that number on its own. You set the goal. Scout gets you there.
English
@randomrecruiter And with our hallucination correction, we'd fix the mistakes anyway :)
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Honored to join the list!
Harmonic@harmonic_ai
❗New Harmonic Hot 25❗ @resolveai joins @Lovable as the only two startups to top the Harmonic Hot 25 twice! This comes after a $40M April extension of their Series A, this time at a $1.5B valuation. Up 50% form the February round. Thousands of investors use Harmonic every day. These are the startups they’re watching most closely as we approach Q3. harmonic.ai/hot-25-startup…
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❗New Harmonic Hot 25❗
@resolveai joins @Lovable as the only two startups to top the Harmonic Hot 25 twice!
This comes after a $40M April extension of their Series A, this time at a $1.5B valuation. Up 50% form the February round.
Thousands of investors use Harmonic every day. These are the startups they’re watching most closely as we approach Q3.
harmonic.ai/hot-25-startup…
English
Giga retweetledi
lots of great startup lessons from @varunvummadi leading Giga. quickly becoming a generational company that is taking down some formidable competitors. Great having him at Startup School in India earlier this year!
Y Combinator@ycombinator
Varun Vummadi (@varunvummadi) is the co-founder of @GigaAI, which builds AI agents for customer support for some of the biggest companies in the world, including DoorDash, one of the largest crypto exchanges in the US, and a top-three global telecom provider. At Startup School India, Varun sat down with YC's @agupta to talk about why he turned down a high-paying quant job to start a company, the multiple pivots it took to find the right problem, and how their small team of eight beat a 400-person competitor to land a contract with DoorDash. 01:33 — Early Days & Origin Story 03:37 — The YC Interview Disaster 06:28 — Pivoting Away From EdTech 07:25 — Finding the Real Idea 08:39 — Beating a Well-Funded Competitor 10:00 — Winning DoorDash With 8 People 11:09 — What GigaML Looks Like Now 12:40 — Advice for College Students 15:51 — Why Charge Early? 17:33 — The Next Big Bet 18:43 — Running the Company on AI 20:19 — How They Hire Engineers 22:04 — Product Over Sales 23:37 — Burn the Boats
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Giga retweetledi
Varun Vummadi (@varunvummadi) is the co-founder of @GigaAI, which builds AI agents for customer support for some of the biggest companies in the world, including DoorDash, one of the largest crypto exchanges in the US, and a top-three global telecom provider.
At Startup School India, Varun sat down with YC's @agupta to talk about why he turned down a high-paying quant job to start a company, the multiple pivots it took to find the right problem, and how their small team of eight beat a 400-person competitor to land a contract with DoorDash.
01:33 — Early Days & Origin Story
03:37 — The YC Interview Disaster
06:28 — Pivoting Away From EdTech
07:25 — Finding the Real Idea
08:39 — Beating a Well-Funded Competitor
10:00 — Winning DoorDash With 8 People
11:09 — What GigaML Looks Like Now
12:40 — Advice for College Students
15:51 — Why Charge Early?
17:33 — The Next Big Bet
18:43 — Running the Company on AI
20:19 — How They Hire Engineers
22:04 — Product Over Sales
23:37 — Burn the Boats
English
🚨 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.
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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.”
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