Giga
AI voice and chat support agents that chase a metric — resolution rate, escalation rate, or CSAT — not just answer calls.
Giga is one of the few support-AI vendors whose pitch is a number on a dashboard rather than a chat window, and the control-group experiment loop is the reason to take a meeting. DoorDash's 90%+ Did We Resolve Rate is the proof point the vendor leads with, and a support leader who owns resolution rate will recognise the shape of the product immediately. The tradeoff is commitment: the improvement engine needs call volume, historical tickets and someone accountable for the KPI to have anything to work with. If you want an untuned FAQ bot live today, look at lighter text-first tools instead.
Verified 5d ago · liveness 58/100 · cite: rightaichoice.com/tools/giga
- Enterprises with high call volume where resolution rate is a tracked KPI
- Regulated fintech, banking and healthcare support teams that need auditable AI
- Global support orgs running multilingual voice queues
- Ops teams that want to test changes on live traffic before full rollout
- Small teams with low call volume that won't generate enough data to optimise against
- Buyers who want an out-of-the-box chatbot running on day one with no tuning
- Text-only helpdesk teams already satisfied by AI replies inside Zendesk or Intercom
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Skip Giga if you want a low-tuning chatbot live this week, or if your support org has no resolution-rate or CSAT baseline and too little call volume for control-group testing to mean anything.
Voice minutes are the cost centre in any voice-agent platform — duration, concurrency and telephony leg charges decide the bill far more than seats do, so model them against your real average handle time before
Giga is priced for support organisations that already run at volume — think mid-market to enterprise contact centres with voice queues in several languages and a leader whose bonus depends on resolution rate. The economics only work once call volume is high enough to amortise configuration, transcript ingestion and the experiment loop across a large conversation base. Teams below a few thousand monthly conversations will find lighter text-first agents cheaper per resolved ticket; enterprise
In short
Giga — AI voice and chat support agents that chase a metric — resolution rate, escalation rate, or CSAT — not just answer calls. Best for Enterprises with high call volume where resolution rate is a tracked KPI, Regulated fintech, banking and healthcare support teams that need auditable AI, Global support orgs running multilingual voice queues. Contact Sales pricing.
What's new in Giga
Checked 6 days agoAcross the latest 7 updates: 6 feature updates and 1 launch.
Teaching a frozen LLM to see: Projector-only vision for a production support agent
Giga research details projector-only vision that adds image understanding to a frozen LLM for its production support agent.
A natural voice at every turn
Giga publishes voice research on natural-sounding turn-taking for its voice agents.
Teaching a language model to stop thinking out loud
Giga research on suppressing visible reasoning output in language models used for customer conversations.
Real-Time Synthetic Voice Detection Comes to Giga
Giga adds real-time synthetic voice detection to flag AI-generated audio on live calls.
Scheduled callbacks for Giga voice agents
Giga voice agents can now offer customers a callback at a time of their choosing and place it themselves.
Scout, now in Slack
Giga's agent-building agent Scout is available inside Slack.
Introducing the Giga CLI
Giga ships a CLI for building and operating agents straight from the terminal.
What people actually say about Giga — is it worth it?
We scanned public community sources for Giga on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Giga? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Autonomous voice agents that optimise resolution rate, CSAT and escalation rate
- Build / Observe / Improve loop across agent configuration, monitoring and testing
- Scout natural-language assistant drafts agents from policies and past tickets
- Scout available inside Slack for contextual metric explanations
- Agent Canvas no-code builder for a first multimodal agent
- Giga CLI for building and operating agents from the terminal
- Browser Agent executes workflows inside browser-based systems without APIs
- Agents take action on orders, billing, accounts and subscriptions mid-conversation
- Native voice in 99 languages without routing through translation
- Real-time hallucination correction at zero latency cost
- Real-time synthetic voice detection flagging AI-generated speech on live calls
- Scheduled callbacks offered to customers and placed by the voice agent itself
- Iterative experiments validated on live traffic against a control group
- Rules for when an agent answers, asks a question, or hands off to a human
- Conversation auditing with emotional arc and plain-English segmentation without filters
About Giga
Giga is an enterprise support-agent platform built around agents that pursue a measurable outcome rather than simply handling a conversation. You choose the objective (resolution rate, escalation rate, CSAT) and Giga runs a loop across three stages the vendor names Build, Observe and Improve: bring your policies, knowledge and tools together, watch how every conversation performs, then use Scout — the natural-language agent-building assistant — to find and test improvements. Each change is validated against a control group on live traffic before being scaled. Agents work across voice, chat and email, and can update orders, billing, accounts and subscriptions during the conversation rather than passing the request to a human. Voice is natively multilingual, and Giga layers in real-time hallucination correction plus real-time synthetic voice detection that flags AI-generated audio on live calls. Agent Canvas is the no-code builder for getting a first multimodal agent running; the Giga CLI ships and operates agents straight from the terminal, and Scout is available inside Slack. DoorDash is the lead customer story, reporting a 90%+ Did We Resolve rate across 40+ countries. Compliance covers SOC 2 Type II, ISO 42001 and ISO 27001, with positioning for fintech, banking, technology, retail, telecom, healthcare, freight and logistics, and hospitality. This fits support organisations carrying real call volume and holding a number they need to move; teams that want an untuned plug-in chatbot running on day one should look at lighter tools.
Behind the Verdict
The interesting thing about Giga is what it refuses to sell. Most support-AI vendors pitch a faster queue or a deflection percentage; Giga pitches a loop — Build, Observe, Improve — where the agent is the output of a process rather than the product. That framing shows up in the actual engineering. Scout drafts an agent from your policies, knowledge base and past tickets, learns tone from brand guidelines and historical replies, and lets you set rules for when the agent answers, asks a follow-up, or hands off. Observability runs on the same plain-English interface: ask Scout to show conversations you resolved successfully and you get a narrow slice without building filters. Then the loop closes — ranked opportunities, tests run against a control on live traffic, winners scaled once the data holds. Voice is where the recent engineering is concentrated. Agents speak natively in 99 languages rather than routing through translation, and Giga claims real-time hallucination correction at no latency cost. The 2026 research posts are unusually specific for a support vendor: projector-only vision that adds image understanding to a frozen LLM, work on natural turn-taking, and a paper on suppressing visible reasoning output in customer-facing models. Real-time synthetic voice detection, shipped August 2026, flags AI-generated speech on live calls — a defensive feature that matters more each quarter. Scheduled callbacks and a CLI round out the operational surface. Giga is not a thin wrapper over a foundation model. Vision projection, turn-taking, reasoning-output suppression and synthetic-voice detection are proprietary research contributions, and the experiment-loop machinery is real engineering. Where it doesn't fit: organisations without a resolution-rate or CSAT baseline have nothing to optimise against, and low-volume teams won't generate enough signal for the control-group testing to mean anything. Voice-first focus means text-only helpdesks already satisfied by AI replies inside Zendesk or Intercom are paying for capability they won't use. Agent Canvas and Scout lower the construction cost, but customisation still carries a learning curve, and you'll want transcripts and enterprise tooling in place before the platform earns its keep. The honest summary: strong fit for a support org with a number it has to move and the volume to move it; wrong shape for anyone shopping for day-one deflection.
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Real-world workflow fit
Concrete scenarios for the personas Giga actually fits — and what changes day-one when you adopt it.
Connects existing policies, knowledge base and historical tickets, uses Agent Canvas to stand up a voice agent for delivery-issue calls, and sets handoff rules for anything involving refunds above a threshold.
Outcome: The agent resolves routine delivery issues in the customer's language while edge cases route to humans with full conversation context attached.
Uses Scout inside Slack to interrogate a drop in CSAT, asks it to pull the conversations behind the metric, then tests a revised escalation rule against a control slice of live traffic before a full rollout.
Outcome: Policy changes ship with evidence behind them, and the audit trail covers SOC 2 Type II, ISO 42001 and ISO 27001 obligations.
Deploys and iterates agents through the Giga CLI, and enables real-time synthetic voice detection on live calls to flag AI-generated audio for trust review.
Outcome: Agent changes move through code review and CI rather than a UI, and suspected synthetic callers surface in real time instead of in a post-mortem.
Use Cases
- Run delivery-issue support end to end across voice, chat and email for a high-volume marketplace.
- Deploy a multilingual voice agent that handles queues natively in 99 languages without translation.
- Test a policy or routing change against a control group on live traffic before scaling it to everyone.
- Give a support leader a plain-English slice of conversations behind any metric drop, without building filters.
- Have voice agents offer and place their own scheduled callbacks instead of leaving the customer on hold.
- Flag AI-generated or synthetic speech on live calls for fraud and trust review.
- Let agents update orders, billing and subscriptions during the conversation rather than passing the ticket on.
Models Under the Hood
as of 2026-09-22
Limitations
- The improvement engine needs input you may not have: a defined resolution-rate or CSAT baseline, historical tickets, and enough call volume that control-group testing produces a signal.
- Low-volume teams will struggle to get value from the experiment loop.
- Voice leads the product, so text-only support organisations may be paying for capability they don't use.
- Customisation carries a learning curve even with Agent Canvas and Scout available, and the platform assumes existing transcripts and enterprise tooling to reach full potential.
as of 2026-10-02
Verification history
We have re-verified Giga 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Giga's pricing actually pencils out — and where peers do it cheaper.
Giga is priced for support organisations that already run at volume — think mid-market to enterprise contact centres with voice queues in several languages and a leader whose bonus depends on resolution rate. The economics only work once call volume is high enough to amortise configuration, transcript ingestion and the experiment loop across a large conversation base. Teams below a few thousand monthly conversations will find lighter text-first agents cheaper per resolved ticket; enterprise
Setup time & first value
How long it actually takes to get something useful out of Giga — broken out by persona, not the marketing-page minute.
Agent Canvas is pitched as a first multimodal agent in under five minutes, and that is a fair estimate for a working draft once your knowledge sources are connected. Realistic time to first measurable value is longer and depends on you: wiring policies, tools and historical tickets so Scout has tone and substance to learn from, and establishing a clean resolution-rate or CSAT baseline to improve
Switching to or from Giga
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Zendesk or Intercom AI replies: export historical tickets and macros to feed Scout, then stand up agents in Agent Canvas for the intents those replies handled.
- →From an incumbent IVR or DTMF tree: map intents to Giga voice agents and keep the existing telephony number while the new flow runs alongside.
- →From a single-language voice bot: move to native multilingual agents in 99 languages instead of maintaining per-language prompt variants.
- →From manual QA sampling: replace sample-based review with plain-English conversation segmentation and emotional-arc auditing across all conversations.
- ↗To a text-only helpdesk with built-in AI replies: export conversation transcripts and rebuild routing rules in the destination's workflow builder.
- ↗To a lighter self-serve chatbot: keep the knowledge base you assembled for Scout and re-point it at the new tool's intent model.
- ↗To an enterprise contact-centre suite: retain Giga's conversation and metrics exports for baseline comparison while re-implementing agents in the suite's own scripting language.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Giga”, and we withheld 6: 6 could not be judged, because “Giga” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Giga.
Official links
Tools that pair well with Giga
Common stack mates teams adopt alongside Giga, with the specific reason each pairing earns its keep.
Decagon
Enterprise AI concierge platform for customer support agents that work across chat, voice, and email.
PolyAI
PolyAI builds enterprise voice AI agents that handle complex customer calls in 40+ languages
ElevenLabs Conversational AI
Build voice and chat agents that hold real conversations in 70+ languages, live in about five minutes.
Featured Head-to-Head Comparisons
Giga vs Presto Voice
Choose Presto Voice if you're a QSR chain focused on drive-thru automation and upselling; its proven integration with major brands and revenue lift make it the tailored choice. Opt for Giga if you need a scalable, multilingual, human-like voice agent for enterprise customer support with strong compliance and real-time correction capabilities.
Giga vs Truleo
These tools serve completely different markets. Truleo is purpose-built for law enforcement to connect siloed data and automate lead generation, while Giga is an enterprise AI voice agent platform for customer support. Choose Truleo if you're a police department needing jail call analysis and faster report writing; choose Giga if you run a large-scale support operation needing human-like voice agents with 90% deflection.
Giga vs B Rokratt
Choose Giga if you run a global enterprise needing high-deflection voice AI with multilingual, compliance-ready agents; the latest updates confirm real-time hallucination correction and dynamic language switching. Choose Bürokratt if you're an Estonian resident looking for free, secure access to e-government services — it's purpose-built and costless. There's no overlap: one is a paid enterprise platform, the other a free public service.
Alternatives to Giga
View all
Decagon
Enterprise AI concierge platform for customer support agents that work across chat, voice, and email.
PolyAI
PolyAI builds enterprise voice AI agents that handle complex customer calls in 40+ languages
ElevenLabs Conversational AI
Build voice and chat agents that hold real conversations in 70+ languages, live in about five minutes.
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