What

Giga is an enterprise AI agent platform for customer support. It is designed to help support teams automate complex service interactions across chat, voice, and multimodal channels, with an emphasis on policy control, customization, and large-scale conversation handling.

The product appears positioned for larger organizations with complex support operations, multilingual needs, and strict workflow requirements. Its core workflow centers on building an agent, grounding it in company policies and documents, testing and launching it, then using performance insights to refine policies and improve support outcomes over time.

Features

Agent Canvas for agent building and governance — Provides a structured workflow to create, define policies for, test, launch, monitor, and improve enterprise AI agents.
Policy-grounded customization — Lets teams fine-tune agent behavior around brand standards, compliance rules, and internal workflows so responses stay consistent and controlled.
Document-based training context — Supports attaching files and training documents to give agents business-specific context for handling support interactions.
Built-in Copilot and auto policy writing — Helps teams start from existing transcripts and generate policy improvements, which can reduce setup effort when designing support logic.
Smart Insights for root-cause analysis — Reviews transcripts and tests hypotheses at scale to identify support patterns, knowledge gaps, and policy changes that may improve KPIs.
Natural voice experience — Offers personalized voices, low-latency responses, and handling for tone, accents, interruptions, and rapid conversational turns in voice support.

Helpful Tips

Evaluate this product primarily on governance depth and policy control, since those appear central to its value for enterprise support environments.
Before deployment, ensure internal teams can provide clean support transcripts, policy documents, and workflow definitions, as these likely shape agent quality and setup speed.
Use a pilot focused on a specific high-volume, rules-driven support workflow to validate deflection, resolution quality, and escalation handling before broader rollout.
Review how the platform’s insight and improvement loop fits your operating model; the strongest value likely comes from ongoing policy optimization rather than one-time agent deployment.
If voice is a priority, test real-world performance on interruptions, multilingual scenarios, and emotionally sensitive conversations, since these are areas the product highlights.