# Giga

**Canonical:** https://apis.io/providers/giga/  
**Website:** https://giga.ai/  
**APIs profiled:** 0

Giga (legal name Giga AI, Inc.; formerly Giga ML) is a San Francisco-based agentic AI company that builds human-like voice, chat, and multimodal AI agents for high-volume enterprise customer support. Its agents run on a unified real-time orchestration layer that listens, reasons, checks connected systems, and responds in roughly half a second, resolving issues end to end rather than merely deflecting. Agents are built in Agent Canvas, a low-code studio that auto-writes policies from uploaded transcripts, and support 90 languages with real-time hallucination correction. Backed by Redpoint Ventures, Y Combinator, and Nexus Venture Partners (USD 61M Series A); customers include DoorDash, Toast, Postman, and Capital.com.

## Kin Score — 24.0 / 100 (emerging)

Scored 2026-08-17 under rubric 0.11.0. Trend: flat (+0.0 from 24.0).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 12.5 |
| Operational Transparency | 5.3 |
| Developer Ergonomics | 30.4 |
| Commercial Clarity | 50.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (2)

- **Giga Domain Security** — TLSv1.3 · HSTS · DMARC
- **Giga Trust Center** — SOC 2, ISO 27001, PCI DSS, HIPAA, GDPR

## Tags

Company, Ai, Artificial Intelligence, AI Agents, Voice AI, Customer Support, Conversational AI, Contact Center, Agentic AI, Enterprise

---

Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/giga/). Scores are computed from the provider's own public artifacts under a published rubric.