Move your team from Decagon to Ada.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in Ada — and the few things that need a manual step, walked through. You send one invite; nobody becomes the help desk, and nothing in Decagon is changed or cancelled. Want to see where you’re paying for both first? Run the free audit.
Ada's Coaching system lets CX operators flag conversations as underperforming or successful; those signals feed a continuous improvement loop that refines future agent behavior, tracked through the Performance Center dashboard (resolution rates, escalation patterns, accuracy). Decagon's Analytics and Insights module provides voice-of-the-customer dashboards, natural-language querying of conversation data (Ask AI), automated Root Cause Analysis for contact volume spikes, and AI-powered knowledge-gap suggestions. When moving from Ada to Decagon: export Ada's coaching annotations and flagged conversation data before cancelling; replicate the performance baseline (resolution rate, escalation %) in Decagon's Insights dashboard; establish Watchtower alerts in Decagon's QA module to replace Ada's manual coaching flags. When moving from Decagon to Ada: import historical conversation exports into Ada; configure Ada's Performance Center baseline metrics; set up Ada Coaching flags for any interaction types that Decagon's Root Cause Analysis was tracking.
- Warning: Ada's Coaching loop is feedback-signal-driven and does not require re-training from scratch; Decagon's improvement path relies on updating AOPs and knowledge-base content based on AI Suggestions — the feedback mechanisms are structurally different and cannot be directly imported from one platform to the other.
- Warning: Decagon's 'Ask AI' natural-language query over conversation history has no direct equivalent in Ada's Performance Center; teams switching to Ada will lose that ad-hoc query capability and must use structured filters instead.
Ada's Reasoning Engine is the proprietary intelligence layer that powers autonomous multi-step decision-making, combining LLMs with adaptive reasoning, context-driven logic, and multi-layer hallucination safeguards — it is included in the core platform rather than a separately licensed module. Decagon's QA and Testing module (Watchtower + QA Hub + Simulations + Trace View) provides always-on quality monitoring, pre-deployment simulation of thousands of synthetic conversations, step-by-step trace debugging, and autonomous failure analysis. When moving from Ada to Decagon: Ada's hallucination safeguards are opaque/proprietary — establish a baseline CSAT and escalation rate in Ada before migrating, then use Decagon Simulations to validate the new agent reaches the same threshold before go-live; use Trace View to inspect agent reasoning on edge cases that Ada's Reasoning Engine previously handled. When moving from Decagon to Ada: run your Decagon Simulation suite against representative query sets to document pass rates; after Ada go-live, monitor Ada's Performance Center for escalation spikes in the first 30 days as a proxy for regression, since Ada does not have an equivalent simulation harness.
- Warning: Ada's Reasoning Engine is a black-box proprietary system with no publicly documented simulation or pre-deployment test harness — regression testing relies on monitoring live traffic post-deployment, unlike Decagon's Simulations which run before deployment.
- Warning: Decagon Trace View exposes step-by-step agent reasoning for debugging; Ada provides no equivalent trace-level introspection tool, making it harder to diagnose specific decision failures in production.
Both platforms offer enterprise-grade security controls and zero-data-retention policies. Ada holds HIPAA, SOC 2, GDPR, and AIUC-1 certifications with a privacy-by-design architecture, independent annual penetration testing, and 99.9% uptime SLA. Decagon enforces zero-day retention with AI providers (OpenAI and Anthropic), AES-256 at rest, TLS 1.2+ in transit, automatic PII redaction via Google DLP, RBAC and SSO (Okta/Microsoft Entra), and publishes compliance artifacts at trust.decagon.ai. When migrating: request the outgoing platform's compliance documentation package (certifications, DPA, penetration test reports) before contract termination; verify the incoming platform's certifications match your legal obligations (HIPAA in particular); update internal data processing agreements to reference the new processor; confirm SSO re-integration (Ada → Decagon: map roles to Decagon RBAC; Decagon → Ada: map Okta/Entra groups to Ada's access model).
- Warning: Ada holds explicit AIUC-1 certification (an AI-specific transparency/safety standard); Decagon does not advertise AIUC-1 — verify with Decagon's sales team if this certification is required by your procurement team.
- Warning: Decagon explicitly names Google DLP for PII redaction in logs and transcripts; Ada's PII handling approach is not described in equivalent granularity — validate Ada's PII redaction controls separately if your industry has strict PII audit requirements.
Both platforms deploy a single AI agent across chat, email, and voice without separate vendor contracts per channel. When moving from Ada to Decagon: export Ada's channel configurations and intent/knowledge data via Ada's admin export tools; re-establish the same channels in the Decagon console (web widget, email inbox, voice); replicate channel-specific routing rules and escalation paths in Decagon's agent settings. When moving from Decagon to Ada: note that Ada requires a minimum of 300,000 annual conversations for eligibility before onboarding, so confirm volume qualifies; port knowledge content from Decagon's console to Ada's Conversation Hub; reconfigure any WhatsApp, SMS, Messenger, or Instagram channels that Ada supports but Decagon may route differently.
- Warning: Ada enforces a minimum of 300,000 annual customer service conversations for eligibility; Decagon does not publish a volume minimum, so smaller teams moving to Ada may be blocked at the sales stage.
- Warning: Ada explicitly lists WhatsApp, SMS, Messenger, and Instagram as supported channels; Decagon's documented channels are chat, email, and voice — confirm social-channel parity before committing to Decagon.
Ada uses Playbooks — multi-step structured workflows configured by admins that the AI agent executes for specific interaction types (order status, refund requests, etc.) with live data lookups from integrated systems. Decagon uses Agent Operating Procedures (AOPs) — natural-language instructions written by admins in the Decagon console that replace low-code flow builders. When moving from Ada to Decagon: document each Ada Playbook's branching logic and data integration points; rewrite them as plain-language AOPs in the Decagon console; use Decagon's AOP versioning to A/B test before going live. When moving from Decagon to Ada: convert each AOP into Ada's Playbook builder format, mapping natural-language steps to Ada's defined step types; reconnect live data sources (CRM, order management) to Ada's integration layer since Ada pulls data at runtime within Playbook steps.
- Warning: Ada Playbooks are built in a structured step-based builder that requires admin configuration per integration; Decagon AOPs use natural language and take effect without an engineering sprint — Ada migrations may require more technical setup effort.
- Warning: Decagon supports AOP versioning and rollback natively; Ada's Playbooks do not advertise equivalent versioning, so test Ada Playbook changes in staging before production rollout.