Palladium vs. delight.ai: a deterministic core vs. a consumer memory platform
Both are AI concierge platforms. They're built for different jobs. Palladium is a deterministic-core, audit-first system built specifically for banks, credit unions, health systems, and insurers. delight.ai is a broader consumer-CX memory and personalization platform, built by Sendbird, aimed primarily at retail, travel, and consumer subscription brands.
Where the two platforms are actually built differently
This isn't a "who's better" table. The two products optimize for different things — read it as which one matches the job you're hiring for.
| Capability | Palladium | delight.ai |
|---|---|---|
| Core design | Deterministic core makes bounded decisions; the model explains within fixed rules. | LLM-driven agent with configurable behavior (AOPs) and a memory layer across sessions. |
| Primary focus | Regulated, high-stakes decisions — eligibility, waivers, claims, compliance questions. | Consumer relationship-building — personalization, proactive outreach, revenue lift. |
| Audit trail | Sealed, deterministic trail: rule fired, source document, inputs evaluated — built in by design. | Trust OS provides observability, guardrails, and tracing for governance teams. |
| Human escalation | Structural boundary: anything outside the ruleset routes to a person automatically. | Unified AI+human Desk for handoff; escalation is workflow-configured, not architecturally fixed. |
| Target industries | Financial services, healthcare, insurance, legal/compliance, government, regulated retail. | Retail, travel & hospitality, on-demand, logistics, with financial services and healthcare as newer verticals. |
| Personalization & memory | Context is scoped to what's needed to answer accurately and traceably, not for cross-session personalization. | Agent Memory Platform (AMP) builds a persistent profile across sessions and channels — a core differentiator for delight.ai. |
| Delivery model | Deployed and supported directly by Sphere, alongside add-ons scoped to specific internal or customer-facing functions. | Self-serve and enterprise tiers via Sendbird's existing CX infrastructure (7B+ conversations/mo claimed scale). |
The strongest case for delight.ai, and where we're built differently
We think the honest comparison is more useful than a one-sided one. Here's where delight.ai genuinely leads, and where Palladium's architecture takes a different bet on purpose.
Consumer memory and scale
- Agent Memory Platform builds a persistent customer profile across chat, SMS, email, and voice — genuinely differentiated for consumer relationship-building.
- Proactive, revenue-oriented outreach (Omnipresent) is native to the platform, not bolted on.
- Runs on Sendbird's existing infrastructure at very large claimed scale — 7B+ conversations/month, 99.9%+ uptime.
- Broad customer base across consumer brands (Hinge, Hyundai, Redfin, Noom, Weight Watchers) shows real production maturity in that segment.
Determinism and audit-by-design for regulated decisions
- The core that makes a decision is rule-based and versioned, not a model producing a rationale after the fact.
- The audit trail is a structural property of the architecture, not a monitoring layer added on top of an LLM agent.
- Escalation to a human is a fixed boundary defined by the ruleset, not a configurable workflow that depends on getting the configuration right.
- Purpose-built for the specific compliance and governance burden of banks, credit unions, health systems, and insurers — not a general CX platform extended into regulated verticals.
Two different jobs to hire for
Choose Palladium if...
You're a bank, credit union, health system, insurer, or another regulated institution that needs customer- or employee-facing AI where every decision must be traceable to a specific rule and source document — and where a regulator or auditor could reasonably ask you to prove it.
delight.ai may fit better if...
You're a consumer retail, travel, subscription, or on-demand brand focused primarily on personalization, proactive engagement, and revenue lift across a large consumer base, where audit-by-design isn't the primary constraint shaping your buying decision.
Questions worth asking us, delight.ai, or anyone else
If a regulator asks why a specific decision was made, what do you show them?
A confidence score or a generated rationale isn't the same as a deterministic trail back to the rule and source document that actually produced the decision. Ask to see a real trace, not a description of the feature.
Which decisions is the AI authorized to make alone, and who decided that boundary?
Every vendor claims "human in the loop." Ask for the specific, documented list of decision types the system handles autonomously, and what happens at the edge of that list.
Is your platform built for our industry's compliance burden, or extended into it?
A platform built first for consumer retail and extended into financial services or healthcare will carry design assumptions from that original use case. Ask what changed architecturally, not just what compliance certifications were added.
What happens when the underlying policy changes?
Ask whether rule changes are versioned and logged with an approval trail, or whether an update just goes live with no record of what changed or why.
See the deterministic core, live
Bring a real use case from your institution and we'll show you the decision trace, source document, and escalation path end to end.