Your customer-facing bots, internal copilots, MCP clients and RPA workflows call the same Appice API your marketing team uses — and inherit the same policy, consent state, audit trail and sub-200ms latency guarantee. No agent backdoor.
POST https://api.appice.ai/v1/decision Authorization: Bearer sk_agent_... Content-Type: application/json { "customer_id": "c_84210", "context": "support_chat", "agent_id": "claude-support-v3" }
{
"action": "offer_top_up_loan",
"reason_codes": ["salary_credited",
"eligible_KFS_v4"],
"consent_ok": true,
"audit_id": "aud_9f2a..."
}
A marketer building a segment in the console and an LLM agent handling a live support chat hit the exact same decisioning core. Same features. Same policies. Same audit log. The console is a UI over the API — not a privileged path.
Marketers, analysts, ops teams and business owners build segments, tune models, launch journeys and read outcomes through the same UI.
Every agent call — MCP tool invocation, REST POST, SDK method — moves through the same four stages. The same four stages a human triggers from the console. Same latency budget, same policy engine, same audit trail.
Agent presents a scoped service-identity token. RBAC resolves what it may read and act on.
Feature store pulls the customer 360 — behaviour, entitlements, live signals, consent state.
ML + rules + policy select the next best action. Response returned with reason codes.
Appice executes (or hands the action back). Every step lands in the immutable audit log.
Every agent workload we see collapses into one of four calls. Ingest a signal. Ask for a decision. Fetch context. Execute an action. Different verbs, same Appice API, same audit.
Your customer-facing bot spots intent (“user asked about home loans”) and pushes it. Appice ingests, hashes PII, and updates the customer feature store immediately.
Your MCP-native LLM needs to answer “what should we offer this customer next?” Appice returns the next best action with reason codes and consent status — in under 200ms.
Your support copilot pulls a scoped 360 — last N events, active offers, product holdings, consent flags. RBAC controls exactly which fields the agent identity can see.
Your RPA workflow tells Appice to send the message — push, SMS, WhatsApp, email, RCS, voice. Appice enforces channel-consent, quiet hours, frequency caps automatically.
Every outcome below started with a signal — caught by a rules engine, an in-house bot, a support copilot or a background workflow — and ended with an Appice-decided action inside compliance. Different callers, same rail.
A rules engine posts /v1/signals when salary credit hits. The bank's in-app agent calls /v1/decision — Appice returns a pre-approved personal loan offer in 64ms. Consent and frequency caps apply automatically.
A background scoring workflow queries /v1/decision daily for at-risk segments — 45+ behavioural signals feed the churn model. Retention action and channel are picked per value tier, under SAMA-compliant audit trails.
An LLM-based document-help copilot detects abandonment mid-application, drafts a personalised recovery message and asks Appice for a channel-and-timing decision. Appice fires the nudge on the channel most likely to win — same session, same context.
View full case →Customer chatbots and copilots — yours or Appice-provided — run conversational flows for KYC nudges, loan application support, statement queries, claims intimation and post-discharge care — with consent state, language localisation (English, Arabic, Hindi, Portuguese, Spanish, Chinese, French) and full audit trails preserved end to end. PII never leaves the customer's perimeter, courtesy of Appice’s hashed-ID Inform layer.
Grab an API key on the developer portal, or book a 30-minute session with our solution engineers to design the integration for your agent stack.