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RapidOS is an open-source WhatsApp claims-intake assistant paired with a staff dashboard. Its creator, Raph Mwanza, describes a workflow that collects claim details and photos, checks extracted information against a customer’s messages, and asks the customer to confirm a draft before creating a numbered claim. It is an intake tool—not an automated claims decision or payout system—and the account is a first-person description, not independent evidence of insurer deployment, security audits, accuracy, or production results.
What problem is RapidOS designed to address?
Mwanza frames the problem as an informal process: someone messages a familiar insurer contact and sends photos, but details may be lost or later re-entered manually. His example of an accident in Kinshasa illustrates that scenario; it should not be read as a measured statistic about claims or WhatsApp use in the DRC. RapidOS aims to turn that conversation into a structured record for an insurer’s claims team.
The author describes the insurer using its own WhatsApp Business number and AI-provider credentials. Customers can provide information in a supported language, while staff manage claims and conversations through a dashboard. The software is identified as AGPL-3.0 licensed, with a Docker Compose deployment path. These are project details reported by its creator, not independently verified implementation or operational outcomes. Mwanza’s project account is the primary source for the features described here.
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The described installation separates messaging, staff operations, and data storage into services:
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- Go API: receives Meta webhook events, manages conversations with the configured AI provider, and sends WhatsApp replies.
- Next.js dashboard: lets staff manage claims, customers, conversations, users, and settings, and generates claim PDFs.
- PostgreSQL and Redis: PostgreSQL stores persistent application data; Redis supports rate limiting.
- Migration container: a one-shot service runs database migrations before the other services start.
Mwanza says one installation can support multiple companies, each with its own WhatsApp number, AI provider, coverage types, team, and roles. He also says WhatsApp tokens and AI keys are encrypted and masked in the interface. Those are author-reported design claims; the account does not establish that the system has undergone an independent security review.
What happens during a claim conversation?
- The insurer configures intake requirements. Staff define coverage types and the fields and documents needed for each kind of claim.
- The assistant gathers details. For each conversation turn, the model receives an extraction tool based on the configured fields. The application carries a partial claim draft forward and asks for missing information.
- The application checks extracted values. Mwanza says it compares extracted values with the customer’s actual messages and drops unsupported values before saving or displaying them. This is the author’s description of a safeguard, not a published accuracy benchmark.
- The customer reviews the assembled draft. Once the required information is collected, the assistant reads it back and waits for the customer to reply YES, NO, or CANCEL.
- A confirmed intake becomes a staff record. After confirmation, the system creates a numbered claim, attaches submitted photos, generates a PDF, and makes the record available to staff.
The design distinction is important: collecting and organizing information is not the same as deciding whether a policy covers an event or authorizing a payment. The described workflow leaves claim handling with the insurer’s team.
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Why unsupported details should stay blank
A model can produce plausible-looking information that a customer never supplied. Mwanza’s implementation rationale is captured in his statement: “In insurance, a made-up policy number is worse than a missing one.” He describes checking extracted values against messages rather than filling gaps with invented policy details. He also says uncertain dates and corrections are handled by replacing related vehicle fields together, rather than leaving a potentially inconsistent combination. The account does not report measured extraction accuracy.
How does the WhatsApp connection work?
The described setup uses each insurer’s WhatsApp Cloud API credentials and a webhook registered in Meta. Mwanza says incoming webhook POSTs are checked against X-Hub-Signature-256 using HMAC-SHA256, acknowledged quickly, and then dispatched for processing. He also describes per-conversation ordering and duplicate-message suppression to handle concurrent or repeated events.
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Meta’s WhatsApp Business Platform and Cloud API examples include webhook, media-message, and signature-validation examples. They establish relevant integration patterns, but do not independently confirm RapidOS’s implementation. For new integrations, distinguish the Cloud API from the former On-Premises API: Meta’s On-Premises deployment-template repository says that API is deprecated and directs developers to Cloud API.
Which languages are supported, and how well are they reviewed?
The author lists 15 assistant and dashboard languages. The list is not evidence that each language has been equally tested or reviewed.
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- Reported as reviewed: English and French.
- Marked for community review: Portuguese, Spanish, Arabic, Swahili, Lingala, Hausa, Yoruba, Amharic, Hindi, Bengali, Vietnamese, Indonesian, and Filipino. The author describes these 13 languages as machine-translated.
PDFs are described as translated into English, French, Spanish, Portuguese, and Swahili. For other languages, the author says PDFs currently fall back to English. An insurer evaluating the tool should therefore check both conversational language quality and the language of the documents its staff and customers receive.
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How can staff oversee or take over a conversation?
Mwanza says staff can see every conversation, pause the bot, and reply themselves. While staff are actively handling a conversation, automatic status updates are held. Staff can also change a claim’s status and send the customer an update with a revised PDF. These controls describe a human handoff path; the account does not provide independent evidence of how they perform in live insurer operations.
What should an insurer verify before relying on it?
The project description is useful as an implementation walkthrough, but it does not establish insurer adoption, production performance, security audit results, or measured operational benefits. The author’s account supplies no benchmark for extraction accuracy, time saved, cost, or language quality. Before relying on any claims-intake system, an insurer should evaluate the parts that affect its own obligations and workflow:
Quick Recap
- Data control: establish where the application and its data will be hosted, who can access them, and how credentials and customer records are protected.
- Language quality: test real intake conversations and generated documents in the languages the insurer plans to support, including the community-review languages.
- Company separation: validate that company-specific numbers, providers, coverage configuration, staff access, and records remain appropriately separated in a multi-company installation.
- Human correction: confirm that staff can inspect, correct, pause, and take over conversations, and that customers can reject or cancel a draft.
- Webhook behavior: test signature validation, retries, duplicate events, ordering, media handling, and recovery when a downstream service is unavailable.
- Operational evidence: request relevant deployment, security, and workflow testing evidence rather than treating a feature description as proof of production readiness.
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