Connect Alloy to ChatGPT: Automate KYC and Identity Decisioning

You want to connect Alloy to ChatGPT so your AI agents can triage fraud investigations, parse KYC documents, and execute identity decisioning workflows automatically. If your team uses Claude, check out our guide on connecting Alloy to Claude or explore our broader architectural overview on connecting Alloy to AI Agents.

Identity verification and fraud operations are data-heavy, deeply relational, and highly time-sensitive. Giving a Large Language Model (LLM) read and write access to your Alloy instance accelerates manual case reviews and eliminates the bottleneck of human operators sifting through risk signals.

However, building this integration from scratch requires translating Alloy's complex identity schemas into LLM tool definitions, managing API authentication lifecycles, and keeping endpoints up to date. You either spend engineering cycles building and hosting a custom Model Context Protocol (MCP) server, or you use a managed infrastructure layer. This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Alloy, connect it natively to ChatGPT, and execute compliance workflows using natural language.

The Engineering Reality of the Alloy API

A custom MCP server is a self-hosted integration layer that translates an LLM's tool calls into REST API requests. While the open MCP standard provides a predictable way for models to discover tools, implementing it against enterprise FinTech platforms like Alloy is challenging.

If you decide to build a custom MCP server for Alloy, you are responsible for the entire integration architecture. Here are the specific hurdles that break standard CRUD assumptions when working with Alloy:

Token Sprawl and Relational Chaining

Alloy's architecture relies heavily on distinct tokens linking discrete domain objects. A person or business has an entity_token. That entity is submitted to a workflow to generate an evaluation_token. If the evaluation triggers a manual review, it generates a case_token, which might be part of an overarching journey_token.

When an LLM attempts to perform a multi-step workflow, it cannot just pass an email address or a database ID. It must meticulously track and inject these specialized tokens across sequential API calls. If your MCP server does not clearly define which token is required for which schema property, the LLM will hallucinate relations and the API requests will fail.

Dynamic Workflow Schemas

Unlike a CRM where a "Contact" object has relatively static fields, Alloy's evaluations and journey applications are highly dynamic. When you run an evaluation via the API, the required payload depends entirely on how the specific workflow is configured in the Alloy dashboard. The MCP tool schema must remain flexible enough to accept dynamic evaluation data while strictly enforcing the required base parameters. Hardcoding static JSON schemas for Alloy endpoints rapidly leads to broken tool calls when workflow rules change.

Agent Attribution and Audit Trails

In the compliance space, who took an action is just as important as the action itself. When updating a document or submitting a case review in Alloy, the API frequently requires attribution - such as the note_author_agent_email. You cannot simply send an anonymous payload. Your MCP server needs a mechanism to inject the correct attribution context so that regulators and internal auditors see a clear chain of custody, even when an AI agent executes the workflow.

Rate Limits and 429 Handling

Identity checks are often subject to strict rate limits. Truto does not retry, throttle, or apply backoff logic on rate limit errors. If you hit an Alloy rate limit and the upstream API returns an HTTP 429, Truto passes that error directly to the caller. Truto normalizes the upstream rate limit information into standard IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). Your client application (or custom agent loop) is entirely responsible for detecting the 429 and implementing exponential backoff.

Step 1: Generating the Alloy MCP Server

Instead of building token-chaining logic and maintaining dynamic JSON schemas in-house, you can use Truto to instantly generate a fully authenticated MCP server. The server exposes Alloy's endpoints as AI-ready tools.

Truto derives these tool definitions dynamically based on Alloy's API documentation, meaning they are always up to date. The resulting MCP server is scoped securely to a single integrated Alloy account.

You can create the MCP server in two ways.

Method A: Via the Truto UI

For teams who prefer visual configuration:

1. Navigate to the Integrated Accounts page in your Truto dashboard.
2. Select your connected Alloy account.
3. Click on the MCP Servers tab.
4. Click Create MCP Server.
5. Select your desired configuration (e.g., restrict to read-only methods, filter by specific tags, or set an expiration date).
6. Copy the generated MCP server URL (it will look like https://api.truto.one/mcp/your-secure-token).

Method B: Via the API

For platform engineers building multi-tenant AI products, you can generate MCP servers programmatically for your users.

Make a POST request to /integrated-account/:id/mcp with your desired configuration:

curl -X POST https://api.truto.one/integrated-account/<integrated_account_id>/mcp \
  -H "Authorization: Bearer <YOUR_TRUTO_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Alloy Fraud Triage Agent",
    "config": {
      "methods": ["read", "write"]
    }
  }'

The API returns a secure URL that encapsulates the authentication context for that specific Alloy instance.

Step 2: Connecting the MCP Server to ChatGPT

Once you have the Truto MCP server URL, connecting it to ChatGPT (or custom agent frameworks) takes seconds.

Method A: Via the ChatGPT UI

If you are using ChatGPT Enterprise, Team, or Plus accounts with Developer Mode enabled:

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Enable Developer mode.
3. Under MCP servers / Custom connectors, click to add a new server.
4. Name: Enter a recognizable name (e.g., "Alloy Compliance").
5. Server URL: Paste the URL generated by Truto.
6. Save the configuration.

ChatGPT will immediately connect, perform an initialization handshake, and discover the available Alloy tools.

Method B: Via Manual Config File

If you are running a local agent environment, an IDE like Cursor, or a custom desktop client, you can connect to the remote Truto server using the standard Server-Sent Events (SSE) transport.

Add the following to your MCP configuration JSON file:

{
  "mcpServers": {
    "alloy-compliance": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "https://api.truto.one/mcp/your-secure-token"
      ]
    }
  }
}

Hero Tools for Alloy

Truto dynamically generates tools for the complete Alloy API surface. Here are the highest-leverage tools available for your AI agents to orchestrate KYC and fraud operations.

Create a Person Entity

Tool Name: create_a_alloy_person_entity

Use this tool to ingest new users into the Alloy system for KYC screening. The LLM can extract personal information from a chat session or a parsed document and structure it into an entity record. The tool returns an entity_token required for all subsequent evaluations.

"I have a new user onboarding request for Jane Doe, born 1985-05-15. Create a person entity for her in Alloy and return the entity token."

Run an Evaluation

Tool Name: create_a_alloy_evaluation

Triggers an evaluation against a configured workflow in Alloy. The LLM must pass dynamic evaluation data and the entity_token generated previously. This is the core engine for making pass/fail/manual-review decisions.

"Using the entity token from Jane Doe, run an evaluation against our standard KYC workflow. Pass in her address and email in the payload."

Create a Journey Application

Tool Name: create_a_alloy_journey_application

For complex, multi-stage onboarding, this tool submits entities to an Alloy Journey. Journeys handle sequential logic, like requiring documentary evidence if a standard SSN check fails.

"Submit entity token ent_98765 to the 'High Risk Vendor Onboarding' journey and let me know if it immediately asks for additional entities or enters a pending state."

Retrieve a Fraud Investigation

Tool Name: get_single_alloy_investigation_by_id

Allows the AI agent to pull the complete context of an open fraud investigation. The LLM can read the risk score, the journey alerts, and the associated entity tokens to analyze why the system flagged the user.

"Fetch the details for investigation inv_3342. Summarize the risk score, the specific journey alerts that triggered the investigation, and any notes attached by previous agents."

Upload an Entity Document

Tool Name: create_a_alloy_document

Attach documentary evidence (like an ID scan or proof of address) to a specific entity. The LLM orchestrates the metadata creation before the actual file binary is processed.

"Create a document record for entity ent_8832. Name it 'utility_bill_jan2026', set the extension to 'pdf', and mark the document type as proof of address."

Submit a Case Review

Tool Name: update_a_alloy_journey_application_case_review

Completes a pending manual review in a Journey Application. The LLM provides the required outcome (e.g., 'Approved' or 'Denied') along with structured reasons, effectively automating the final step of a triage workflow.

"Submit a manual review for case case_123 on journey application app_456. Set the outcome to 'Approved' and provide the reason 'Identity verified via secondary public records'."

For the complete tool inventory, request schemas, and parameter requirements, visit the Alloy integration page.

Workflows in Action

Once connected, ChatGPT can sequence these individual tools into complex, autonomous compliance operations. Here are two real-world workflows.

Workflow 1: End-to-End KYC Onboarding Triage

Instead of an operations team manually copying data from a secure form into Alloy, ChatGPT orchestrates the intake and evaluation.

"A new customer, Alex Smith (DOB 1990-11-12, email alex@example.com) applied for an account. Create a person entity for him, run an evaluation on the standard KYC workflow, and tell me the outcome. If it flags for manual review, summarize the reasons."

1. create_a_alloy_person_entity: The LLM parses the natural language, structures the JSON payload, and creates the entity, capturing the returned entity_token.
2. create_a_alloy_evaluation: The LLM injects the entity_token into the evaluation payload and executes the workflow run.
3. Analysis: The LLM reads the evaluation response. If the status_code is "Approved", it informs the user. If it requires manual review, the LLM reads the alert details and presents a triage summary.

sequenceDiagram
    participant User
    participant ChatGPT as ChatGPT (MCP Client)
    participant Truto as Truto MCP Server
    participant Alloy as Alloy API
    User->>ChatGPT: "Create person and run KYC evaluation"
    ChatGPT->>Truto: Call create_a_alloy_person_entity
    Truto->>Alloy: POST /v1/entities/person
    Alloy-->>Truto: Return entity_token
    Truto-->>ChatGPT: Return entity_token
    ChatGPT->>Truto: Call create_a_alloy_evaluation
    Truto->>Alloy: POST /v1/evaluations
    Alloy-->>Truto: Return evaluation_token & status
    Truto-->>ChatGPT: Return evaluation results
    ChatGPT-->>User: "Evaluation complete. Status: Manual Review."

Workflow 2: Investigation Analysis and Case Resolution

For Tier 1 fraud analysts, an AI agent can gather the context of an alert and propose (or execute) a resolution.

"Look up investigation inv_9001. Review the journey alerts and the attached entity data. If the only alert is a slight name mismatch but the SSN matches, submit a case review approving the application with the reason 'SSN match supersedes minor name variance'."

1. get_single_alloy_investigation_by_id: The LLM pulls the investigation object, analyzing the risk_score and journey_alerts array.
2. get_single_alloy_person_entity_by_id: Using the entity tokens found in the investigation, the LLM fetches the raw entity data to cross-check the name and SSN fields.
3. Logic execution: The LLM determines the criteria are met.
4. update_a_alloy_journey_application_case_review: The LLM formats the approval payload with the required justification and resolves the case.

Security and Access Control

Giving an AI model access to sensitive identity data requires strict guardrails. Truto's MCP servers provide granular access control out of the box:

• Method Filtering: By defining config.methods: ["read"] during server creation, you can strictly prevent the LLM from making state-changing calls. The model can fetch investigations and read entities, but cannot create cases or run evaluations.
• Tag Filtering: If Alloy resources are grouped by tags, you can restrict the MCP server to specific domains (e.g., config.tags: ["compliance_read_only"]), limiting the scope of exposed tools.
• Time-Bound Access: Provide a Unix timestamp to the expires_at field to create ephemeral MCP servers. This is perfect for granting an AI agent temporary access to resolve a specific batch of cases, automatically revoking access afterward.
• Layered Authentication: Enable require_api_token_auth to force the MCP client to pass a valid Truto API token in addition to the server URL, preventing unauthorized execution even if the URL is leaked.

Automate Compliance Safely

Building an LLM integration for an identity decisioning engine is a high-stakes engineering project. You have to handle dynamic evaluation schemas, complex token chaining, and strict audit attribution.

By leveraging Truto's managed MCP server, you abstract away the API maintenance, schema mapping, and authentication boilerplate. Your AI agents get immediate, secure access to Alloy's capabilities, allowing your engineering team to focus on building intelligent compliance workflows rather than writing REST API wrappers.