Aigensei

Through Model Context Protocol the platform connects to any live data source, including CRMs, knowledge bases, ticketing systems, pricing engines and ERP systems. Workflows can call any tool, including RAG knowledge bases, MCP servers connected to live systems, or external agents.

Coverage comes from the protocol, not a fixed connector catalog, and the platform acts on real data in real time instead of cached answers. No individual integration product is named, and there is no connector catalog, count or configuration guide.

Live integrations already run inside partner products, including a partner CMS, a customer support platform and a behavioral health product.

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Each request runs through a seven step execution architecture, starting with request intake. A planning agent then interprets intent and designs the execution path, deciding which workflows apply, what information is needed and how to sequence the steps. Workflows configured in advance take control, with defined steps, conditional logic and decision branches. They call tools such as RAG knowledge bases, MCP servers or external agents, and their results go into a shared memory layer.

A specialized Writer Agent formats the output in the correct tone, structure and brand voice, and the loop closes by delivering a response or downstream action. The process uses three specialized agent roles. One configuration handles thousands of parallel process instances. Aigensei says nothing about looping, parallel steps within one process, or failure and retry handling.

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RAG knowledge bases are a first class tool that workflows call in step four of the architecture, alongside MCP servers connected to live systems and external agents. The platform acts on real data in real time, not cached answers or information that was true yesterday. That gives it both a persistent retrieval corpus and live queries into CRMs, ticketing systems, pricing engines and ERP.

Retrieval follows an explicit plan, with the planning agent deciding what information is needed before workflows retrieve it. Aigensei does not say how a knowledge base is created, what formats it ingests, how it is indexed or refreshed, whether answers carry citations, or who maintains it.

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Precision human escalation hands work to a person with full context, with escalation thresholds set by workflow, segment or risk. Every process runs within boundaries the organization defines, and the platform decides when to bring a person in. There is no runtime approval step, checkpoint, pause for confirmation, queue of pending actions or reviewer approval surface. Execution closes with a delivered response or downstream action. Approval flows come as an Operations and Approvals playbook that customers configure, not as a platform primitive.

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The infrastructure is aligned with SOC 2, but Aigensei claims no audited attestation. It names no SOC 2 type, observation period or audit firm, and has no trust page or way to request a report. Customer facing controls include role based access controls with granular permissions at every level of the organization, data residency controls, full audit trails that log every AI action and the full decision chain, and explainable decisions. Aigensei targets healthcare, financial services and insurance and treats security as a design constraint.

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Every action, decision and branch is logged, traceable and explainable, with no black boxes and no gaps in the record. Full audit trails cover every AI action and the full decision chain, not just outcomes. The execution architecture is built to close a business process with every decision logged, every action traceable and every escalation precise. Explainable decisions are a separate property alongside logging, meant for a compliance team asking what the AI did and why. There is no published example audit record, and no field list, retention period, export path, dashboard or query surface.

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A shared memory layer stores all results, retrieved data and decisions so every later step has full situational awareness. Shared memory across all workflow steps gives each agent full context of the session. That memory lasts for a single process run across the seven step loop, persisting across steps but not across sessions.

State does not carry past the closed loop, and one configuration runs thousands of independent parallel process instances. Aigensei also says the platform learns from outcomes over time and improves as it operates without manual retraining, but it names no store, retention policy, per customer memory or mechanism behind that.

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Data residency controls keep the customer's data where they need it, alongside role based access controls and audit trails. The platform also embeds into partner products, including a CMS, a customer support platform where it runs live through an Aigensei widget, and a behavioral health platform. There is no self hosted, private cloud, virtual private cloud or on premises option, and no region list. The customer controls where data resides but not where the software runs.

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The Playbooks section offers six playbooks, each with its own page. They cover Sale Qualification, Customer Service Resolution, Onboarding and Intake, Operations and Approvals, HR and People Operations, and Finance and Compliance. Aigensei does not prescribe a workflow.

Customers configure their own playbook, and every workflow follows the customer's steps, logic and decision criteria instead of a generic template. The demo invitation asks prospects to share their highest priority playbook for Aigensei to walk through.

Aigensei does not say whether the six playbooks are starting configurations customers can adopt or worked examples of processes it configures.

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Every process enters at request intake, the first step of the execution architecture. A form submission, an inbound message, an API trigger or a scheduled event can each start one. Inbound messages also arrive through an embedded widget inside a partner's customer support product, where the platform runs live. There is no webhook configuration or event subscription mechanism, and no cron or recurrence syntax for scheduling.

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No model provider is named. There is no model picker, bring your own key arrangement, endpoint configuration, per workflow model setting or routing between models. The seven step architecture has a planning agent and a Writer Agent, and Aigensei does not say what models power them. Workflows can call external agents as tools, so customers can route work to agents they control. The model behind the platform's own agents is still not the customer's choice.

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An API trigger is one of four ways a request enters the execution architecture, so an outside system can start a process programmatically. There is no published endpoint, authentication method, API reference, SDK, developer documentation or developer portal. The MCP integration and external agent calls run the other way, with the platform calling out to live data sources. Partner products, including a CMS and a customer support platform, embed Aigensei under commercial arrangements, not through public developer access.

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Customers configure a playbook and the platform executes it. There is no preview, sandbox, simulation, test run, scoring, test set, expected outputs, version comparison or debugging view. Through continuous improvement the platform learns from outcomes over time and gets better as it operates without manual retraining. That loop is internal, and customers get no result they can read or compare. The full audit trail records what happened without scoring correctness, replaying cases or comparing configurations.

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Workflows call RAG knowledge bases, Model Context Protocol servers connected to live systems and external agents, all of them programmatic interfaces. The platform connects to any live data source through MCP. There is no browser control, navigation, form filling, screen interaction, scraping, visual grounding, desktop automation, code execution or web access tool. The widget in a partner's customer support platform places Aigensei inside another product's interface, and Aigensei does not operate that interface.

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