Datarails vs Vena (2026)

FinanceOS connects to more than 600 systems and data sources, from ERPs, CRMs and HR platforms to banks, billing systems and spreadsheets, and keeps them synchronized and consolidated. The AI works on what arrives. Spend codes, matches and reconciles invoices and card transactions against ERP entries, Desk pulls invoice, payment, vendor and customer records into each request, and the assistant drafts a journal entry in the conversation for a person to check and post. Corrections and payments stay inside Datarails, and no agent posts a change back into the ERP or CRM by itself.

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Source data comes into Vena's central database through prebuilt native integrations, Microsoft Fabric connections and open import APIs, with export APIs for an extra fee, and Vena sits inside Microsoft Excel, Teams, Power BI, PowerPoint and Dynamics 365 Business Central. The agents work on that data in Vena and in the workbook.

The Planning Agent writes driver based budgets into Excel templates, the Reporting Agent builds reports and charts with drill down, and changes made through Vena MCP in Excel can be written back to forecasts and scenarios. None of them changes a record in an ERP, CRM or HR system.

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The strategy, planning and reporting agents have merged into one general assistant, which asks clarifying questions, offers analysis options, confirms the key decisions and then builds the output, so one request can run from a data pull to a finished Excel model and deck.

Task agents sit inside the screens where the work happens, such as flux analysis and journal entries in the close. Workflows route budgets, forecasts and reports through the right reviewers, Month-End Close assigns tasks with deadlines across the close, and Desk routes each finance request by team, type and priority with SLA timers.

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A visual workflow builder maps a whole process, from budget input through review, approval, upload and distribution, automates report sends and task reminders and notifies each person when their step comes up, and the same builder runs compensation planning and financial close management. Inside a single request the Analytics Agent breaks a variance, trend, comparison or ranking question into several steps, shows each one and asks when context is missing, and the Planning Agent turns one plain language request into a structured, driver based budget aligned to the CubeFLEX model.

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Insights sends AI written summaries on the schedule a team sets, weekly, monthly or quarterly, to the people it chooses, and automated agents rerun recurring analyses without being asked. Desk takes finance requests from email, Slack, Teams and other channels into one queue, where the AI gathers the records and drafts an answer. People also reach the agents and the assistant in the web app, and outside assistants reach the data through the AI Connector.

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People reach Vena AI in Excel, in the web app and in Microsoft Teams, where it can sit in a meeting as a private chat, and anyone in the business with a Vena AI license can ask the Reporting Agent for a report. Recurring work runs through skills and scheduled automations in the customer's own AI tools connected by Vena MCP, which can send proactive alerts and repeat an analysis on Vena data. Vena's own agents start when someone asks, and none of them wakes on a new close, a data load or a threshold.

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Agents work on FinanceOS, a governed data model that consolidates, maps and harmonizes data across entities, currencies and systems, with mappings, allocations and version control kept by the finance team. A Semantic Layer defines business metrics such as revenue, gross margin, headcount, ARR and DSO once for use across Datarails, and the assistant says whether a number came from a governed metric or an aggregation of the tables. Every answer can drill from a summary to departments, accounts and transactions.

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Vena AI reads the live CubeFLEX model the team already maintains, with no extra setup, and inherits its data, application and per user permissions.

Vena Omega, the context engine underneath, adds each company's operating model, fiscal calendar, chart of accounts, entity structure, cost centers, historical performance, KPIs and terminology, industry considerations and seasonality, and the stage and dependencies of the planning and close cycle.

Answers drill down to the data behind each number, and the calculations stay deterministic on the governed model while the language model reasons over them.

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Every conversation with the assistant is saved automatically for the user who had it, across all areas of the product, and nobody else sees it. Each conversation runs as a session on Anthropic's infrastructure, which holds its history, files and outputs, so it falls outside Anthropic's zero data retention option. A team can also keep company profile documents, such as board presentations or financial models, that agents and outside assistants read for context, and the team reviews and updates them itself. An agent does not write down what it learned from a conversation for next time.

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The Analytics Agent, in open beta, builds context about each user's role, preferences and business from what people share with it. Admins save organization wide context, each user can add personal instructions and preferences that carry into later conversations, and Vena Omega tunes the depth, framing and format of an answer to the user's role and permissions. How long that context lasts, and how a user reviews or clears it, is left open.

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Before creating or changing anything, the assistant states exactly what will change and waits for the user's OK, so nothing is created or changed without approval. A journal entry arrives in the conversation as a draft for the user to check, and a person posts it, never the agent. In Spend, agents work under defined controls and approval requirements, so a person approves before any payment, and invoices with discrepancies wait for review. Desk drafts each reply for a team member to review and send.

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Budgets move through Vena workflows with review and approval steps and a notice to each approver in turn. The Analytics Agent shows how it reached an answer, flags missing context or uncertainty and asks before it fills a gap, and the user can correct a GL account, period or assumption and rerun. Every output is the customer's to review under Vena's AI terms, and agent output itself has no approval step of its own before it lands in a workbook.

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Datarails holds a SOC 2 Type II report and a SOC 1 report, both available on request from its trust center. Sign in supports SAML 2.0 single sign on with Okta, Microsoft Azure, Duo, Google Workspace and JumpCloud, with multifactor authentication. Access follows roles (Super Admin, Admin, Contributor and Viewer) and permission groups down to the entity, department or report, the AI tools read only what the user is already allowed to see, and authorization is checked again on every request.

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Deloitte performs Vena's annual SOC 1 and SOC 2 Type II audits, both available on request, and a SOC 3 report is public. Vena is a Cloud Security Alliance Trusted Cloud Provider and takes part in TRUSTe's privacy certification program.

Customers can turn on SAML 2.0 single sign on, multifactor authentication and IP restrictions, and role based access sets per user permissions on sensitive data, which the agents inherit.

Each customer gets its own database instance, data is encrypted at rest with AES 256 under keys held in AWS Key Management Service and in transit with TLS 1.2 or higher, and Vena staff reach production only through federated sign in with MFA and a VPN.

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FinanceOS keeps audit trails and full data lineage on the numbers, and the close records approvals and every change. Super Admins get an audit log of outside assistant sessions on the AI Connector, listing each session's member, AI platform, time, duration and status with a timeline of its events, while a durable record of each tool call is still being built. Conversations with the in app assistant stay visible only to the user who had them, so an admin has no record of an agent session to open later.

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Audit trails record every document, spreadsheet, budget, report, template and cell level change with date, time and user, with version histories and rollback. Admins get a view of every question put to Vena AI and every response, and answers marked wrong can be audited. The Analytics Agent shows the steps and logic behind each answer so a user can inspect what it used, and visual workflow tracking shows where each step of a process stands.

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Datarails runs on Microsoft Azure in the United States, Canada and the UK, and each customer's environment sits in one of those regions, with the application database following the same Azure location. When a user works with the assistant, some data leaves Datarails for Anthropic's infrastructure, in a region left unnamed. The AI Connector is processed in the United States, with routing by region under evaluation and no delivery date. The platform is a hosted service with no customer hosted edition.

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Vena Hubs run in AWS or Azure data centers in Canada, the United States or the EU, and each customer chooses its hub region to meet residency rules and keep transfers fast. Nightly backups go to Amazon S3 or Azure Blob Storage across several regions in the same geography, so data stays in that geography, and current data is always stored redundantly. Vena AI runs on Azure OpenAI Service, and Vena does not promise that its AI features are available in every region. There is no edition a customer can host itself.

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Agents sit ready in the screens where finance work happens, each switched on for an account separately: flux analysis and journal entry drafting in the close, a month end close assistant, cash transaction matching and an intercompany reconciliation assistant. Spend adds agents for invoice extraction and coding, transaction classification, matching and exception checks, and Desk adds an assistant that drafts answers to finance requests. Teams working in Claude get a Datarails plugin with ready made finance skills that produce Excel workbooks and PowerPoint decks.

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Three agents are ready for different jobs. The Planning Agent drafts driver based budgets and forecasts in Excel from plain language, aligned to the CubeFLEX model. The Reporting Agent lets anyone in the business request ad hoc reports and charts with drill down, which teams edit together in Excel Live inside Teams.

The Analytics Agent, in open beta with general availability expected later in October 2026, works through variance, trend, comparison and ranking questions in steps. A Scenario Agent for modeling cost, pricing, M&A and timing decisions across entities is announced and not yet available.

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The assistant and the agents built into Datarails screens run on Anthropic's Claude models through Anthropic's Claude Managed Agents platform, and other AI features use OpenAI models, directly and through Azure OpenAI. A customer cannot switch its agents to another provider or model. Teams that prefer Claude or ChatGPT can also query Datarails data through the AI Connector, but that is their own assistant reading Datarails.

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Vena AI runs on OpenAI models through Microsoft Azure OpenAI Service, under Microsoft's code of conduct for that service, combined with the CubeFLEX model, and customer data is never used to train public models. A customer cannot move its agents to another provider or model. Claude Cowork, ChatGPT Work and other assistants can work with Vena data through Vena MCP, but those are the customer's own tools, not a choice of model for Vena's agents.

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The FinanceOS AI Connector is a hosted MCP server with OAuth 2.0 and PKCE sign in per user and a public tool list for discovering tables, reading metrics, running filtered queries and aggregations. Every connector tool is read only for now, so an outside agent can analyze Datarails data but not change it. Any Datarails customer can also pull its tables over an OData feed at no extra cost, and the Data Gateway Service API accepts CSV and Excel uploads into Fileboxes from other systems.

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Open platform APIs move data into Vena, with import APIs included in every plan and export APIs for an extra fee. Vena MCP gives supported AI tools such as Claude Cowork and ChatGPT Work read access to ledger, transaction and operational data under Vena's permissions, and changes made through it in Excel can be written back to forecasts and scenarios. Vena's AI terms bar calling its AI features by any route other than the ones Vena provides, and the MCP endpoint and full tool list are not public.

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Custom AI workflows built under the AI Transformation package are tested against real data by a Datarails forward deployed engineer before they go live, which is a service, not something a team runs itself. Datarails' AI terms leave it to the customer to review outputs for accuracy before relying on them, and within the product a finance team cannot score an agent's answers or try it on known questions before rolling it out.

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The Complete plan adds a sandbox environment to test, refine and validate new features and data sets before end users see them. Users give feedback on responses as they arrive, wrong answers can be audited, and admins can write natural language rules for phrases the AI misreads, while the Analytics Agent lets a user fix an input and rerun an analysis. None of this scores agent answers against a test set, so a team judges agent quality by checking results itself.

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Agents work on FinanceOS data and produce Excel files, decks, PDFs and widgets inside Datarails. Data from other systems arrives through connectors and uploads, and when Claude works in Excel the Datarails add in carries out refreshes and drill downs rather than an agent reading the screen. None of Datarails' agents operates another application's screen for a user.

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The agents work inside Vena's model and the Excel, Teams and Power BI surfaces Vena connects to, and data from other systems arrives through integrations and APIs. None of them takes over another application's screen for a user.

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