Datarails vs Pigment (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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Data reaches the model through scheduled imports from ERP, CRM, HR, data warehouse, BI, spreadsheet and file storage sources, and the agents work on that model instead of inside the source systems. Agent connectors reach outward to Notion and Slack. An admin switches a connector on, each member signs in with their own account, and each read, write or delete tool is set to always allow, needs approval or blocked. Google Drive is still to come, and a team cannot add an MCP server of its own, so agent action outside Pigment stops at those two products.

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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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In Plan & Build mode the Modeler Agent gathers context, writes a structured action plan and then carries it out across Dimensions, Lists, Metrics, formulas and data imports, asking before it switches to another mode. The Analyst Agent takes a Mission, a task written in plain language, and works through its subtasks.

It sources the data, drills into the drivers, runs its own code against the model's metrics and assembles a report of text and charts. A user can leave an agent working and come back to an unread badge on the chats that have answered. Custom Agents draw on the same analysis and documentation skills, though they cannot schedule Missions.

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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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Saving an Analyst report as a Mission makes it repeatable, and switching on Scheduling sets a start time and a cadence for later runs, with each run's output kept in the report or exported to a text Metric. The Missions tab shows every saved Mission with its run frequency. People also start the agents from the AI sidebar on any Board, from the AI Analysis button beside a Metric, Table or numeric Widget, and from a prompt typed at the top of the Workspace homepage.

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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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Answers come from the customer's live planning model, its Applications, Blocks, Metrics and Boards, and an admin can switch AI data access off on any Block that should stay out of reach. The Analyst prepares itself once per Application on the first question, a setup that takes longer the more Blocks there are.

Custom Agents carry standing knowledge a team supplies, such as process documents, glossaries, FAQs, guidelines and chosen Boards and Metrics, and users can attach spreadsheets, text and images to a chat. When a security admin allows it, the agents also search the web, optionally only on approved domains, and look inside the model first when the answer may be there.

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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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Each member keeps a private chat history with the agents, where chats can be pinned, filtered by Application and reopened to carry on from where they stopped. The Modeler remembers which mode was last used in each chat, and a saved Mission keeps an analysis so it can run again. A team that wants an agent to hold on to its conventions writes them into a Custom Agent's instructions, since chats do not build up a memory of their own.

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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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In Plan & Build mode the Modeler Agent puts its action plan up for validation and waits for a go ahead at each waypoint as it works. Auto accept lets it continue without stopping, except for deletions, which a person confirms every time.

Its changes to the model are permanent and attributed in Application History to the member who ran it, and Ask mode answers without touching anything.

Connector tools set to needs approval make the agent propose a create, edit or delete in Notion or Slack and wait for the member to confirm it, and a web search that might reveal sensitive data asks for approval first.

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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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Sign in runs through SAML 2.0 single sign on with SCIM provisioning for Okta, Microsoft Entra ID or any compliant identity provider, or through Google Login, with multifactor authentication and domain allowlisting on top.

Group based roles and fine grained data access rights decide who can see and change each piece of data, and the agents, Custom Agents and MCP sessions act only within the rights of the member using them, under separate permissions to use agents and to configure them. Pigment holds ISO 27001 certification and SOC 1 Type 2 and SOC 2 Type 2 reports, audited every year, offers a SOC 3 report, sits at CSA STAR level one and covers GDPR and CCPA.

An independent penetration test summary was refreshed in June 2026, and API keys created since May 2026 follow a structured format that secret scanning tools can catch.

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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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The Missions tab shows every Mission the agents have run, and each change the Modeler makes is recorded in Application History against the member who ran it, inside edit histories that go down to the block level. Agent outputs explain how they were reached, with the data and logic behind them, and the Modeler writes up each change as it goes. The Audit Trail API sends login, administration, security and application events to a SIEM, and admins follow AI credit use in near real time on the Plan & Usage and AI credits screens.

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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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Customer data is stored in the region the customer picks, Frankfurt in Germany or Oregon in the United States, on Google Cloud across three independent zones, with backups in multiregional buckets inside the same geography. Buyers who need French or European sovereign hosting can run Pigment on S3NS PREMI3NS, a SecNumCloud 3.2 qualified cloud operated by a Thales subsidiary and shielded from jurisdiction outside Europe. Disaster recovery is rehearsed live once a year against a six hour recovery time and a 24 hour recovery point.

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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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The Modeler, Analyst and Documentation Agents come ready to switch on for each Application, and each does its own job. The Modeler builds and maintains models, the Analyst analyzes and reports, and the Documentation Agent answers platform questions from Pigment's knowledge base. Custom Agents come with worked examples for user guidance and for FP&A, workforce and supply chain analysis, and the chat offers a library of suggested prompts. The Analyst comes with ready use cases by role, from monthly expense variance and EBITDA and cash checks to ARR cohorts, hiring gaps and supplier risk.

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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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The agents run on pretrained models from AI vendors under enterprise agreements with zero data retention, and customer data is never used to train them. Which vendors and models sit behind the agents is not public, and an admin has no setting to pick or switch the model. Claude, ChatGPT or Mistral can reach Pigment through its MCP server, which puts a team's own assistant in front of Pigment data while Pigment's agents stay on Pigment's choice.

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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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The public REST API, with a Swagger reference, imports data, exports Metric, Table and List data, returns import configurations and starts data flows, under API keys that carry their owner's access rights, IP allow lists and set rate limits, beside an Audit Logs API.

The Pigment MCP server gives each Workspace its own endpoint and signs people in with their Pigment accounts. Its default tools read and analyze, and advanced mode adds tools that create Applications, build Dimensions, Metrics and Tables, write formulas, design Views and Boards and enter data. Pigment keeps Skills files and plugins for Claude Code and Cursor in a public repository.

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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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A builder previews a Custom Agent and refines it before switching it on for members. Test & Deploy environments stage model changes, the Modeler's included, before they reach a production Application that can be locked, and in managed Applications the Modeler works in Ask mode only. Every answer can be traced to its data and logic. Nothing scores the agents' answers against a test set before rollout.

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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 act inside Pigment's own model and reach Notion and Slack through connectors. Web search returns information to them, and none of them opens a browser, drives another application's screen or works a desktop session for a user.

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