AI & Agents

Bloomfire Alternatives: Modern Knowledge Sharing and Company Brain Platforms

Bloomfire alternatives are modern knowledge engagement and intelligence platforms designed to help distributed teams centralize documentation, search multimedia assets, and eliminate institutional knowledge silos. Bloomfire now builds a governed knowledge layer over content kept in other systems and serves it to people and AI agents through search, MCP and an API. The alternatives below range from search overlays and wikis to intelligent workspaces where the working files themselves live.

Tom Langridge
20 min read Updated
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Modern company brain platforms index documents on arrival and expose verified knowledge to people and AI agents via open protocols.

Why Organizations Re-Evaluate Bloomfire for Knowledge Management

Finding accurate operational documentation inside a growing company should not require pinging colleagues or opening conflicting browser tabs. A Gartner survey of 4,861 employees found that 47% of digital workers struggle to find the information or data they need to do their jobs effectively. When project files, technical specifications, and procedural guidance remain scattered across personal drives, chat channels, and unmaintained knowledge bases, teams waste hours searching for context or make expensive decisions based on outdated instructions.

Bloomfire alternatives are modern knowledge engagement and intelligence platforms designed to help distributed teams centralize documentation, search multimedia assets, and eliminate institutional knowledge silos.

Bloomfire established itself as an enterprise knowledge engagement platform, focusing on social knowledge sharing, question-and-answer exchanges, and multimedia search across audio and video transcripts. Organizations initially adopted Bloomfire to capture subject matter expertise in posts, centralize sales enablement materials, and establish searchable repositories for support teams. Its current platform adds an Enterprise Intelligence Engine that connects to systems such as SharePoint, Google Drive, Box, Dropbox, OneDrive, Confluence, and Salesforce without moving content, Synapse conversational search with source citations, and AI Connect, which exposes that knowledge to AI agents through MCP and an API.

The way distributed teams produce and consume information has changed. Knowledge lives directly inside active working files: architecture decision records, engineering specifications, vendor agreements, and financial spreadsheets. A platform like Bloomfire answers from a knowledge layer built over those files, while the files themselves stay in drives and other source systems. Teams then manage two places: the systems where files change, and the knowledge platform that indexes and governs them.

The adoption of autonomous AI agents has introduced an entirely new consumer of enterprise knowledge. Systems like Claude Code, Cursor, and Codex connect to knowledge through open standards like the Model Context Protocol (MCP) to read documents and, where a server allows it, write generated deliverables back.

The Maintenance Burden: Why Posts Drift from Working Files

Knowledge platforms built around posts and answers still depend on people to write those posts, even with AI help. Bloomfire's Author Assist drafts summaries, writes titles, and applies tags from source material, but someone still turns a slide deck or a report into a post and publishes it.

As organizations scale, operational bottlenecks emerge:

  • Content duplication: When an answer lives in both a post and the original file, the two can drift apart, and readers find contradictory guidance.
  • Information drift: Operational processes change in the working files first. An engineer updates a specification in a cloud drive, and any post written from it needs its own update.
  • Review load: Platforms can flag stale or conflicting posts, and Bloomfire runs automatic knowledge health checks, but each flag still needs an author to fix it.

An intelligent workspace takes a different route: it indexes the working files themselves as they arrive, so there is no separate post to keep in step with the source.

The Shift from Static Wikis to Working Company Brains

Legacy knowledge software separates where work happens from where knowledge is recorded. Employees create contracts, code, and presentations in office suites, and then someone is expected to summarize that work in a separate wiki.

A modern company brain unifies storage and retrieval. Instead of maintaining a separate social forum, an intelligent workspace treats active working files as the primary knowledge store.

When specifications, contracts, and research reports live in a shared workspace, an intelligent retrieval layer reads the content directly. Employees and autonomous AI agents query the workspace using natural language, and the system answers with citations pointing to the source files, pages, and text snippets that support each claim.

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How to Evaluate Knowledge Engagement and Company Brain Platforms

Selecting an alternative to Bloomfire requires evaluating how different platforms store documents, index content, and expose knowledge to human workers and automated systems. Traditional checklists focused on social features like upvoting, badges, and user leaderboards. Technical leaders now prioritize retrieval accuracy, cloud storage integration, and AI agent interoperability.

When evaluating knowledge engagement platforms and company brain solutions, apply these five core criteria:

  1. Search speed and indexing depth: The platform must combine exact keyword matching with semantic vector search and structured metadata extraction, indexing documents on arrival without manual tagging.
  2. Cloud synchronization and repository integration: The platform should connect directly to existing storage repositories, including Google Drive, Dropbox, Box, and OneDrive, allowing teams to index active project directories on a schedule or on demand.
  3. Permission boundaries and access governance: The system must enforce role-based access controls at organization, workspace, and share boundaries, ensuring search queries only return answers derived from authorized files.
  4. AI agent accessibility via open protocols: The platform should support standardized protocols like the Model Context Protocol (MCP), enabling AI coding agents to read source files with citations and save newly generated deliverables back to shared storage.
  5. Cost predictability and seat flexibility: The platform should offer clear, published subscription tiers with transparent limits on seats, storage, and AI processing credits, avoiding high minimum seat commitments and opaque sales negotiations.

Hybrid Retrieval: Combining Full-Text, Semantic, and Structured Metadata

Enterprise search fails when it relies on a single retrieval method. Lexical search matches exact words or stems, excelling at finding specific error codes, part numbers, or variable names. However, it fails when an employee asks a conceptual question using different terminology than the original author.

Conversely, pure semantic vector search converts text into mathematical embeddings to capture conceptual meaning. While semantic search excels at broad thematic queries, it can struggle to pinpoint exact alphanumeric identifiers or specific contract clauses.

Leading company brain platforms implement a three-part hybrid retrieval architecture:

  • Deterministic keyword search: Preserves exact lexical matching for technical terms, part numbers, and unique identifiers.
  • Semantic vector search: Maps conceptual meaning so users find relevant policies, guides, and research even when phrasing queries conversationally.
  • Structured metadata extraction: Parses unstructured documents into typed database records. Fastio accomplishes this through Metadata Views, which turn PDFs, spreadsheets, and scanned documents into typed fields (Text, Integer, Decimal, Boolean, URL, JSON, Date & Time) without manual data entry or rigid templates.

Why Autonomous AI Agents Require Native File Storage

A key limitation of traditional knowledge engagement software is that primary working files live outside the workspace where agents operate. Modern engineering and operations teams deploy autonomous AI agents like Claude Code, Cursor, Codex, and custom multi-agent workflows.

These agents do not interact with software through conversational browser interfaces or social comment boxes. They require direct, programmatic file access to execute their tasks:

  • Inspecting verified source files: The agent must read raw specifications, extract data from spreadsheets, and check factual assertions against ground-truth files with page-level citations.
  • Writing persistent artifacts: Once the agent synthesizes research, drafts a specification, or generates code, it must write those deliverables back into a persistent, versioned workspace where human colleagues can review, edit, and inspect them.

Search overlays do not host files, and wikis store pages rather than the working files, so an agent's outputs usually land apart from the sources it read. Establishing intelligent workspaces bridges this gap by giving humans and AI agents a common file substrate governed by advisory file locks, per-file version history, and shared workspaces.

How Leading Bloomfire Alternatives Compare

Evaluating alternatives to Bloomfire requires balancing social knowledge sharing against deep file indexing and active workspace collaboration. The leading platforms each serve distinct operational priorities across modern organizations.

The comparison table below evaluates the leading Bloomfire alternatives across core architectural dimensions:

Platform Core Architecture Primary Storage & Ingestion AI Agent & Developer Access Pricing Structure
Fastio Native intelligent workspace and company brain Native workspace storage; Cloud Sync connects Google Drive, Dropbox, Box, OneDrive Remote MCP over Streamable HTTP (/mcp/tools, /mcp/operations, /mcp/code), REST API, advisory file locks Transparent tiers ($29 to $299/mo); 14-day free trial on monthly plans (credit card required)
Bloomfire Enterprise intelligence engine plus a knowledge management app (posts, Q&A) Posts and uploads (25+ file types, with audio and video transcription) plus connectors to SharePoint, Google Drive, Box, Dropbox, OneDrive, and other systems that read content in place AI Connect API and MCP server; does not host the working files, so no file locks or Notes beside them Custom fixed price set by scope, with no user counts
Glean Enterprise search overlay Connects to external SaaS apps and builds its own index; does not host files Remote MCP server with search and admin-enabled tools, including actions in connected apps; no file storage of its own Not published; sold through sales
Guru AI knowledge base and verified cards Verified cards plus 100+ connected tools such as Slack, Microsoft Teams, Salesforce, Confluence, and SharePoint MCP server and Knowledge Agents over cards and connected sources rather than a hosted file workspace Custom package scoped with sales; no published prices
Notion AI Modular workspace and relational wiki Blocks, pages, and file attachments, plus AI connectors to Slack, Google Drive, SharePoint, and other apps MCP server and REST API; working files live outside pages rather than in a unified file workspace Free, Plus, Business, and Enterprise plans
Document360 Structured documentation and SOP portal Category hierarchies, Markdown and WYSIWYG articles, migration from other sources MCP server (Business and Enterprise) and API; working files are not hosted beside the articles Custom quote for every plan; 14-day free trial
Confluence Enterprise team wiki and documentation hub Team spaces, page trees, Jira attachments Atlassian Rovo MCP server, REST APIs, marketplace plugins Free tier for small teams; Standard, Premium, Enterprise tiers

1. Fastio

Fastio is an intelligent cloud workspace and company brain built for human teams and autonomous AI agents. Rather than functioning as a standalone wiki or an external search overlay, Fastio stores files natively in organization-owned workspaces, automatically indexing every document on arrival for full-text search, semantic discovery, and AI chat with direct source citations.

  • Native document persistence: Files uploaded directly or synchronized via Cloud Sync from Google Drive, Dropbox, Box, or OneDrive (SharePoint libraries through the OneDrive connector) index on arrival for keyword and semantic retrieval.
  • Built-in source citations: Answers cite specific files, page numbers, and text snippets used to generate each response.
  • Remote MCP connectivity: Autonomous agents connect directly to Fastio workspaces over Streamable HTTP at https://mcp.fast.io/mcp/tools (Claude and general apps), https://mcp.fast.io/mcp/operations (ChatGPT and Codex fallback), and https://mcp.fast.io/mcp/code (coding agents). Setup documentation is available at https://mcp.fast.io/docs.
  • Structured data extraction: Metadata Views turn unstructured documents into typed database tables without manual templates.
  • Advisory file locks and versioning: Multi-agent workflows stay coordinated through advisory per-file leases (lock-acquire, lock-release, lock-status) and per-file version history.
  • Collaborative Notes: Markdown notes live beside working files for documentation and agent handoffs, and people and agents co-edit them live with labeled cursors.
  • Limitations: Focuses on document and file intelligence rather than hosting customer-facing public support forums or social employee upvoting boards.
  • Best for: Distributed teams, engineering organizations, and agencies seeking an intelligent company brain where active files and autonomous AI agents collaborate in unified workspaces.
Plan Monthly Pricing Included Seats Storage Allowance Max Upload Monthly AI Credits
Starter $29 5 seats 1 TB 25 GB 300,000
Business $99 20 seats 10 TB 50 GB 1,200,000
Enterprise $299 50 seats 50 TB 100 GB 4,500,000

Monthly plans start with a 14-day free trial (credit card required). Plans are structured across Starter, Business, and Enterprise tiers with included seats, workspace storage, and monthly AI credits that cover AI work: chat, file ingestion, agent runs, and document processing.

2. Glean

Glean is an enterprise search engine and workplace assistant that connects across third-party applications to build a unified knowledge graph.

  • Strengths: Broad connectivity across enterprise SaaS tools, personalized search ranking, and conversational answers with inline citations.
  • Limitations: Does not host working files, so agents that connect through its MCP server read and act on content kept in other apps rather than saving outputs beside the sources.
  • Best for: Large enterprises seeking cross-application search without consolidating document storage.
  • Pricing: Pricing is not published; contact sales.

3. Guru

Guru is an AI knowledge management platform that delivers verified information cards directly inside team chat applications and web browsers.

  • Strengths: Built-in verification workflows prompt subject matter experts to re-verify cards on recurring schedules; Knowledge Agents give cited, permission-aware answers across 100+ connected tools; an MCP server delivers the same knowledge to outside AI tools.
  • Limitations: Knowledge lives in cards and connected apps rather than in a file workspace, so large working files stay in other systems.
  • Best for: Support and operations teams that rely on concise, verified snippets and procedural checklists in daily chat workflows.
  • Pricing: Custom package scoped with sales around scale, knowledge complexity, and AI maturity; no published prices.

4. Notion / Notion AI

Notion is a workspace that combines collaborative text documents, relational databases, project boards, and AI writing assistance.

  • Strengths: Modular canvas with custom views; clean markdown editing; Notion AI search with citations across pages, uploaded PDFs, and connected apps; an MCP server for agents.
  • Limitations: Organizing pages and maintaining database schemas requires continuous human design; files live as attachments on pages, capped at 5 GB each on paid plans, rather than in a working file store.
  • Best for: Startups and product design groups that value flexible visual layouts, relational documentation, and lightweight task tracking.
  • Pricing: Free plan for individuals, with Plus, Business, and Enterprise plans priced per member; Notion AI is included on Business and Enterprise, with a trial on Free and Plus.

5. Document360

Document360 is a specialized knowledge base platform for building structured internal standard operating procedure (SOP) manuals and external documentation portals.

  • Strengths: Hierarchical category manager for structured manuals; article revision history with version comparison; Eddy AI search and answers; an MCP server on Business and Enterprise plans.
  • Limitations: Designed for authored help articles rather than serving as a workspace for raw working files; content from Google Docs, Confluence, SharePoint, or PDFs comes in by migration into articles.
  • Best for: Technical writing teams and support leaders who need a formal platform for public help centers and internal operational manuals.
  • Pricing: Custom quote for every plan, priced by team accounts, workspaces, languages, and AI usage; 14-day free trial available.

6. Confluence (Atlassian)

Confluence is an enterprise documentation wiki developed by Atlassian, widely adopted by software engineering, IT, and product management teams.

  • Strengths: Deep integration with Jira issues and Bitbucket repositories; rich template library; Rovo search, chat, and agents on paid plans; Atlassian's Rovo MCP server for outside AI tools.
  • Limitations: Spaces frequently suffer from documentation sprawl and redundant pages; complex permission hierarchies; working files live outside pages rather than in a unified workspace with file locks.
  • Best for: Engineering teams standardized on Jira and Atlassian Cloud who require integrated technical documentation alongside issue tracking.
  • Pricing: Free tier for small teams; Standard, Premium, and Enterprise plans.
Fastio features

Explore Modern Bloomfire Alternatives with Fastio

Deploy an intelligent workspace where working files are indexed on arrival, answers cite source snippets, and human teams collaborate with AI agents via MCP. Monthly plans start with a 14-day free trial (credit card required).

Architectural Tradeoffs: Search Overlays, Wikis, and Intelligent Workspaces

Deciding between Bloomfire alternatives requires understanding the operational consequences of each platform's architectural foundation. Enterprise knowledge tools generally fall into three architectural categories: external search overlays, knowledge platforms and wikis, and file-native intelligent workspaces.

Each architectural model embodies distinct assumptions about how knowledge is created, where files are stored, and how AI systems interact with organizational data:

  1. External Search Overlays (Glean, GoSearch): Connects to third-party applications to index content and build a unified search graph, and Glean exposes that index to agents through MCP. Aggregates SaaS silos without migrating files, but hosts no files of its own, so agent outputs land in other systems.
  2. Knowledge Platforms and Wikis (Bloomfire, Guru, Document360, Confluence): Center on authored posts, cards, articles, and pages, now with AI drafting, connectors, and MCP servers. Bloomfire and Guru also index content from connected drives and apps, but the working files themselves stay in those other systems.
  3. File-Native Intelligent Workspaces (Fastio): Centralizes raw working documents in shared cloud workspaces, automatically indexing every file on arrival for keyword and semantic retrieval. Eliminates manual tagging overhead, provides exact source citations, exposes native file storage and advisory locks to AI agents via MCP, and unifies human and agent collaboration.

The Hidden Cost of Keeping Two Copies of Knowledge

A common reason knowledge management initiatives stall is documentation fatigue. A team rolls out a wiki or a knowledge platform, and enthusiasm is high at first. Leaders encourage employees to ask questions, share insights, and write up what they know.

Over time, daily operational pressures take precedence. Software engineers focus on shipping code, legal teams focus on closing contracts, and sales teams focus on client meetings. Asking these professionals to duplicate work by writing supplementary articles introduces friction that teams tend to avoid.

When writing up stops, any platform built on authored content falls behind the files it describes. AI tagging and stale-content flags, which Bloomfire and others offer, help find the gaps, but someone still has to update the post. An intelligent company brain avoids the second copy by indexing the working files themselves on arrival.

Unifying Human Teams and Autonomous Agents Around One Source of Truth

The future of enterprise knowledge work involves a hybrid workforce: human professionals and autonomous AI agents working in parallel. In this operating model, both humans and AI agents must reference the exact same underlying facts.

An intelligent workspace establishes a single source of truth for both actors:

  • For human team members: Fastio provides an intuitive browser interface where teams can organize files into workspaces, inspect per-file version history, review changes in a detailed activity log, and ask natural language questions with source citations.
  • For autonomous AI agents: Fastio provides remote MCP endpoints over Streamable HTTP: https://mcp.fast.io/mcp/tools for Claude apps and general clients, and https://mcp.fast.io/mcp/code for coding agents like Claude Code, Cursor, and Cline. Agents search documents, inspect structured metadata, acquire advisory file locks, and write generated files back to shared storage.

By grounding human decisions and automated agent execution in the same versioned files, organizations reduce miscommunication and keep teams aligned.

Steps to Migrate from Legacy Knowledge Bases to an Intelligent Workspace

Moving from a knowledge platform like Bloomfire to a file-native company brain does not require months of manual data re-entry. By focusing on primary source files rather than transcribing posts and forum threads, organizations can wire up workspaces quickly while syncing and indexing continue in the background.

Follow this five-step implementation roadmap to deploy a unified company brain:

  • Step 1: Audit and Consolidate Active Working Files. Identify where authoritative knowledge lives across engineering specifications, architecture decision records, customer agreements, and operational runbooks. Archive obsolete drafts and consolidate active documents.
  • Step 2: Establish Centralized Workspaces and Connect Cloud Storage. Create dedicated workspaces for functional teams and configure Cloud Sync with Google Drive, Dropbox, Box, or OneDrive. Enable one-way or two-way synchronization on a periodic schedule or on demand.
  • Step 3: Configure Role-Based Membership and Restrict Default Join Access. Assign organization roles (owner, admin, member) and workspace roles (owner, admin, member, guest, view). By default any organization member can join a workspace and any workspace member can manage members, so for sensitive team spaces set the join setting to invited members only and limit member management to admins and owners. Search queries and AI features answer exclusively from the workspaces and shares the asking person or agent belongs to.
  • Step 4: Deploy Structured Extraction with Metadata Views. Configure Metadata Views for document collections like contracts, invoices, or specifications. Define field schemas in natural language to extract typed values across matching files into queryable spreadsheets.
  • Step 5: Connect Autonomous AI Assistants and Coding Agents. Connect Claude using the custom connector at https://mcp.fast.io/mcp/tools with OAuth sign-in, and configure coding agents (Cursor, Claude Code, Cline, Gemini CLI) via https://mcp.fast.io/mcp/code. Agents can query verified files with citations and write completed deliverables back to shared storage.

Five-Step Implementation Checklist for Enterprise Teams

Before launching your new company brain platform, review this operational checklist:

  1. File format verification: Confirm that candidate documents (PDFs, Word documents, Markdown files, spreadsheets, presentations) are legible and uncorrupted before ingestion.
  2. Cloud Sync plan: Decide which folders sync one-way or two-way, and when to run an on-demand sync on top of the regular schedule.
  3. Access boundary review: Verify that sensitive financial records, human resources files, and executive correspondence are assigned to restricted workspaces with explicit member rosters.
  4. Pilot team deployment: Roll out the intelligent workspace to a technical pilot team to validate search retrieval accuracy and source citation relevance.
  5. Agent integration testing: Verify that AI assistants and coding agents can query workspace files via MCP and return citation-backed answers before broader organizational rollout.

Managing Access Boundaries and Permissions Without Complex Folder ACLs

Legacy knowledge management platforms often introduce complex permission models featuring nested folder inheritance, individual file access control lists (ACLs), and conflicting group permissions. These complex trees frequently result in permission sprawl or leaky search results where restricted data surfaces in unauthorized search queries.

Fastio eliminates this complexity through a clean, boundary-based governance model:

  • Permissions are set per organization, workspace, and share by member role.
  • There are no folder-level or file-level permissions inside a workspace; every workspace member reaches all workspace content according to their role.
  • If specific documents must be restricted to a subset of employees (such as executive compensation plans or pending litigation files), those documents are placed in a dedicated, restricted workspace or a scoped share.
  • External partners, clients, or contractors can be granted access to specific files or folders through branded shares without consuming an organization seat or gaining visibility into the broader workspace.

This structural boundary keeps search and AI answers predictable: a person or agent querying the company brain gets answers only from the workspaces and shares they belong to.

Sources

References used to verify factual claims in this guide.

  1. 1 EMSNow: Gartner Survey on Digital Workplace Information Friction Accessed

    A Gartner survey of 4,861 employees found that 47% of digital workers struggle to find the information or data they need to do their jobs effectively.

Frequently Asked Questions

What are the best alternatives to Bloomfire?

The best alternatives to Bloomfire depend on organizational needs. Fastio serves as an intelligent company brain that stores working files natively, indexing documents on arrival for keyword and semantic retrieval while connecting to AI agents via MCP. Glean provides an enterprise search overlay across third-party SaaS applications. Guru offers verified knowledge cards directly inside chat tools. Notion AI delivers a flexible collaborative wiki with custom database views, and Document360 specializes in structured internal SOPs and external documentation portals.

Why do teams look for Bloomfire alternatives?

Teams look at alternatives when they want the working files and the knowledge in one place. Bloomfire builds a governed knowledge layer over content that stays in SharePoint, Google Drive, Box, and other source systems, and serves it to people and agents through search, its Synapse conversational AI, and AI Connect (MCP and an API). Its pricing is a custom quote set by scope. Teams that want agents to search, lock, edit, and save the files themselves under one set of permissions look at file-native workspaces such as Fastio, which publishes its plan prices.

How does document retrieval in Fastio differ from Bloomfire?

While both platforms provide semantic search over content, in Fastio working files live directly in the workspace alongside source files. People and AI agents search primary documents, extract structured fields with Metadata Views, co-edit Notes, and receive citation-backed answers pointing to specific files, pages, and snippets under one set of workspace permissions.

Can autonomous AI agents query and update content stored in Bloomfire?

Agents can query Bloomfire through AI Connect, which exposes its search and reasoning surfaces over MCP and an API to tools such as Claude, Microsoft Copilot, and Glean. The difference with Fastio is where the files live. In Fastio the working files sit in the workspace, so agents search them, take advisory file locks, co-edit Notes, and save outputs beside the sources under one set of workspace and share permissions over remote MCP endpoints.

How does Fastio function as an intelligent company brain?

Fastio acts as a company brain by unifying primary file storage, automatic indexing, and AI agent access within shared workspaces. Documents uploaded directly or synchronized from existing drives are indexed on arrival for full-text and semantic search without manual tagging. Users and AI agents can ask natural language questions and receive accurate answers citing specific source files and snippets. Through Metadata Views, Fastio also extracts structured fields from unstructured documents into typed database tables.

Does Fastio support synchronization with existing cloud storage providers?

Yes. Fastio Cloud Sync connects Google Drive, Dropbox, Box, and OneDrive folders (including SharePoint libraries through the OneDrive connector) to a workspace, one-way or two-way, on a schedule or on demand. Synchronized files index automatically on arrival for keyword and semantic search, and they adopt the Fastio workspace's role-based permissions, keeping access governance unified.

How do permissions work in Fastio compared to traditional knowledge bases?

Traditional knowledge bases often use complicated folder inheritance and individual document access control lists, which lead to permission sprawl and unauthorized search leaks. Fastio sets permissions per organization, workspace, and share by member role, with no folder-level or file-level permissions inside a workspace. Material that requires restricted access lives in its own dedicated workspace or share. Search queries and AI assistants only retrieve answers from workspaces and shares where the user or agent is an authorized member.

Related Resources

Company Brain
One workspace your agents can ask, with cited answers
Workspaces
Organize teams, agents, and files
Ripley AI
Built-in AI: search, chat, and summarize
Fastio features

Explore Modern Bloomfire Alternatives with Fastio

Deploy an intelligent workspace where working files are indexed on arrival, answers cite source snippets, and human teams collaborate with AI agents via MCP. Monthly plans start with a 14-day free trial (credit card required).

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