Before rollout
Run curated scenarios that test grounded answers, refusals, escalation paths, and retrieval quality.
Grounded AI agents
for your company's data
Answer employee questions from your company’s data, then monitor how agents are used, evaluate their answers, and improve them over time.
An AI agent platform for companies
AI-Flow lets you build central AI agents that a team, department or your whole company can rely on. They answer from your own data, and every interaction becomes part of a quality loop.
Connect provider-agnostic AI agents to SharePoint, your CMS, or company databases for RAG, talk-to-your-data, or other agentic use cases. This gives employees one place to query both documents and structured company data.
Users can give feedback directly on answers. You can review conversations, run evaluations, track whether quality changes over time, and see what people actually ask about.
Monitoring, tracing and audit trails show when agents are used, which topics come up, how usage develops, and what that usage costs in tokens and model spend.
AI-Flow isn’t trying to give every employee their own personal AI assistant. ChatGPT and Claude already do that well. It’s for building shared company agents that stay grounded, observable and under your control.
Build
Design AI workflows with knowledge sources, tools, triggers, and human approval steps on a visual canvas.
Operate
Run with permissions, escalation, auditability, and full visibility into how agents behave in production.
Improve
Evaluate outputs, measure groundedness, and refine agent performance continuously.
Start with the questions your teams answer every day
Give HR, IT, and operations teams a reliable way to answer recurring questions and handle routine work.
Support
IT Support Assistant
Answer recurring IT questions from approved documentation and hand complex cases to the service desk.
People Operations
HR Assistant
Give employees cited answers about policies, benefits, and internal processes without adding to the HR inbox.
Knowledge
Technical Documentation
Turn manuals and SOPs into accurate, source-grounded answers your teams can actually use.
Built-in observability and control
Keep agents grounded, reviewable, and under human control as more teams start using them.
Grounded in Company Sources
Answers cite the documents, policies, and data your teams already use, so employees can check the source themselves.
Human Oversight
Critical actions route to the right person before anything is sent, changed, or executed.
Full Traceability
See which workflow ran, which sources it used, and which actions the agent took.
Roll Out in Stages
Start with one use case, measure the result, and add teams on the same governed platform.
Included with the platform
The production tools are part of the platform
AI-Flow includes document ingestion, access controls, traces, cost tracking, feedback, and evaluation. The product screenshots below show how each part works.
Build
A visual builder your whole team can read
The visual canvas shows each agent's knowledge, tools, triggers, and approval steps. Business experts can follow the logic, while technical colleagues configure the details.
Documents
Document ingestion that handles a real SharePoint
Sync SharePoint folders continuously, upload files, or pull from the web. Search every knowledge base by keyword, semantically, or both, and edit documents in place with a built-in editor.
Grounded answers
Every claim links to the page it came from
Answers cite their sources with footnotes. A click opens the sources panel with the document, the page, and the passage, so anyone can check the agent's work.
Observability
Every run can be replayed, step by step
Call History records every execution with its trigger, duration, and cost. Open the full trace of any run or download its audit log. When a run fails, the trace shows the step where it failed.
Operations
Track cost by model, agent, and call
See token usage and provider-reported cost by model, agent, connection, trigger, or individual call. Cached and reasoning tokens are included in the same view.
Governance
Manage users, permissions, and adoption in one place
Role-based access control, organization-wide sharing, granular permissions, and adoption analytics live in the same admin area. Export usage data to Excel for reporting.
Feedback
A thumbs-down goes straight into the evaluation suite
Anyone can rate an answer in place, pick a reason, and add a comment. The evaluation suite collects these next to your test sets, so you find quality problems where they happen.
Also included
- Human approval steps before critical actions
- Agent versioning with named checkpoints and rollback
- Publishing an agent is a click: a shareable web app with its own URL, password, or org sign-in
- Triggers from email, Telegram, schedules, and webhooks
- Built-in evaluation with precision, recall, and groundedness scores
- Model-agnostic: OpenAI, Anthropic, Mistral, Gemini, and European models
Evaluation
Test answer quality before and after rollout
Use the same evaluation workspace before launch and in production. Review groundedness, failure cases, and answer quality against real scenarios.
Groundedness
Check whether answers cite approved sources, follow defined boundaries, and refuse when they should.
Quality
Score relevance, completeness, and usefulness against real scenarios before release.
Improvement
Use traces, failed cases, and evaluator feedback to decide what to change in the workflow.
In production
Inspect what the agent said, what it used, and where the workflow succeeded or broke down.
Evaluation workspace
Compare prompts, agents, and RAG configurations against the same test set.
Open evaluationWhy AI-Flow.eu
What AI-Flow.eu includes
Workflow control, credential isolation, managed deployment, and evaluation in one platform.
AI Flow
AI agents with visible workflows and controls
01
Defined workflow execution
Use fixed steps where predictable behavior is required
Workflow steps you define
The agent follows the sequence you configure
Visual flow builder
Every step is explicit and auditable
Controlled execution paths
Limit model calls to the steps and tools you configure
Built-in agent services
RAG, memory, and API connections in the same platform
Built-in vector databases
Knowledge retrieval without external infrastructure
Persistent agent memory
Context that survives across sessions and users
Guided connection setup
Configure service connections in the app
02
Credential protection
Credentials stay outside model context
Credential isolation
Secrets are encrypted and excluded from the LLM context window
Encrypted secrets vault
API keys and tokens never passed to a model
Full audit trail
Every agent action is logged and traceable
Model-agnostic architecture
Works across model providers and API contracts
Change models without rebuilding
Keep the workflow structure when you switch providers
Stable workflow structure
The configured steps stay in place across model changes
03
Managed deployment
Launch without a separate infrastructure project
Managed cloud option
Run without maintaining servers or containers
Hosted deployment
AI-Flow manages the application infrastructure
Single control center
All agents, triggers, and sources in one place
Actions in connected systems
Update calendars, send email, or call business APIs from a workflow.
Calendar, email, and API connections
Run actions in the systems your teams already use
Low technical barrier
Analysts can configure workflows in the visual builder
04
Built-in evaluation
Test answer quality before and after rollout
Pre-release evaluation suite
Run repeatable scenarios before release
Automated test scenarios
Coverage across grounded answers, refusals, and retrieval
Groundedness scoring
Measure precision, faithfulness, and recall
Scheduled evaluation runs
Track quality over time with repeatable test runs
Result history
Compare groundedness and other scores across runs
Production feedback
Review ratings and comments alongside evaluation results
See how AI-Flow.eu compares to n8n, Zapier, Dify, and ten other platforms, feature by feature
Hands-on
Prefer to validate it yourself first?
Open a personal agent, inspect the builder, and try the evaluation workspace before booking a demo.
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