Four AI disciplines, one runtime.
MetaFlowKit combines generative AI, autonomous agents, predictive analytics and workflow automation into a single platform — grounded in your documents, your business rules and your ERP data, not a generic chatbot bolted onto your stack.
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The four pillars
Everything runs on one grounded runtime.
Each capability below is a layer in MetaFlowKit, not a separate product — they share the same document context, permission model and audit trail.
Generative AI
Large language models grounded in your document corpus and ERP schema — not open-ended chat. Every generated field, summary or draft is tied back to a source passage with a confidence score, so outputs are explainable and auditable by design.
AI Agents
Autonomous agents that plan multi-step tasks — reconcile an invoice, chase a missing PO, escalate an exception — and execute them inside your ERP through governed, permissioned actions. Every action is a step a human could take, just faster and logged.
Predictive Analytics
Forecast cash needs, flag invoices likely to be disputed, predict close-cycle bottlenecks before they happen — models trained on your historical ERP transactions, refreshed continuously as new data lands.
Automation
Deterministic business-rule workflows for the parts of the process that don't need a model at all — routing, approvals, three-way matching and write-back — configured, not coded, so your team can adjust it directly.
Inside an agent run
Plan, act, verify — every time.
An agent never freelances against your ERP. Each run follows the same governed loop, with a human in the loop wherever risk warrants it.
Read the trigger
A new document, an overdue approval, or a scheduled check kicks off the agent with full context from MetaFlowKit's document and ERP state.
Break down the task
The agent decomposes the goal into an ordered set of steps, choosing from its available tools and skills — nothing is hard-coded per scenario.
Execute with guardrails
Low-risk steps run automatically; anything touching payment, GL posting, or master data pauses for human approval per your policy.
Confirm and log
The agent checks its own output against source data before closing the task, and writes a complete, timestamped record for audit.
Why it's different
Rules automation vs. agentic AI.
Traditional RPA / rules
QubixLabs agentic AI
See an agent work your queue, live.
Bring a real backlog — approvals, exceptions, reconciliations — and we'll show you what an agent does with it.
Talk to the team