Financial decisions you can stand behind.
Decision Control examines the financial substance of AI-generated proposals through proprietary quantitative evaluation.
Your firm’s limits, permissions and approval requirements govern whether the connected action may proceed—with evidence to explain why.
When to consider Decision Control
Where automation gets held up.
New automation
Still doing the work manually?
The cost of a wrong action keeps the work manual.
Start with the decision you want to automate. No existing AI deployment is needed.
AI already in use
Review queues grow.
Routine checks compete with decisions that need judgement.
Controls get rebuilt.
Each new AI workflow repeats the rules, connections and review work.
Examine the financial substance before action.
Recalculate the figures.
Check amounts, allocations and cash flows against accepted data. Surface discrepancies.
Measure the exposure.
Assess the resulting concentration, balances and commitments against your firm's limits.
Test the assumptions.
Explore agreed scenarios to identify when a proposal would cross a limit or no longer meet the required criteria.
AI proposes. Your requirements govern what proceeds.
A recommendation looks reasonable. Its exposure exceeds the limit.
Consider a portfolio change that raises sector exposure from 18% to 27%, above the client's 25% limit.
Release stopped.
The quantitative layer calculates the effect. The control platform applies the limit before the recommendation is sent, with the finding and supporting evidence ready for review.
Control the action that actually runs.
Decision Control governs release on the connected action path. Changed proposals or relevant facts require a fresh evaluation.
Keep people focused on judgement.
Qualifying work can proceed automatically within the authority your firm defines. People retain the decisions that require their judgement.
Change the AI. Keep business ownership.
Keep the control layer as models change. Reassess affected analysis and requirements, then test the new action path.
The provenance chain
Follow the evidence.
Explain the decision.
Answer a customer dispute or review question without rebuilding the case across separate logs.
Proposal
What the AI put forward.
Analysis
The quantitative findings, source facts and applicable requirements.
Decision
Whether action was permitted, and why.
Action
What the connected system did.
Result
The confirmation available.
Missing evidence and unconfirmed results stay visible.
Entellex implementation
Technology. Connections.
Expertise.
Apply the quantitative layer.
We configure the financial calculations, exposure measures and scenarios for your decision, using accepted data sources and your firm's requirements.
Connect the decision to action.
We integrate Decision Control with the agreed execution paths and establish who handles exceptions.
Test beyond a clean run.
We test changed proposals, missing facts, unconfirmed actions and unnecessary stops.
Start with Decision Control
Bring a decision worth automating.
Start with existing AI or work you want to automate. We'll identify where Decision Control fits and what implementation would involve.
Talk about Decision ControlA Decision Replay evaluates example cases and returns the quantitative findings, the reasons each case proceeds, waits or stops, and any missing evidence. Live implementation connects and tests the agreed action paths.
Evaluation scope and system connections shape implementation and price.