Every module NairaRefinery AI uses to turn raw data into capital decisions
From ingestion to allocation, NairaRefinery AI is built as a set of connected tools that analyze market and operational data and translate it into ranked, explainable recommendations for your team.
Illustrative configuration view. Actual coverage depends on connected data sources.
A structured pipeline, not a black box
NairaRefinery AI organizes analysis into discrete, inspectable stages so your team can see how a number or recommendation was reached before acting on it.
Unified data ingestion
Connect internal spreadsheets, transaction records, and market feeds into a single structured workspace, removing manual reconciliation between sources.
Scenario modeling
Build side-by-side projections under different assumptions — currency movement, demand shifts, cost changes — and compare outcomes on one screen.
Allocation scoring
Every option is scored against configurable criteria such as risk tolerance, liquidity needs, and target horizon, then ranked for review.
Explainable outputs
Each recommendation is paired with the underlying drivers and assumptions, so decision-makers can trace the logic rather than take a score on faith.
Role-based dashboards
Analysts, finance leads, and executives each get a view scoped to what they need — detailed inputs for one team, summary trends for another.
Audit-ready history
Every model run and recommendation is timestamped and stored, giving your team a record to revisit when reviewing past decisions.
How the analysis engine weighs your options
Inputs are normalized, then run through configurable scoring models that weigh factors such as expected return, volatility, and liquidity constraints. The result is a ranked shortlist rather than a single opaque answer.
Weightings are visible and adjustable, so your team stays in control of how aggressively the model favors growth versus capital preservation for any given decision.
Illustrative scoring output for a single sample scenario. Actual weightings are configured per client.
Built with data discipline in mind
Because NairaRefinery AI works with sensitive financial and operational data, the platform is structured around access control, separation of environments, and traceability.
Access control
Permissions are set per user and per workspace, limiting who can view raw data versus summarized outputs.
Environment separation
Client data workspaces are kept logically separated to reduce the risk of cross-account exposure.
Change tracking
Adjustments to model assumptions and scoring weights are logged, so teams can review who changed what and when.
- Access modelRole-based
- Data workspacesPer-client separation
- Change historyLogged & timestamped
- Export formatsConfigurable on request
From first connection to first recommendation
Onboarding is structured so your team understands how outputs are generated before relying on them for real decisions.
Connect your data
Link the spreadsheets, records, or feeds relevant to the decisions you want to inform, within a scoped workspace.
Configure weightings
Set the criteria that matter for your context — risk appetite, liquidity needs, time horizon — before any scoring runs.
Review and refine
Walk through the first set of ranked outputs with our team, adjusting assumptions until the model reflects how your team actually decides.
Use cases across capital-facing teams
NairaRefinery AI is used wherever a team needs to compare options against data rather than intuition alone.
Cash and reserve allocation
Compare holding, deploying, or hedging cash positions across different currency and rate scenarios before committing.
Portfolio option screening
Narrow a wide set of potential positions down to a ranked shortlist based on configured risk and return criteria.
Working capital planning
Model the effect of supplier terms, inventory levels, and receivables timing on near-term liquidity.
Board-level decision review
Present ranked options with visible assumptions, making it easier to walk a board or investment committee through the reasoning.
Designed for teams who need to justify a decision, not just make one
Every feature in NairaRefinery AI exists to make a recommendation traceable — from the raw data used, through the assumptions applied, to the final ranked output.
That structure matters as much as the output itself, especially for teams who answer to investors, boards, or regulators for how capital decisions were made.
See NairaRefinery AI against your own data
Request access to walk through the platform with a workspace configured around a scenario relevant to your team.