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Agents
We build the analytics, dashboards, scorecards and management information that run an operation — and we prove it on a complete, working bank contact centre you can open and interrogate right now. Not a slide deck of screenshots; the live article, reported on exactly as we would report on your operation.
A complete 24×7 bank contact centre, simulated end to end and reported on with the same stack we would build for a real client. Most reporting vendors show you screenshots. This one you can sort, filter, disagree with, and check whether the analysis survives scrutiny — because the data underneath is real and the calculations are open.
Three self-contained products, each built on a real, published data model — the Genesys Info Mart schema for the contact centre, NICE IEX SmartSync for workforce management, and Apache Fineract's DDL for banking operations. Open any of them:
The same discipline behind the demo, built for your operation — on your systems, your metrics, your reporting cadence.
Root-cause analysis, outlier detection and the statistical work that turns a table of numbers into a decision you can defend. On the demo, the outlier detector scores 100% recall and 89% precision against known ground truth — and the headline finding overturns the obvious read of the roster entirely.
Evidence: Call Analytics →Real-time wallboards, intraday and 15-minute interval views that show the operation as it happens — service level, occupancy, adherence, abandon — designed so the person watching knows exactly what to do next, not just what happened.
Evidence: the live Wallboard →A scorecard is only useful if it measures the person, not the work they were dealt. We build in the work-mix adjustment that makes agent and team comparisons fair — the difference between a performance conversation that is right and one that is wrong about nearly everybody.
Evidence: the 99.4% finding →The right numbers, to the right people, on the right cadence — tying contact demand back to why customers called and what the operation did about it, so each report answers a decision someone actually owns rather than reporting for its own sake.
Evidence: Banking Ops →99.4% of the apparent gap between agents
was the work they were given —
not how well they did it.
Ranked on raw handling time, the roster looks full of stragglers. Measure it properly — adjust for the mix of contacts each agent actually handled — and 99.4% of that spread disappears into the work itself. Six of the ten teams change rank once work mix is removed. A performance conversation built on the raw table would have been wrong about nearly everybody on it.
That is what “we know how to build a scorecard” means in practice: not an adjective, but a concrete, checkable claim you can reproduce in the demo yourself. It is also exactly the kind of error a badly built dashboard makes quietly, every day — which is the whole reason to have someone build it properly.
Every figure below is queried from the demo's own 60-day warehouse — reproducible in the live product, not rounded into something punchier.
The data is simulated — realistic and internally consistent, but not a real bank's traffic. We say so because it builds more trust than hiding it: what you are checking is the method, and the method is what transfers.
The banking model uses Apache Fineract because it publishes its schema. A real Indian bank runs Finacle, FLEXCUBE or BaNCS, which publish nothing — so what carries across to your operation is the integration pattern, not this particular schema. We are not implying a ready-made Finacle connector.