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Agents
We build the analytics, dashboards, scorecards and management information that run an operation — and we prove it on ABL-Sim, 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.
Ninety seconds through the whole thing — real-time wallboard, deep-dive analytics, workforce planning and banking operations, all driven by one configurable simulation.
ABL-Sim is 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 live demo above runs free, around the clock, at demo.abldigitech.com — open it any time, no install and no cost. It is just one configuration of ABL-Sim, our fully customisable contact-centre simulator. When you want your own, a built-in admin console dials the whole centre to whatever you want to study — then the engine rebuilds the simulation, and every dashboard, report and scorecard follows.
Contacts per day, day-of-week shape, the intraday curve, IVR self-service and abandon rates.
Add or remove queues; set handle time, wrap, patience, routing priority and the service-level target for each.
Headcount, the five shrinkage components, shift patterns, skills and cross-training.
Spread in speed, wrap, holds, transfers and punctuality — how varied your people are.
Disposition codes, why customers called, resolution rates and the core-banking action behind each.
Seed known strong and weak performers and anomalies — then test whether the analytics actually finds them.
Around 200 settings in all — plus a built-in Erlang what-if check: enter your volume, handle time and headcount, and see the service level and occupancy you'd get, and the staffing that would hold your target, before you commit to anything.
Give analysts, team leaders and WFM planners a complete, realistic centre to learn on — data that's safe (nothing real), labelled (you planted the answers) and resettable. Practise reading dashboards, building scorecards and running intraday without ever touching a live operation.
Model the "what if" before it happens: volume doubles, handle time creeps up, a site closes, headcount is cut, a new queue launches. Rebuild the centre to match and watch what your reporting, staffing and service level actually do — a safe sandbox for capacity, resilience and reporting decisions.
How it's delivered: ABL-Sim is shipped and set up per client — one dedicated instance, hosted in your own environment and yours to run. Pricing is tailored to the scale and configuration you need; talk to us for details.
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.