Section 2 — Live Demo · Step 1 of 5

You named the problems.
Here's what solving them looks like.

Three things the room agreed on in the last 25 minutes.

60%
Pilots never reach production
The pilot cemetery is real. Most AI POCs die between the innovation lab and the core system.
72%
Automating — but not measuring
Institutions report AI changing workflows. Only 19% can demonstrate the value rigorously.
$2.7B
AML fines in 2023 alone
Regulators are scrutinising the AI behind compliance decisions — not just the outcomes they produce.
The most dangerous gap in financial services AI is not awareness — it is the distance between knowing what needs to change and having a system in production that actually does it.

What follows is not a concept. It is a working system, live in a real bank.
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Section 2 — Live Demo · Step 2 of 5

Sky GenAI — Agentic AI for banking operations

Built by InDataCore. In production. Not a pilot.

SKY GENAI
by InDataCore
Agentic AI Document
Processing Platform
"Fully autonomous document processing — compliance, extraction, and decision-making, end to end."
Automatic reading of structured, semi-structured, and unstructured documents
eKYC / eKYB identification and strong authentication
Intelligent compliance controls with audit trail
Integrated workflows — decision automation and process optimisation
360° dashboard — real-time control of every customer file
Full on-premise deployment — data never leaves your infrastructure
⚡
Operational Efficiency
Back office workload reduced by 90–100% across credit processing, trade finance, and onboarding. Processing time drops from days to seconds.
🔒
Compliance by Design
Regulatory controls are built into every workflow — not bolted on. Every AI decision is documented, explainable, and auditable from day one.
🏦
Production-Grade
Live at BMCI (Banque Marocaine du Commerce et de l'Industrie). Not a proof of concept. This is what production actually looks like.
🔗
No Rip-and-Replace
Integrates via API and SDK into existing digital ecosystems. No infrastructure overhaul required to go live.
▶ LIVE DEMO
Sky GenAI — Agentic Document Processing
InDataCore · Banking Sector · BMCI Production Instance
Credit application processing — end to end
Compliance verification — AI-powered, fully documented
Human analyst freed for exception handling
Processing time: days → seconds
Section 2 — Live Demo · Step 4 of 5

Production results — not projections

Measured across live banking deployments. InDataCore client data, 2024–2025.

90–100%
Back office workload eliminated
Per use case. Staff reassigned to exception handling and higher-value client work — not made redundant.
Days → Seconds
Processing time — every use case
Credit: 3–5 days to seconds. Trade Finance: 3–10 days to seconds. Onboarding: 3–5 days to seconds.
70–90%
Fraud detection rate
AI-powered anomaly detection across transaction history and customer interactions. Proactive, not reactive.
Use Case Back Office Charge Processing Time Data Reliability Revenue Capacity
Consumer Credits Reduced 90–100% 3–5 days → seconds +90–100% ×5–10 without recruiting
Real Estate Loans Reduced 90–100% 3–5 days → seconds +90–100% ×5–10 without recruiting
Customer OnBoarding Reduced 80–100% 3–5 days → seconds +90–100% ×5–10 without recruiting
Trade Finance Reduced 80–100% 3–10 days → seconds +90–100% ×5–10 without recruiting
AML / Compliance Controls Reduced 90–100% 15 days → seconds +90–100% — (risk reduction)
Bank Check Processing Reduced 80–100% 1–2 days → seconds +90–100% ×5–10 without recruiting
* Direct operational costs only. Revenue multiplier reflects capacity increase without additional headcount. Source: InDataCore client data, 2024–2025.
Section 2 — Closing Challenge
The technology Works

You've seen automation that reduces 90% of back-office workload. Processing times that drop from days to seconds.
Compliance that is auditable from the moment the document enters the system.

In this case, this is not a pilot. It is production.
The gap is not in the technology.

A
How's your infrastructure estate — Do you need to need to upgrade your legacy systems to more efficient and cost effective ones?
B
How is your data and system governance model — who monitors and owns the output when the AI makes a compliance decision?
C
How alligned your organisation strategy in using AI? — does the business case for AI reach the board, or does it stop at the CTO?