Real-World Impact: KPIs, Outcomes, and Case Studies
Measured improvements across speed, quality, and operational risk - validated in production environments.
We start every project by agreeing on 3–5 concrete KPIs (cycle time, error rate, cost, satisfaction) and how we will measure them in your systems
Every engagement with us starts with a simple question:
“How will we know this AI solution is successful?”
How we define success
We don’t treat “AI” as a goal in itself. For each project, we agree on measurable outcomes in three dimensions:
Operational impact
Faster decisions and processes
Quality & risk
Fewer errors, better use of existing knowledge, more consistent decisions
Cost & productivity
Less manual effort, more work done with the same team
For business‑critical processes we always add risk and compliance metrics, so your risk and audit teams can stand behind the solution.
Impact at a glance
Across recent projects, our AI solutions have delivered:
These numbers are not “theoretical potential” - they are measured before/after deltas on real processes. Exact values differ by client and domain.
Selected case studies
Reducing handling time for complex support cases
Challenge
A global e‑commerce company was facing increasing complexity in customer support: more products, more regions, more exceptions. Handling time for complex cases was growing, and senior agents were spending a lot of time on manual investigation and drafting responses.
What we did
- ●Designed an AI‑assisted workflow for complex tickets
- ●Added automatic case summaries and context gathering from internal systems
- ●Implemented next‑best‑action suggestions based on policies and historical resolutions
- ●Connected internal playbooks and policies as a trusted knowledge source with citations
Results
- ●–35% average time‑to‑resolution for complex cases in the pilot region
- ●60% of cases processed with AI assistance in the first 3 months
- ●<2% re‑opened tickets due to AI‑related errors
- ●Higher satisfaction scores from both agents and customers
Speeding up KYC reviews while keeping risk under control
Challenge
A financial services provider needed to speed up periodic KYC reviews. Manual data collection and drafting risk assessments were consuming a lot of analyst time. At the same time, risk and compliance teams could not accept lower control.
What we did
- ●Mapped the end‑to‑end KYC review process with risk and compliance teams
- ●Built an AI‑orchestrated workflow that collects relevant data from internal systems and external sources
- ●Drafted risk summaries and recommendations
- ●Flagged edge cases and inconsistencies for human review
- ●Ensured that every recommendation came with traceable references to underlying data and policies
Results
- ●–30–40% reduction in review cycle time
- ●More consistent risk assessments across analysts and regions
- ●Better transparency for internal audit: clearer records of what was reviewed and why decisions were made
Turning scattered documents into a trusted knowledge layer
Challenge
A digital services company had thousands of contracts, policies and technical documents spread across different tools and repositories. Teams were spending hours searching, re‑reading and copying fragments into new documents - or they were simply guessing.
What we did
- ●Implemented a document ingestion pipeline for contracts, policies, procedures and technical documentation
- ●Built a knowledge layer on top, allowing users to ask questions and receive answers with citations
- ●Added tools to compare versions, highlight key changes and detect potential risks in updates
Results
- ●50–70% less time spent searching and reading documents for recurring questions
- ●Higher consistency of answers across teams and regions
- ●Fewer missed clauses and outdated references in key decisions, according to internal audits
Accelerating delivery of core platform features
Challenge
A technology company needed to deliver new features in a core platform faster, without increasing defect rates or technical debt. Traditional development cycles were too slow, and previous “AI code helper” experiments did not fit their architecture and quality standards.
What we did
- ●Introduced an AI‑augmented delivery process for selected services
- ●Used AI to assist with architecture options, implementation, tests and refactoring
- ●Integrated AI suggestions into existing code review and CI/CD processes, instead of bypassing them
Results
- ●30–50% faster delivery for selected features
- ●Lower defect rates in early production releases for those services
- ●More time for senior engineers to focus on architecture and complex design, instead of boilerplate
What we measure and how
Our KPI framework is designed to be understandable for C‑level, operations, IT and risk teams alike. We group metrics into five categories.
1Speed & throughput
- ●Time‑to‑decision
- ●End‑to‑end process cycle time
- ●Time‑to‑resolution (for incidents, tickets, cases)
2Quality & consistency
- ●Error and rework rates
- ●Consistency of decisions across teams/regions
- ●Coverage and correctness of references to internal knowledge
3Productivity & cost
- ●Manual effort per case / ticket / report
- ●Volume of work handled per FTE
- ●Cost per processed unit (case, transaction, review, feature)
4Risk & compliance
- ●Number and severity of risk/compliance incidents related to the process
- ●Audit findings related to AI‑supported operations
- ●Coverage of critical controls and checks
5Adoption & satisfaction
- ●Active usage of AI assistant / workflow
- ●User satisfaction (agents, analysts, engineers)
- ●Stakeholder confidence (operations, risk, C‑level)
For each project we pick the relevant metrics, define baselines together with your team, and track progress over time. If the numbers don’t move in the right direction, we adjust the solution - or stop it, instead of scaling something that doesn’t work.
Working with your data, systems and risk constraints
We don’t operate AI in isolation. For every engagement we:
- Integrate with your existing systems and data sources instead of building separate “shadow tools”
- Align with your risk, security and compliance teams from day one
- Make sure every AI‑supported decision is traceable back to data and documents
- Provide you with a clear view of how the system behaves and how it is monitored
This is what allows us - and you - to stand behind the KPIs and the decisions your AI systems help to make.
Reviews
