EtaCar SystemsMeasurable business impact from AI in production.
Measured gains in speed, quality, and risk control — tied to governed outcomes.
3–5 KPIs. Defined upfront. Measured in production.
Defined, Measured, Delivered
Success is defined upfront and measured across three clear KPI dimensions:
Impact at a glance
Across recent projects, our AI has delivered:
These are measured deltas, not theoretical potential. Values vary 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
- ●–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
- ●–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
- ●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
- ●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 for C-level, operations, IT, and risk teams.
- ●Time-to-decision
- ●End-to-end cycle time
- ●Time-to-resolution
- ●Error and rework rates
- ●Decision consistency across teams
- ●Accuracy and coverage of knowledge references
- ●Manual effort per case
- ●Work volume per FTE
- ●Cost per processed unit
- ●Risk and compliance incidents
- ●Audit findings
- ●Coverage of critical controls
- ●Active usage of AI workflows
- ●User satisfaction
- ●Stakeholder confidence
For each project, we select relevant metrics, define baselines, and track progress. If results don’t improve, we adjust — or stop.
Working within your data, systems, and risk constraints
We don’t operate AI in isolation. In every engagement we:
- Integrate with your systems and data — not "shadow tools"
- Align with risk, security, and compliance from day one
- Ensure every AI-supported decision is traceable to data and documents
- Provide clear visibility into system behavior and monitoring
What our clients trust us with
