Independent expertise and strategy for your digital transformation. We analyze infrastructure, identify weaknesses, and develop an actionable AI implementation plan.
From Complexity to a Clear Roadmap
Making modernization decisions without a complete picture is a risk. Our engineering consulting is not a high-level report, but a deep systemic analysis of your IT and production ecosystem.
We answer key questions: How ready is your infrastructure for AI? Where are the hidden bottlenecks? Which investments will deliver maximum return? Our goal is to provide you with afeasibility study (FS) for informed decisions.
Key audit directions
We conduct comprehensive diagnostics across key vectors critical for the stability and development of a company’s technology stack.
01
Audit of IT and production infrastructure
Analysis of servers, networks, storage systems (SAN), cloud environments, industrial equipment (IIoT), and backup systems. Assessment of performance, fault tolerance, and scalability.
02
Analysis and optimization of business processes
Identification of manual operations, bottlenecks in document flow, and inefficient decision-making cycles. Determination of process candidates for automation (RPA) and AI implementation.
03
Assessing readiness for AI and data science implementation
Analysis of data quality, structure, and volume. Assessment of computing power and team competencies. Development of a phased AI implementation roadmap with ROI calculation.
04
Cybersecurity and compliance audit
Checking perimeter protection, access policies, monitoring systems. Assessment of compliance with industry standards (ISO 27001, PCI DSS, NIST, etc.) and regulatory requirements.
05
Project management audit
Assessment of the maturity of project processes, methodologies (Agile/Waterfall), and planning tools. Analysis of risks, communications, and team effectiveness. Development of recommendations to improve predictability of deadlines, budget, and quality of results.
Who we help
Our work stages
- Step 1: diving and data collection – On-site interviews, document analysis, metric collection.
- Step 2: diagnostics and modeling – Using analysis tools, building an “as-is” model.
- Step 3: risk and opportunity analysis – Assessment of each risk (downtime, security) and opportunity for optimization.
- Step 4: roadmap development – Proposal of development scenarios (“to-be”) with budget, timeline, and impact estimation.
- Step 5: presentation and protection of results – Preparation of a final report and its presentation to technical and business stakeholders.
What the client receives as a result
Initial situation
A large agri-holding: low accuracy of yield forecasts, losses during storage, suboptimal logistics.
Revealed
- Disparate data collection systems (field sensors, warehouse ERP), no single framework.
- Historical data is not structured for AI analysis.
- Insufficient computing power to run predictive models.
Roadmap
- Short-term:Data consolidation in cloud storage, a pilot forecasting project for one region.
- Medium-term:Development and implementation of a computer vision model for field monitoring.
- Long-term:Integration of AI models into ERP for automatic logistics and resource planning.
Effect: the client received a prioritized plan with ROI calculation, which enabled them to secure funding and begin implementation.
Ready to get an objective picture and a clear plan?
Contact us to discuss audit goals and scope. Primary analysis can identify up to 80% of critical risks.
