From software engineering and AI governance to legacy modernization and cloud cost optimization, we empower your digital transformation.
Modern cloud-native architectures, microservices, CI/CD automation pipelines, and high-availability enterprise software engineering.
Enterprise AI roadmap, regulatory compliance (EU AI Act, GDPR), model auditing, risk mitigation frameworks, and AI Center of Excellence (CoE) setup.
Seamless modernization of monolithic architectures, legacy databases, and core business logic into scalable, API-first enterprise platforms with zero downtime.
On-premise deployment, fine-tuning, and private orchestration of top open-source LLMs within sovereign, air-gapped enterprise environments.
High-throughput API gateway implementation, secure middleware architecture, and seamless integrations across core ERP, CRM, and enterprise platforms.
FinOps methodologies, GPU/CPU resource right-sizing, and architectural refactoring to eliminate cloud waste and reduce infrastructure spend by 30–50%.
Diagnostic analysis of manual workflows and end-to-end process re-engineering using autonomous AI agents, predictive workflows, and automated decision engines.
We look beyond the invoice at everything that drives it: process, architecture, code and configuration. For AWS, Google Cloud and Microsoft Azure.
What is cloud cost optimization (FinOps)? It is the practice of reviewing cloud spend across process, architecture, code and configuration, removing unnecessary cost and keeping the budget under control for good.
Average annual savings
~40%For every ₺1 million of cloud spend, about ₺400 thousand stays in the company
process · architecture · code · configurationAnnual cloud cost
Why we are different · we look at four layers, not invoice lines
OpenAI, Anthropic, Gemini or on-prem, it does not matter: every call passes through, is monitored and managed. Team-based virtual keys, budgets and quotas; cost and energy in the same panel.
What is an AI gateway? It is the single entry point every AI call in the company goes through. It manages identity, policy, model routing, caching, logging and budget control in one place, and lets teams use different providers and in-house models through the same interface.
Cost · today
₺4,820/ 62% of monthly budgetTeam quotas · alert at 80% · automatic limit at 100%
Who uses what
Idle & risk detection
Energy & resources · on-prem
Cost and energy in one panel; model choice backed by data
Figures in the panels are sample data.
For organizations that cannot send data to the cloud: 100% isolated AI on your own servers. Full performance on an air-gapped network; KVKK, BDDK and ISO 27001 compliance.
What is on-premise (closed-loop) AI? It means running AI models and data entirely on the organization’s own servers, on a network with no internet connection (air-gapped). Data never leaves the organization.
Sensitive defense, finance and customer data never leaves; full performance on an air-gapped network.
GPU sizing, Kubernetes cluster management, local optimization of open-source and custom models.
Every portal, application and business unit is fed by the central local AI infrastructure through secure APIs.
Fine-tuning and RAG architecture compliant with KVKK, BDDK, ISO 27001 and defense industry standards.
It depends on the infrastructure. In our engagements the average annual saving is about 40%; the exact figure is clear after the first analysis.
We work with AWS, Google Cloud and Microsoft Azure, and we also handle multi-cloud setups in a single view.
When teams use different AI services separately, cost, security and data flow get out of control. An AI gateway routes every call through one point, so identity, budget, policy and logging are managed in one place.
No, not once a policy is in place. Requests containing personal data are automatically routed to the in-house model.
It depends on your needs. We size the GPUs based on user count, model size and response-time targets, and install the hardware turnkey.
Open-source models such as Llama and DeepSeek deliver enough performance for many enterprise tasks when combined with RAG and fine-tuning on company data, and they keep the data in-house.
Let's Lead the New Era Together. Build your company's AI advantage starting today.