Solutions
We build DevOps platforms on Kubernetes/OpenShift: CI/CD, SSO, security gateways, self-service deployment, and local AI infrastructure.
Our clients
DevOps
DevOps ties engineering metrics to operations: release frequency, lead time, change failure rate, MTTR, observability and service ownership.
Metrics, logs, traces, and alerts before incidents, not after business complaints.
CI/CD and infrastructure as code make releases repeatable and verifiable.
SLA/SLO, runbooks, and postmortems turn support into a system.
DevOps at KT.Team is not renting an engineer who fixes environments at night. We make production a managed business capability: the team can ship value, deploy services, provision access, see incidents, and manage AI infrastructure without constantly waiting in line for an external engineer. Simple does not mean easy here: we simplify the platform, remove manual rituals, introduce standards, loose coupling, and measurable operating rules.
An immature environment
Managed platform
| System / layer | Scope of responsibility |
|---|---|
| CPO | TTU grows: business value takes longer to travel from idea to use |
| CTO and CIO | WIP, incident risk and total cost of ownership grow |
| CDTO | It stalls AI transformation: the model or agent looks ready, but it cannot be safely connected to data, tools and production processes |
We design clusters, namespaces, quotas, storage, ingress, registry, test/stage/prod environments and operating rules. In Kubernetes we configure RBAC for API access and NetworkPolicy to control traffic between pods, namespaces and external networks based on CNI plugin support. In OpenShift we account for host, container/orchestration, build and application security layers, so the platform is a managed environment rather than a pile of YAML.
We build the pipeline from commit to production: build, tests, security checks, container registry, migrations, preview/test environments, blue-green or canary, rollback and release notes. The goal is for the product team to deploy a service to standard on its own, so DevOps never becomes the bottleneck between business result and production.
We build metrics, logs, traces, alerts, runbooks and postmortems. We manage throughput and stability indicators: deployment frequency, lead time for changes, MTTR and change failure rate. These DORA metrics show where the platform speeds up the business and where it only adds rituals.
We deploy a gateway layer for API, integrations, and AI tools: authentication, authorization, rate limits, allowlists, mTLS, WAF/API protection, policy gates, and auditing. We segment the network into zones, isolate critical services, keep secrets out of pipelines and repositories, and make access temporary and measurable.
We deploy Keycloak and compatible IAM setups on OIDC/OAuth2/SAML: a dedicated auth server, realm/client models, roles, groups, MFA, federation with AD/LDAP and service accounts. For AI agents we design SSO separately: the agent gets an identity, scope, short-lived token and audit trail - not a shared technical user with unlimited rights.
We deploy self-hosted and on-prem environments for LLMs: vLLM production stack, NVIDIA NIM, Open WebUI, private model registry, GPU scheduling, inference endpoints, quotas, latency/cost monitoring, and data isolation. For Red Hat environments, we look at OpenShift AI as a hybrid platform for open-weight models and autonomous agents.
Clusters, Helm/Kustomize, operators, ingress, a service mesh when needed, storage classes, backup, autoscaling, pod security, RBAC, NetworkPolicy, policy-as-code, resource quotas, and multi-tenant rules.
Enterprise environments on Red Hat: security context constraints, image streams, routes, builds, compliance, OpenShift GitOps, OpenShift Pipelines, and OpenShift AI for LLM/inference workloads.
GitLab CI/CD, Argo CD, Tekton/OpenShift Pipelines, environment promotion, immutable artifacts, migrations, automated tests, quality gates, rollback, and release governance without manual SSH access to servers.
Security gateways, API gateway, ingress/egress policies, DNS/TLS, certificate lifecycle, secrets management, VPN/private links, mTLS, segmentation, action logging, and incident investigation readiness.
Keycloak, OIDC/OAuth2/SAML, AD/LDAP federation, MFA, service accounts, client credentials, role mapping, delegated administration, access lifecycle management, and single sign-on for internal systems, contractors, and agents.
Self-deployment AI agents without chaos: an agent can create an MR, request a test environment, or trigger deployment only through pipeline, policy gates, approvals, sandbox, signed artifacts, and logged tool calls.
Local models, inference serving, model/runtime registry, vLLM, NVIDIA NIM, Open WebUI, GPU quotas, offline/self-host mode, data control, compliance and unit-economics calculation for LLM workloads.
Observability stack, SLI/SLO, incident response, capacity planning, backup, disaster recovery, patch management, FinOps, and training for the client team so the platform does not depend on a single external engineer.
Three jobs of the DevOps environment for an AI agent
Identity
Runtime
Control
In 1-2 weeks, we map services, environments, pipelines, access, incidents, costs, security gaps, and AI scenarios. We also identify where business value gets stuck before production use.
We design a minimally sufficient platform: Kubernetes or OpenShift, CI/CD, GitOps, SSO, security gateways, observability, backup, DR, local LLM infrastructure and operating rules. We keep only what reduces TTU, WIP, cost or risk.
We launch the first flow that actually gets used: self-service deployment of one service, SSO for contractors, monitoring of a critical process or an on-prem inference endpoint for an AI agent. A demo or test environment isn't the finish line until users start working.
We migrate services in batches so the business keeps running. We containerize, separate configuration, move secrets out, define IaC, and add health checks, readiness/liveness probes, rollback, and alerts.
We document runbooks, train the team, introduce DORA/SRE metrics and remove dependence on external DevOps for routine operations. KT.Team stays on as architectural support, but the platform becomes self-sufficient.
In the public TOCHNO Group case, the KT.Team team deployed a product development infrastructure, configured Keycloak, linked SSO with Active Directory and connected the first service - proving competence in SSO, service catalog and access for employees and contractors.
For Lenta we deployed DAM Pimcore on the client's servers, set up a Kubernetes cluster with test/prod environments, linked the solution to the data bus and website via API, and used S3 storage for images: the result is an on-prem system with no dependence on a cloud vendor.
In a DAM project for a large manufacturer and retailer, the architecture included a Kubernetes orchestrator, Pimcore, RabbitMQ, Redis, Filebeat, storage and Elasticsearch - media search dropped from 16 hours to a few minutes, and media usage controls save about 3.5M ₽ per year.
KT.Team's public AI cases describe environments where an agent works with 1C and other systems via API/MCP, Sloy turns chats, meetings, Drive, Git, tasks and finance into memory for AI agents, and a financial agent receives data via MCP and SQL; these projects require the same DevOps foundation: identity, gateways, runtime, observability and audit.
Cases
Releases go through a standard pipeline, lead time for changes decreases, the team ships small changes more often, and the business gets usable value faster, not just a demo.
Incidents are detected through monitoring, not through users. MTTR drops thanks to logs, traces, runbooks, rollback and a clear zone of responsibility.
People, services, and AI agents receive only the minimum necessary rights through SSO/IAM, network policies, security gateways, and auditing.
The client's team gets self-service deployment, documentation, runbooks, and clear operating rules. An external expert is needed to evolve the platform, not for every release.
Local models, an MCP/API gateway, agent SSO, a sandbox, and an audit trail make it possible to connect AI to real processes without moving sensitive data outside.
Quotas, autoscaling, FinOps, observability, and environment standards show where the platform spends money and where it saves development time, downtime, and incident risk.
Where to start
1-2 weeks
A safe first step is not a contract for a full rebuild, but a short diagnostic: a map of services, environments, pipelines, access, incidents, costs and security gaps, followed by a minimally sufficient target architecture tuned to TTU, WIP, cost and risk.
