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SoftSpark

Commerce infrastructure, production delivery, and AI systems for teams that cannot afford fragile operations.

SoftSpark is a founder-led engineering company with 12+ years of operator experience. We design, build, and stabilize revenue-critical platforms. Our work stays close to production: architecture, infrastructure, releases, observability, recovery, and engineering automation.

softspark.eu · biuro@softspark.eu

Founder

SoftSpark is built and led by Łukasz Krzemień, a software and infrastructure engineer who works directly on architecture, implementation, delivery, and production operations.

Łukasz combines commerce-platform experience with infrastructure automation and AI engineering. His work spans Magento and PHP systems, cloud and VPS infrastructure, CI/CD, observability, knowledge retrieval, MCP servers, coding-agent platforms, and multi-model orchestration.

The operating principle is simple: important systems need explicit ownership, measurable acceptance criteria, repeatable delivery, and a tested recovery path.

What We Build

Area Scope Typical stack
Commerce platforms Catalog, checkout, search, cache, PIM, integrations, multi-store operations Magento 2, PWA, Akeneo, Symfony, Pimcore
Infrastructure and delivery Provisioning, containers, CI/CD, observability, backup, recovery Terraform, Terragrunt, Ansible, Docker, AWS, Azure, Hetzner
AI delivery systems Coding-agent rules, knowledge retrieval, MCP servers, model routing, multi-agent workflows Claude Code, Codex, Copilot, Gemini, MCP, RAG, CRAG
Operational enablement SOPs, runbooks, architecture decisions, incident workflows, team tooling GitHub Actions, GitLab CI, OpenTelemetry, Prometheus, Grafana

Commerce Engineering

We work on the application paths where latency, deployment risk, and integration failures affect revenue.

Capability Delivery scope
Platform engineering Magento 2 B2C and B2B, headless storefronts, middleware, extensions, upgrade paths
Performance Varnish, OpenSearch, queues, cache policy, PHP runtime, database diagnostics
Product data Akeneo PIM, catalog normalization, feeds, imports, exports, synchronization
Operations Multi-store releases, monitoring, incident loops, rollback procedures, peak-readiness checks

Infrastructure and Delivery

Our infrastructure work targets repeatable changes and recoverable failures. The stability target for release-critical paths is 99.95%.

Layer Implementation
Provisioning Terraform and Terragrunt for Hetzner Cloud, AWS, Azure, and hybrid VPS environments
Automation Containerized Ansible, explicit inventories, idempotent playbooks, configuration ownership
Delivery CI/CD validation, zero-downtime deployment patterns, health checks, rollback gates
Operations Structured logs, metrics, tracing, backups, restore tests, capacity and cost reviews

AI Delivery

We treat AI as an engineering system, not a chat window. Rules, permissions, retrieval, evaluation, and release gates remain explicit.

Layer SoftSpark implementation
Engineering toolkit ai-toolkit distributes 109 skills, 44 agents, hooks, audits, and shared policies across 12 developer-tool integrations
Work management jira-mcp connects MCP clients to multiple Jira instances with ADF formatting, caching, and comment templates
Knowledge Hybrid retrieval, corrective RAG, multi-hop search, reranking, evaluation sets, and knowledge-gap tracking
Model access dsh-codex connects DeepSeek Harness to a locally authenticated Codex app server using a ChatGPT subscription
Orchestration dsh-orchestrator delegates bounded tasks to Claude Code and GitHub Copilot Gemini through native subscription logins
Control plane Approval gates, least-privilege tools, credential boundaries, SARIF audits, signed package provenance, human review

How We Work

  1. Diagnose. Inspect architecture, release flow, runtime evidence, operational risk, and the actual bottleneck.
  2. Design. Define ownership, interfaces, environments, migration steps, failure modes, and measurable acceptance criteria.
  3. Codify. Implement infrastructure, services, pipelines, policies, tests, SOPs, and runbooks.
  4. Ship. Release small reversible changes with automated gates, health verification, and a rollback path.
  5. Transfer. Leave the team with documented systems, maintainable tooling, and evidence they can reproduce.

Typical Engagements

  • Stabilize Magento, PWA, search, cache, queue, or integration paths that affect conversion and release safety.
  • Standardize infrastructure that has grown through manual changes and undocumented dependencies.
  • Build CI/CD, observability, backup, restore, and incident procedures around an existing production platform.
  • Convert AI experiments into governed tools with retrieval, permissions, evaluation, audit trails, and human approval.
  • Create reusable engineering systems: modules, MCP servers, agent workflows, SOPs, runbooks, and knowledge bases.

Open Source

Project Purpose Distribution License
ai-toolkit Shared skills, agents, hooks, policies, audits, and cross-tool generators npm Apache-2.0
jira-mcp Multi-instance Jira MCP server with ADF conversion and local task caching npm Apache-2.0
dsh-codex DeepSeek Harness provider for locally authenticated Codex app-server sessions npm Apache-2.0
dsh-orchestrator DeepSeek Harness bundle for Claude Code and Copilot Gemini delegation npm Apache-2.0
gitspace Git identity, GitHub account, SSH key, signing, and audit controls bound to workspace directories npm Apache-2.0
mage2x Magento and shell command runner for Docker, Podman, and Kubernetes with target and production safeguards npm Apache-2.0

Every public module starts at 1.0.0, ships with Apache-2.0 and NOTICE, and uses automated supply-chain verification appropriate to its distribution channel.

Selected Building Blocks

Docker Terraform Ansible AWS Azure Hetzner Magento PHP Python Node.js TypeScript PostgreSQL

Contact

Architecture review, commerce stabilization, infrastructure automation, or AI delivery system:

softspark.eu · biuro@softspark.eu

This profile is licensed under Apache-2.0. See NOTICE. Linked repositories carry their own licenses.

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