DevOps foundations
Hardened hosting, SSO, backups, monitoring, and infrastructure as code so the system is owned, recoverable, and easier to support.
- Authentik SSO
- Borg backups
- Prometheus + Grafana
Services
DevOps as a Service, workflow software, and AI systems built to stay reliable.
We plan and ship the operating layer around the app: identity, hosting, backups, data flow, automation, and AI features that are measured and observable in production.
Workflow software
Customer portals, internal tools, and operational systems that remove duplicate entry and make the process visible to the right people.
- Internal tools
- Customer portals
- Scheduling and CRM
AI implementation
Knowledge search, copilots, extraction, and routing systems that make work faster without losing the context a team needs.
- Retrieval and search
- Document extraction
- Routing and summarization
Reliable delivery
Observation, testing, rollout, and support practices that keep systems predictable long after the first release.
- Monitoring
- Regression tests
- Support handoff
AI implementation
AI that reduces manual work without becoming a mystery box.
We only add AI where it solves a real workflow problem. That means retrieval from trusted content, structured outputs, clear permissions, and a human path when the result is too important to automate blindly.
Knowledge search
Search internal docs, proposals, support notes, and manuals without asking people to remember the exact filename or folder.
Document extraction
Turn PDFs, emails, and forms into structured records that can drive the rest of the workflow.
Drafting and summarization
Create summaries, first-pass responses, and meeting notes faster while keeping a human in the loop.
Routing and triage
Classify requests, assign them to the right queue, and escalate only when a person needs to step in.
Reliability
Every AI feature needs guardrails, not just a prompt.
Reliability is part of the implementation, not a follow-up patch. We design for evaluation, logging, fallbacks, and safe rollouts so the system keeps working as usage grows.
Evaluation sets
We test prompts and workflows against curated examples so the system can be measured, not just admired.
Scoped access
Retrieval and tool access are limited to the data and actions the workflow actually needs.
Version control
Prompts, models, and workflow rules can be changed without losing track of what is live.
Fallback paths
If confidence drops or an upstream dependency fails, the system can fall back to a deterministic workflow.
Observability
Logs, metrics, and usage data show what the model did, how often it was right, and where it needs improvement.
Human review for risk
When the outcome matters, we keep approval steps in place so AI supports the process instead of replacing it blindly.
What we ship
One team can own the stack, the workflow, and the AI that sits inside it.
That is the point of the service: a clearer operating model, fewer subscription layers, and systems that stay understandable when the original demo is long gone.
Clear ownership
Secure data flow
Reliable rollout
Support after launch
Next step