Put your AI work into a working system.
Practical orchestration, operator tools and reusable engineering. Learn the method. Explore the technology. Build your next workflow with us.
Interactive illustration · no live scans or agent telemetry
Build something useful.
All solutions →Pick the outcome you need. Each scope comes with deliverables and a concrete acceptance test.
AI workflow pilot
Move one repetitive process from chat into a bounded, reviewable workflow.
DSHREM terminal onboarding
A project-aware terminal workflow with accountable execution.
Telegram operations cockpit
See progress and respond to authorised decisions without watching a terminal.
Research spider integration
Collect useful evidence from a declared source list.
Agent evaluation workshop
Choose a worker by accepted results, with failures included.
3D intelligence interface
Make a complex workflow understandable through an interactive visual map.
The technology, explained.
AI orchestration: from a prompt to an accepted resultA practical design for delegation, evidence and recovery.DSHREM: a project-aware AI terminalSessions, scoped work and receipts before unattended automation.Telegram as an operator interfaceProgress, trusted decisions and a clear boundary around control.SPIDER: a source-to-evidence workflowDefine the sources, preserve provenance and make missing data visible.Shared memory without shared mistakesKeep facts, hypotheses and permissions distinct across agent sessions.What AI worker taught us about acceptanceEvaluate completeness and accepted outputs, not fluent completion messages.Brains: make the system understandableAn interactive map should explain a workflow before it claims to monitor one.Trust the sender, not a public checksumBind the complete message to an explicitly trusted authority.Two swarm models: a terminology comparisonA dated comparison of self-organisation and controlled engineering workflows.
Engineering case library (14)
Engineering that can show its workTVIJO connects operators, bounded agent work and knowledge that can be checked and reused.AGENTORCH: the orchestrator has a concrete jobAGENTORCH names the orchestration role: plan, assign, observe, reconcile and accept.The task packet is the interfaceA good task describes a verifiable outcome rather than an invitation to improvise without limits.AGENTJUDGE: acceptance follows evidenceA reviewer checks the task, the artifact version and each acceptance criterion.Measure cost per accepted resultSmall task benchmarks illustrate why task class and acceptance matter more than a cheap model label.Budgets and outages belong in the state machineA reliable operator view distinguishes working, waiting, failed, blocked and accounting uncertainty.Keep VECTORBASE, evaluate retrievalUse a private source-linked index to find evidence; do not treat search results as authority.SPIDER: evidence collection and useful progressThe first implementation reads an explicit project-evidence list and produces a dated operator digest without model calls.DSHREMBOT and tmux: one operator viewAuthenticated project commands, budget visibility and explicit schedule controls connect the operator to existing execution lanes.The AI worker–Hugging Face incident: learn from controlsThe incident reports support specific engineering lessons, not a controlled ranking of our system.What the supplied AI worker screenshot actually provesThe screenshot records a safeguard-triggered switch in one session; it does not explain the exact trigger. The observation is the supplied screenshot dated 15 October 2026.Training our own model: a gated research directionA proprietary model is an option to test after retrieval, prompts and routing have measurable limits.A bilingual knowledge product with visible limitsPublish useful explanations first, then expand languages when the evidence and workflow are ready.Free learning, custom projects and future challengesTurn evidence-backed engineering into clear offers and a useful developer community.
Understand the workflow.
Explore the map above, then test one important idea. Useful automation needs more than a confident answer.
Turn the method into your project →