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Tools

Products we build (and the standards we ship them with)

Most “tooling portfolios” are a graveyard of half-finished demos. Ours is built around one rule: if it can’t be measured, operated, and explained, it doesn’t ship.

Local-firstDeterministic behaviorBenchmarks + gatesAudit evidenceOperator UXSecurity-by-default
Core products
Production-grade tools and platforms we actively build and refine.
Nodus
Secure Sync · DesktopPrivate

A local-first desktop sync system built for deterministic execution, auditability, and verifiable transfer evidence.

  • Policy-driven jobs: explicit endpoints, filters, delete behavior, and commit semantics
  • Deterministic scan → plan → run pipeline with structured progress/events
  • Receipt-backed integrity using strong hashing + verification workflows
  • Capsule packaging for portable, replayable transfer evidence
TauriRustSvelteSQLiteIntegrity
WispDB
Vector DB · RetrievalActive R&D

A browser-native vector database with WebGPU scoring, worker-first execution, and persistence that survives refresh.

  • Exact search + IVF-Flat for scalable retrieval without “demo-only” shortcuts
  • Worker-first runtime to keep UI responsive under real workloads
  • IndexedDB snapshots + append-only journal for reliable persistence
  • Benchmarks + quality gates to track latency, recall, and regressions
WebGPUVector SearchWorkersIndexedDBBenchmarks
Invar
Developer Tool · API TestingPrivate

A minimalist, local-first API requester and response inspector designed for speed, clarity, and deterministic validation.

  • Compose requests cleanly: methods, headers, bodies, params, variables, environments
  • Inspect responses with structured metadata and readable previews
  • Checks and validations (status, headers, JSON paths, schemas) to prevent drift
  • Local-only by design: no forced accounts, no cloud dependency
TauriRustTypeScriptValidationLocal-first
ZeroTrace
Security · Data DestructionPrivate

Secure, verifiable deletion workflows with guardrails, receipts, and audit-ready outputs for modern systems.

  • Controlled shredding with overwrite profiles and metadata cleanup (where supported)
  • Free-space sanitization and snapshot/shadow-copy handling (platform dependent)
  • Receipts with integrity binding and optional signature support
  • Dry-run planning and explicit flags to reduce accidental destructive ops
C++ReceiptsAuditGuardrailsLocal-only
Ripcord
Windows Erasure PlatformPrivate

A Windows-focused secure erasure platform built for deterministic, auditable destruction with cryptographic evidence.

  • Operator-friendly UI + automation-ready CLI for repeatable workflows
  • Receipt and attestation system with verification and export paths
  • Optional broker mode for privilege isolation and safer execution boundaries
  • Built-in erase profiles aligned with common operational and compliance needs
.NET 8WinUI 3CLIReceiptsWindows
Vyre
Networking · DiagnosticsPrivate

Cross-platform Wi-Fi analysis and diagnostics: clean data, explainable insights, and local-only operation.

  • Discovery + analysis with normalized metadata across platforms
  • Evidence-based diagnostics: channel congestion, signal issues, security posture
  • Exportable reports (JSON/CSV/HTML) for offline analysis and comparison
  • Performance-first core with a modern cross-platform UI
C++20.NET MAUIWirelessReportsNo telemetry
Labs & prototypes
Systems R&D that sharpens our engineering edge and becomes product-ready when it earns it.
Aevrix
Runtime · Deterministic RenderingPrototype

A deterministic, privacy-first first-paint engine in Rust with a desktop host for controlled rendering experiments.

  • Deterministic pipeline across HTML tokenization, CSS cascade, layout, and paint planning
  • CPU raster output for stable, reproducible first paint
  • FFI bridge for host integration + headless CLI harness
  • Offline-first defaults by design, with capability-gated networking direction
RustAvaloniaFFIDeterminismOffline-first
Sylphos
Systems R&D · Fetch → Parse → PresentPrototype

A Rust workspace exploring a clean end-to-end pipeline: hardened fetch, minimal DOM normalization, and GPU-backed presentation.

  • Hardened HTTP/TLS fetch layer with safeguards and predictable behavior
  • Minimal HTML-ish DOM stack with deterministic serialization goals
  • Native window + GPU render loop for present-layer experiments
  • Structured dev workflow for reproducible iteration and validation
Rusthyper+rustlswgpuwinitTooling
KoMaKo Inference
AI InfrastructureInternal

Inference primitives and integration patterns we use to ship production AI systems safely and repeatably.

  • Operational patterns for model routing, rollbacks, and controlled release
  • Evaluation-aware integration: measure quality, then ship changes safely
  • Telemetry conventions that connect model behavior to user outcomes
EmbeddingsRerankingOpsEvalsReliability
Want one of these built for your environment?
We can adapt these capabilities to your stack, data, constraints, and compliance posture. You’ll get measurable acceptance criteria, a deployable system, and artifacts your team can maintain.
Maintenance

Reliability engineering, not “support”

“Support” is reactive. Reliability engineering is prevention-first: clear baselines, controlled change, measurable quality, and incident response that actually improves the system instead of producing more meetings.

SLOs + Dashboards
uptime, latency, errors, cost
Controlled Change
release notes, rollbacks, approvals
Prevention Cycle
observe → measure → fix → prevent
What we operate
Platform & product ops
  • Stability: bug fixes, regression control, reliability improvements
  • Performance: latency tracking, cost profiling, targeted optimization
  • Security hygiene: dependency updates, secrets practices, safe defaults
  • Resilience: backups, recovery checks, auditable change history
  • Observability: logs, metrics, tracing, alerting tuned to user impact
AI ops (where it gets real)
  • Drift monitoring + periodic quality checks against evaluation sets
  • RAG health: retrieval accuracy, indexing freshness, source coverage
  • Guardrails & policy tuning from real usage + failure modes
  • Model/version rollouts with rollback paths and tracked changes
  • Eval regressions so “it got worse” doesn’t ship silently
What you get every month
Delivery artifacts
  • Weekly change log: shipped work, risk notes, next priorities
  • Incident reports with timelines, impact, and corrective actions
  • Postmortems that produce prevention items (not blame theater)
  • Runbooks + on-call notes that reduce MTTR over time
Quality guardrails
  • Regression suite updates when new failure modes appear
  • Alert tuning to reduce noise and increase signal
  • Release gating for high-risk changes (AI + infra)
  • Cost ceilings and performance budgets, tracked and enforced
Maintenance as a discipline: observe → measure → improve → prevent recurrence. That’s the job.
Baselines & guarantees
SLOs
Targets for uptime, latency, error rate, and cost
Response
Same-day critical triage + tracked incident timeline
Change
Release notes, approvals, rollback-ready deployments
Visibility
Weekly report: what shipped, what changed, what’s next
Recovery
Backups + restore verification (RTO/RPO aligned)
Engagement models
  • Retainer: steady capacity for continuous improvement
  • SLA: production-critical systems with response guarantees
  • On-call: incident readiness + postmortems + prevention work
Optional: Stabilization Sprint
A short baseline phase to set SLOs, dashboards, alerts, evals, and the initial hardening plan.
Production systems deserve grown-up ownership
If your stack includes AI, retrieval, or integrations that can fail in creative ways, we’ll help you measure quality, reduce incidents, and ship changes safely without slowing delivery to a crawl.