Start Practical
Begin with a rescue call or small audit before committing to a larger rollout.
AIML Solutions helps teams roll out hardened OpenClaw, OpenCode, NemoClaw, and MCP-based workflows with scoped permissions, reproducible evaluation, validation-first data pipelines, and cloud-ready operating practices.
AIML Solutions helps teams make agentic AI workflows safer, more reproducible, and easier to operate. The work focuses on scoped runtimes, evidence artifacts, evaluation environments, and validation-first data systems.
Begin with a rescue call or small audit before committing to a larger rollout.
Every engagement aims to leave behind a runtime map, handoff note, report, test path, or proof artifact.
Credentials, private data, paid-platform details, and client-sensitive facts stay out of public materials.
Fixed-scope starter engagements and hourly consulting are available for teams adopting agentic tools, coding-agent evaluation, and AI/data/cloud workflows. Engagements can start with a small audit before moving into implementation or monthly support.
60-90 minute live diagnostic for broken or unstable agent runtimes, VPS setups, Docker issues, or OpenClaw/NemoClaw workflows.
Set up a scoped, documented agent runtime on VPS infrastructure.
Review an existing agent workflow for risks, boundaries, and reliability gaps.
Turn agent work into auditable episodes with reusable evidence artifacts.
Inspect Docker/Kubernetes/PyTest-style evaluation environments for reproducibility and deterministic scoring.
Classify APIs and public feeds by availability, freshness, provenance, cost, and ingestion risk.
MultiClaw OS ties the portfolio into one commercial system: runtime operations, harness artifacts, evaluation workflows, intelligence gathering, data validation, cloud deployment patterns, and service delivery.
VPS, Mac mini, containers, OpenClaw, OpenCode, NemoClaw, Hermes Agent, shell access, and recovery notes.
Scoped workspaces, least-privilege tools, browser/session boundaries, and public/private separation.
Task specs, tool registries, verification reports, failure attribution, entropy audits, and episode packages.
Docker/Kubernetes/PyTest/verifier workflows for coding-agent and frontier-model evaluation.
IntelliClaw for public-source OSINT, research feeds, market context, job/company signals, and event tracking.
QuantTools for provider readiness, provenance, freshness metadata, and source-matrix planning.
CloudInfra for Terraform, Kubernetes, local-first deployment patterns, and VPS hardening documentation.
Service Patterns for FastAPI, Pydantic schemas, tests, Dockerfiles, and prototype-to-API handoff.
Technical reviewer for OpenClaw AI in Production by Ken Huang, reviewing code, sequencing, reproducibility, dependencies, lifecycle, and security-boundary issues before publication.
Works on frontier-model evaluation workflows involving reproducible environments, task setup, transcripts, tool-calling behavior, deterministic scoring, and failure recovery review.
15+ years of financial-risk, watchlist, entity-resolution, SQL, Python, and data-quality experience informing current AI/data engineering work.
Public materials use sanitized examples. Private platform details, credentials, paid-task specifics, and client-sensitive information are excluded.
Agentic AI solutions engineer and technical reviewer based in Reno, NV. Operates hardened VPS-hosted OpenClaw/OpenCode/NemoClaw-style multi-agent runtimes, contributes to frontier-model evaluation workflows, and brings 15+ years of financial-risk data engineering experience.