Most San Francisco teams don't need another AI demo — they already have one. What they need is the demo turned into a system: an agent or RAG assistant that behaves the same way on Monday as it did in the pitch, fails loudly instead of silently, and ships with the evals and regression tests to prove it. That is the work ArtJeck Technology does for SF startups — taking one AI workflow from prototype to production, or building it production-grade from the start.
Anyone can wire a model to an API in an afternoon. The production questions — what happens on malformed input, who approves the risky action, how do you know accuracy didn't drop last Tuesday — are software engineering questions, and they are the ones this studio answers.
Take the agent or assistant your team already validated and rebuild the fragile parts: structured outputs, error handling, retries, guardrails, observability, and a test suite that catches regressions before your users do.
Agents for research, lead qualification, support triage, and back-office work — designed so the deterministic steps are exact, the judgment steps are evaluated, and the risky steps wait for a human.
Assistants over product docs, engineering notes, tickets, and internal knowledge that answer with citations, admit when the corpus doesn't cover the question, and escalate instead of hallucinating.
Eval datasets built from your real cases, golden answers, deterministic checks, and CI integration — so "the AI seems fine" becomes a measured claim instead of a hope.
The pattern is consistent: the prototype works, the deadline is real, and the last 20% — reliability, edge cases, evals, deployment — turns out to be 80% of the work. These are the projects that close that gap.
An AI feature that works in the founder's demo but hasn't survived contact with real user input — hardened with structured outputs, fallbacks, and regression tests.
A RAG assistant over product docs and support history that must cite sources and admit uncertainty before it is allowed in front of customers.
An internal agent for research, lead qualification, or support triage that needs approval gates before it touches the CRM or sends an email.
An AI MVP that has to combine Next.js, APIs, a database, and model calls into one deployable, testable release.
Document and data extraction — contracts, PDFs, spreadsheets — where the output feeds a workflow, so accuracy has to be measured, not assumed.
An eval harness for a model-dependent feature, so the team can upgrade models or prompts without fear of silent regressions.
Useful automation starts with the workflow, not the model. Each step reduces uncertainty before the system is trusted with more responsibility.
Read the code, map the failure modes, and measure baseline behavior on real inputs. You get a written gap list between what exists and what production needs.
Rebuild the fragile parts: structured outputs, retries and timeouts, input validation, guardrails, and human approval wherever an action carries risk.
Turn real cases into eval datasets and golden answers, add deterministic checks, and wire it into CI — so every change proves it didn't break behavior.
Deploy with monitoring, review paths, and rollback. Then tune prompts, retrieval, and rules against production data instead of guesses.
ArtJeck's AI automation work is grounded in deterministic software, model evaluation, and QA discipline.
ArtJeck Technology is a Sacramento, California software studio run by founder Evgenii Menshikov. It builds AI agents, RAG assistants, and workflow automation for businesses in the San Francisco, CA, working on-site where it helps and remotely everywhere else. Every system ships with automated tests and model evaluations before it touches real work.
Sales Agent
Multi-marketplace resale automation — prototype to production
Yes — that is the most common San Francisco engagement. The work starts with an audit of the existing code and its failure modes, then a written scope for hardening: structured outputs, guardrails, evals, regression tests, and deployment.
No. The typical setup is working alongside a founding team: ArtJeck owns the AI workflow — agent design, retrieval, evals, QA — and hands it back documented and tested, so your team can maintain it without depending on the studio.
Yes. Projects can include product UX, Next.js or iOS development, APIs, databases, model integration, evals, and deployment — one accountable engineer across the full stack.
Two public case studies. The Sales Agent system is real, paid client work: multi-marketplace automation with deterministic listing rules and idempotent scheduled runs. The IFTA agent files real fuel-tax returns with 429 automated tests and a backtest that matches a prior filing to the penny.
Exact calculations stay in deterministic code. Model behavior is measured with evals built from real cases, grounded in sources, and covered by regression checks — with human approval wherever the workflow needs accountability.
ArtJeck works with businesses across Northern California — from Sacramento to San Francisco and the wider Bay Area. Pick the page closest to you, or start from the full AI automation services overview.
San Francisco
Prototype-to-production AI for startups: agents, RAG assistants, evals, and launch QA.
Bay Area
Cross-industry operations automation: intake, reconciliation, reporting, and back-office workflows.
Sacramento
Workflow automation for local operators, reporting, logistics, and compliance.
Also serving Bay Area, Sacramento. See all AI automation services.
Start with one workflow
Include what the workflow is, who handles it, what files or systems are involved, and what a successful result would look like.