AI agents
Agents for intake, triage, research, routing, review, and follow-up with approval gates where the workflow needs accountability.
AI automation servicesSan Francisco · Bay Area · Sacramento
AI automation
for teams that need production systems.
ArtJeck Technology builds AI agents, RAG assistants, workflow automation, and custom product systems for teams in San Francisco, the Bay Area, and Sacramento. The work is closer to boutique software engineering than template automation: fewer manual steps, clear source grounding, custom integrations, and QA checks before launch.
Capabilities
One automation stack, four kinds of leverage.
Every engagement draws on the same four capabilities: AI agents for repeated decisions, RAG assistants for internal knowledge, integrations that stop manual re-keying, and QA that keeps all of it trustworthy.
RAG assistants
Cited assistants over product docs, SOPs, policies, tickets, PDFs, spreadsheets, and internal knowledge sources.
Workflow integrations
Automation across forms, CRMs, spreadsheets, APIs, email, dashboards, and internal tools so data stops being copied by hand.
QA-tested production AI
Continuous evals, deterministic checks, regression cases, edge-case coverage, and launch gates before automation touches real work.
Positioning
Production AI engineering, not template automation.
The strongest niche for ArtJeck is custom AI automation where reliability, proprietary systems, data handling, and software quality matter.
Built past the demo
AI systems are tested against edge cases, source-grounding failures, hallucination risk, and regression cases before they are trusted with customer or staff workflows.
Custom systems, not template glue
The work can include non-standard APIs, internal databases, dashboards, web apps, iOS interfaces, and approval flows when off-the-shelf automation cannot cover the process.
Vertical workflow automation
Good targets include compliance parsing, appointment scheduling, document intake, reporting, logistics operations, client verification, and data-heavy review workflows.
Product discipline around AI
Exact calculations stay in deterministic code, language and judgment go through AI, and the surrounding product experience makes outputs reviewable and actionable.
Local Pages
Where ArtJeck works.
ArtJeck is based in Sacramento and works with businesses across Northern California. Each area page covers the local industries and workflows that fit automation best.
01
AI Automation San Francisco
Prototype-to-production AI for San Francisco startups: agents, RAG assistants, and AI features hardened with evals, guardrails, and regression tests.
Open page02AI Automation Bay Area
Operations automation for Bay Area businesses: intake, routing, reconciliation and reporting — AI where it helps, deterministic code where it counts.
Open page03AI Automation Services Sacramento
AI automation services in Sacramento, CA: AI agents, RAG assistants, and workflow automation for local businesses — built, tested, and supported locally.
Open pageProof
Proof over promises.
Every claim on these pages traces back to shipped systems: real clients, real tests, real filings. Start with the IFTA case study to see how a production AI workflow is actually built and verified.
Current proof asset
IFTA Agent case study
- Live AI-assisted filing workflow with deterministic tax math and an AI review agent.
- 562 automated tests and a real-data backtest that matches a prior filing to the penny.
- This proof supports the AI automation pages because it shows production engineering, eval discipline, and workflow automation.
Questions
What teams ask before starting.
Service-level answers. The location pages cover what automation looks like in each area.
What does AI automation actually mean in practice?
Taking a workflow your team runs by hand — intake, triage, document review, reconciliation, reporting, follow-up — and rebuilding it so software does the repetitive part and a person handles the judgment. In practice that usually means exact calculations stay in deterministic code, language and classification go through a model, and anything irreversible waits for human approval. It is closer to custom software engineering than to configuring an off-the-shelf tool.
How do you decide whether a workflow is worth automating?
Good signals: someone does it every week, it involves documents or structured files, the rules can be written down, and mistakes are costly. Poor signals: it depends entirely on relationships, it happens twice a year, or the rules change every time. If a workflow falls into the second group, you will be told that at the intro call rather than sold a project that cannot pay for itself.
Do you replace the tools we already use?
Usually not. Most of the expensive manual work happens between systems — form to spreadsheet, email to record, invoice to ledger — rather than inside any one of them. Automation connects the tools you already run through their APIs instead of asking your team to learn something new and migrate their data into it.
How do you stop an AI system from making things up?
Three ways, layered. Arithmetic and rule-following stay in deterministic code, so the model never does maths. Retrieval-based answers are grounded in your own documents with citations, and the system is built to say it does not know rather than guess. Then evaluations built from your real cases run on every change, so a regression shows up as a failed test rather than as a wrong answer in front of a customer.
Can you take over an AI prototype our team already built?
Yes, and it is one of the more common engagements. The work starts with an audit of the existing code and its failure modes, then a written scope for hardening it: structured outputs, error handling and retries, guardrails, observability, evaluations, and regression tests — the gap between something that demos well and something you can put in front of users.
What does a project cost, and how long does it take?
AI automation projects start at $8,000 against a fixed quote, with advisory work at $150/hour. Most first projects — one workflow, built, tested and deployed — take two to six weeks depending on the systems involved. Scope and price are agreed in writing before any build work starts, and there are no open-ended retainers.
Who owns the system once it is built?
You do. All the code, all the accounts, all documented and handed over. There is no licensing arrangement, no per-seat fee, and no dependency on ArtJeck to keep it running. The point of documenting and testing the handover is that your team can maintain it without calling anyone.
What happens when the automation gets something wrong?
The system is designed around that question rather than assuming it will not happen. Deterministic code handles calculations, review gates sit before anything irreversible, tests and evaluations run before launch, and the automation runs alongside your existing manual process on real work until the outputs agree. When something does fail, it fails visibly — flagged as an exception — instead of silently.
Start with one workflow
Send the manual process you want to reduce.
Include the location, workflow, files or systems involved, and what a successful result would look like.