I build custom AI workflows for Los Angeles ops teams that have outgrown Zapier and need real integrations with their stack.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Los Angeles mid-market ops leader or startup whose team spends 20+ hours a week on repetitive document and data work. LA tech pool skews creative/frontend; engineering-first senior talent is scarce.
The pain today
- Your team has hit the Zapier complexity ceiling
- Vendor AI demos look great but never integrate with your stack
- LA tech pool skews creative/frontend; engineering-first senior talent is scarce.
- Every new workflow takes three internal sprints to build
The outcome you get
- AI pipelines integrated with your existing APIs and databases
- 15 to 25 hours per week returned to the team
- Evals, cost monitoring, and observability from day one
- Regular collaboration and weekly review
Los Angeles workflows I see most often
Document review, finance reconciliation, customer ops triage, KYC packet prep, RFP or proposal drafting, and sales-ops enrichment. Most share the same shape: a human reads a document, extracts a few fields, decides, and writes a summary. LLM-plus-rules workflows handle 70 to 90 percent of that today. The remaining work is exception handling, which the humans still do but on a much smaller volume. LA tech pool skews creative/frontend; engineering-first senior talent is scarce. I write the pipeline, the evals, the cost monitor, and the handoff doc. Your team keeps shipping while I build.
AI pipelines integrated with your existing APIs and databases
Why Zapier stops working around the tenth workflow
No-code tools are great for the first few automations. Around the tenth you hit three walls: cost, observability, and edge cases. Cost because per-task pricing at scale beats a custom pipeline. Observability because when a Zap fails at 2am you have no idea why. Edge cases because real business logic needs branching that Zapier handles with duct tape. The move is not to scrap Zapier entirely; it is to graduate the critical workflows to a proper pipeline and leave the light stuff where it is. Full Pacific-Time overlap with LA hours, same coast.
3s → 300ms: API response time.
How we collaborate and measure ROI
$3,999/mo retainer, one complex pipeline or two simpler ones per month, 14-day money-back guarantee, cancel any month. US-LLC to US-entity B2B contract. NDA standard. Work made for hire on payment. Before we start I ask you to log baseline hours on the target workflow; monthly we compare current hours plus vendor cost to that baseline. Most customers see payback in two to four months. Pipelines ship with confidence thresholds, a human-in-the-loop step for low-confidence cases, and documented handoff so your team can extend the system.
Recent proof
A comparable engagement, delivered and documented.
Rescued a slow API that was blocking user growth
Cuez is a live broadcast production tool used by TV teams on air across Europe. I inherited a backend API averaging 3 seconds per response and cut it to 300ms, while reducing infrastructure costs by 40% and leaving the system stable under real production load.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Private-model deployments available (Azure OpenAI, AWS Bedrock, self-hosted Llama or Mistral). Audit logs, access controls, and data residency configured per engagement. I have shipped regulated workflows at bolttech (a $1B+ unicorn).
Yes. Your IT handles infra and access; I handle the pipeline, evals, and handoff docs. Weekly sync.
Baseline hours logged before engagement, monthly comparison against current hours plus vendor cost. Typical payback is two to four months.
Full Pacific-Time overlap with LA hours, same coast. English primary. Spanish and Portuguese available for LATAM-adjacent work.
US-LLC to US-entity B2B contract. NDA standard. Work made for hire on payment. $3,999/mo subscription, month-to-month with a 14-day money-back guarantee.