AI that replaces real labor hours, not chatbot demos.
Claude and GPT in your workflow, wired to your Postgres, your CRM, your queue. Production automation, not a Loom demo.
Claude reads inbound MSP tickets, classifies, drafts the dispatch SMS. 80% of tier-1 triage automated. 4 FTEs redirected to higher-tier work.
AI automation at Wolrix means putting Claude or GPT inside the workflows your team currently does manually — ticket triage, document extraction, internal reporting, drafted replies. We ship the integration with the model, the prompt cache for cost ceiling, the human-in-loop where the action is risky, and the observability so you see exactly what the model decided. Most automations ship in 2–4 weeks at $10K–$25K.
Quick answers
Which AI model do you use?
Default Claude Sonnet 4.6 with prompt caching for cost ceiling. Switch to GPT-5/4o where its function-calling, vision, or speed wins. We're provider-agnostic from day 1 — your stack stays portable.
What's a typical AI automation cost?
Most automations ship in the Build tier — $10K-$25K, 2-4 weeks. Bigger workflows that need RAG, fine-tuning, or multi-agent orchestration land in Scale at $25K-$50K. Hourly advisory at $100-$200/hour.
What about hallucinations?
Three layers: RAG against your data instead of model knowledge, function-calling for structured output, and human-in-loop on any path that takes an irreversible action. We don't ship AI on actions you can't undo.
Can you replace my whole team with AI?
No. We replace specific repetitive tasks (triage, classification, drafting) — usually 60-80% of tier-1 work. Humans approve the edge cases. Most clients redirect their team to higher-leverage work, not headcount cuts.
What we automate
The repetitive work your team does every day — named with the actual model, the actual storage, the actual job runner. So you can tell whether we know what we are doing.
Claude reads inbound, drafts the response
Tickets, emails, intake forms hit the queue. Claude classifies, extracts the structured fields, and drafts the dispatch SMS or reply. Human approves with one click. The 80% of tier-1 work that did not need a person stops needing one.
PDFs and forms in, Postgres out
Invoices, contracts, intake forms, signed PDFs. Claude with structured output (JSON schema) returns clean rows. Validated, written to Postgres, pushed to the CRM/ERP via API. Edge cases get flagged for human review, not silently dropped.
Reports that write themselves
Cron job pulls data from Postgres, Stripe, your CRM, and whatever else matters. Claude composes the narrative, charts render server-side, Resend delivers a PDF or HTML email on the schedule you set. The 4-hour Monday report becomes a 4-second cron.
Vision for QC and inspections
Photos uploaded from the field hit Claude vision or GPT-4o. Defects flagged against a checklist, compliance status returned, report generated. The QC team reviews flagged items, not every shot.
Cross-system sync without Zapier debt
When the workflow outgrows Zapier — rate limits, branching logic, secrets sprawl — we replace it with Inngest or Trigger.dev jobs in your codebase. Versioned, retried, observable, owned by you.
Real ROI, anonymized
Tier-1 tech reads every ticket, classifies it, drafts dispatch SMS, assigns. ~6 minutes per ticket, 200+ tickets/day, 4 FTEs on the queue.
Claude classifies and drafts. Human reviews and clicks send. 80% of tier-1 triage automated.
4 FTEs redirected to higher-tier work
5-person QC team manually reviewing 200+ site photos per visit, generating reports in Word.
GPT-4o vision flags issues against checklist. QC lead reviews flagged-only.
4 FTE reduction, 80% faster report turnaround
Staff copies SKUs and prices across POS, e-commerce, CRM, and supplier portals. 2 hrs/day per location.
Postgres source-of-truth, Inngest jobs push to every channel on change.
2 hrs/day per location, zero copy-paste errors
How it runs
Find the hours
Where is your team's time going? We pick the workflow with the highest hours-saved-per-dollar to automate first.
Pick the model
Claude, GPT, or no LLM at all. We test on your real data before committing to an architecture or a price.
Build and verify
One technical lead. The automation runs against historical data first so you can score the output before anything goes live.
Ship with a fallback
Sentry on errors, queue retries, human-in-the-loop for low-confidence outputs. Nothing fails silently.
The stack
Right tool for the job. Not every problem needs an LLM — sometimes a Postgres view and an Inngest job outperform a model at a tenth the cost.
Pricing
Single workflow — document extraction, report generator, ticket triage. Fixed price, production-ready.
Multi-step pipeline with dashboards, monitoring, human-in-the-loop review, and cross-system integrations.
Automation audit, ROI math, architecture review. Figure out what to automate before you pay anyone to build it.
Send the workflow. Get the quote.
Tell us what manual work is eating hours. We will tell you if AI can fix it, what model fits, and what it costs. NDA before any details.