AI Automation Freelancer
Most "AI automation" deliverables in 2026 are Zapier boards held together with prayers. I build the version that survives — Python workers on real infrastructure, with retries, schema validation, cost caps, and a runbook so you can hand it off. I am Ignacio (IGNAX), a Spain-born, Paraguay-based, solo full-stack developer + SEO/AEO specialist. AI automation is the second-most-common reason founders and agencies hire me, after SaaS MVPs.
Who is this service for?
- Founders drowning in repetitive back-office work — inbound lead triage, doc parsing, content production, support tagging.
- Sales and ops teams who hit Zapier's pricing ceiling or its branching ceiling and need real code.
- SaaS companies who want an AI layer inside their product — not a chatbot widget, a workflow that earns revenue.
- Agencies that need a senior AI/automation subcontractor for a client engagement, white-label.
If you are still figuring out which manual task is worth automating, the discovery call sorts that — I will not let you pay me to automate something that should be deleted instead.
What is included in an AI automation build?
- Discovery + one-page workflow spec, signed off before any code is written.
- Python workers (FastAPI or pure async) with retry, timeout, idempotency, and structured logging.
- LLM layer: OpenAI, Anthropic, or both, with cost-aware model routing.
- Schema-validated outputs (Pydantic or Zod) so silent failures cannot poison downstream data.
- Storage: Postgres on Supabase or Neon, plus Cloudflare R2 or S3 for files.
- Integrations: REST/webhook/GraphQL into whatever you already use — HubSpot, Notion, Slack, Stripe, Airtable, etc.
- Cost dashboard (per-task LLM spend) shared from week one.
- Production deploy on your own cloud account. You own the code, the data, the logs.
- 14-day post-launch fix window for non-spec bugs.
How does the build process work?
The flow mirrors my SaaS MVP build, just compressed.
- Discovery call (free, 30 minutes). Bring an example of the manual task — a recorded screen-share, a sample inbox thread, a Notion doc. I map it to a workflow before we hang up.
- One-page spec + fixed quote inside 48 hours.
- Build. Daily commits to your repo, Friday Loom video showing the worker running end-to-end on real data.
- Pilot run. I run the automation on a sample batch (last 30 days of your data) before flipping it on full-time. We catch edge cases before they hit production.
- Handover. 60-minute call covering deploys, secrets, cost dashboard, and how to read the logs. Written runbook in the repo.
Working hours: Mon–Fri, 8:00–17:00 PYT (GMT-3). Weekends off. Overlap with US East 7:00–16:00 EST, with Madrid 13:00–22:00 CET.
What stack do you ship with and why?
- Python 3.12+ — the language with the deepest AI/ML ecosystem. FastAPI for HTTP, asyncio for concurrency, Pydantic for I/O contracts.
- OpenAI and Anthropic APIs — managed models beat self-hosted for almost every automation. Cheaper to ship, swap-able later. See the Anthropic API documentation and OpenAI API reference for the canonical references.
- Postgres on Supabase or Neon — the default sink. pgvector handles embeddings without a second database.
- Cloudflare Workers + R2 — global edge for webhook receivers, generous free tier, no cold starts.
- Inngest, Trigger.dev, or self-hosted RQ/Celery — depending on whether you want managed orchestration or full control.
- GitHub Actions for scheduled jobs when a full queue is overkill.
If you are committed to TypeScript end-to-end, I can match it — Node 22 with Vercel AI SDK is a clean Python alternative for lighter automations.
How much does AI automation cost?
Pricing band: $2,000–$10,000 USD (Gs 12.3M–61.4M). The lower half is a single-workflow automation with one integration and one LLM call per item — say, lead enrichment that runs nightly. The upper half is multi-step pipelines with branching, multiple integrations, batch processing, or human-in-the-loop review UIs. Hourly outside that band: $30–$70 USD/hr. PYG values approximate, recalculated quarterly. Rate as of 2026-05-27: Gs 6,136.82 / USD. For deeper background on stack tradeoffs see best stack for AI SaaS 2026.
For agencies — white-label AI automation
White-label AI is one of the cleanest subcontracts available right now. Most agencies have not hired a dedicated AI engineer yet, and clients are starting to ask. Day-rate option: $240–$560 USD/day (derived from the hourly band). I sign your NDA, deliver under your brand, and stay invisible to your client. EN/ES bilingual coverage included at no extra cost — useful when you have LATAM or Spain accounts but an English-only team.
What does the launch look like?
I deploy into your own AWS, Cloudflare, Fly.io, or Render account. You own the GitHub repo from commit one. After launch I run a 60-minute handover covering deploys, secrets management, the cost dashboard, log aggregation, and the retry/replay flow. A written runbook lives in the repo. Two weeks of free in-scope fixes follow.
What about ongoing maintenance?
Most automations should run silently for months. When they do not, I offer a 5–10 hour/month monitoring bucket at 15% off hourly, 30-day rolling cancel, no lock-in. For background on how I sequence AI builds see build a SaaS MVP fast and what is AEO? — the same shipping discipline applies to AI automations.
Related case studies and articles
- AI Lead Generation Platform — production AI automation inside a SaaS, OpenAI + Stripe + Postgres.
- Auto Video Pipeline — fully automated news-to-video pipeline with TTS, AI imagery, and FFmpeg.
- Build a SaaS MVP fast — the sequencing pattern I reuse on automation builds.
- Best stack for AI SaaS in 2026 — model routing, cost controls, infra choices.
Ready to talk? Email hello@ignax.dev with a one-paragraph description of the manual task you want gone. I reply within one business day.
Working hours: Mon–Fri, 8:00–17:00 PYT (GMT-3). Weekends off. Overlap with US East 7:00–16:00 EST, with Madrid 13:00–22:00 CET.
Pricing
PYG values approximate, recalculated quarterly. Rate as of 2026-05-27: Gs 6,136.82 / USD.
For agencies
Available as a white-label senior subcontractor. Standard NDAs welcome. Day-rate option: $240–$560 USD/day depending on stack.
Email hello@ignax.devFrequently asked questions
What kinds of automations do you actually build?
Inbound lead triage and enrichment, document parsing and structured-data extraction, support-ticket tagging and routing, internal report drafting, news/social monitoring, image and video pipelines, and batch content production. The common thread is that the LLM is the glue, not the entire product. The output usually lands in Slack, a Postgres table, a Notion database, or a Google Sheet — wherever your team already lives.
Why not just use Zapier or Make?
Use Zapier and Make for the first ten runs of any idea — they are great for validating the workflow. Beyond that they get expensive per-step, hide failures behind a UI, and break the moment you need real branching, retries, or LLM tool-use. A Python worker on a $5/month box runs the same workflow for a fraction of the cost, logs every step, and can be debugged like real code.
How do you keep LLM costs predictable?
Three controls. First, route easy cases to a cheap model (Haiku, GPT-4.1 mini) and only escalate to Sonnet or GPT-4.1 when the cheap model rejects the task. Second, cache prompts and embeddings — repeated context should never be re-tokenized. Third, set hard monthly spend caps in the OpenAI and Anthropic dashboards. I share a cost dashboard with every client from week one.
Will the automation keep working when the LLM API changes?
Yes — that's part of the design. Model names are environment variables, not hard-coded. Prompts are versioned in git. Schema-validated outputs (Pydantic or Zod) catch silent regressions before they hit your data. When OpenAI deprecates a model, you change one env var and redeploy. I document this in the runbook.
Do you handle the data side, or do I need a data engineer too?
I handle ingest, transformation, and storage end-to-end for typical SMB scale (under 100 million rows). Postgres is the default sink; Cloudflare R2 or S3 for files. For real warehouse-scale work — billion-row joins, real-time CDC, complex dbt models — I bring in a data engineer rather than fake it. I will tell you in the discovery call which side of that line your project sits on.
Can you work under our agency's NDA and white-label brand?
Yes. White-label AI work is one of my most common subcontracts. Day-rate $240–$560 USD/day, NDA-friendly, I sign your paperwork and stay invisible to the client. Many agencies need AI automation capacity without hiring a full-time specialist — that's the gap I fill. EN/ES bilingual coverage included at no extra cost.
How does this connect to my existing SaaS or CRM?
Through whatever they expose — REST API, webhook, GraphQL, SDK, or, worst case, screen-scraping with rotated proxies. HubSpot, Salesforce, Pipedrive, Notion, Airtable, Slack, Discord, Stripe, and Linear are all stacks I have wired before. The only integration I refuse is one that violates the target platform's terms of service. I will flag that in the discovery call before quoting.
What happens after the build is shipped?
Two weeks of free in-scope bug fixes are included. After that, automations either run for months without touching them (the goal) or they need a 5–10 hour/month monitoring bucket. I do not push retainers — most automations should be quiet. When something does break I bill hourly against the discounted maintenance rate. No lock-in, 30-day rolling cancel.