We’ve shipped AI features that move real numbers — multilingual PDF parsers built on GPT-4V + Claude that automate hours of manual review per day, RAG pipelines for internal knowledge bases that actually get used, structured-output systems that produce JSON your engineers can trust, and customer-support bots that answer the boring 80% so humans can focus on the rest. On the automation side we’ve built Puppeteer / Playwright fleets running on cloud VPS that scrape SERP and Google Maps data at scale, account-automation workflows that survive UI churn, and LinkedIn-class outreach pipelines (BigLinker). We treat all of it like any other production system: cost-bounded, observable, evaluable, and recoverable when the model — or the target site — regresses.
Stack & defaults
Models
OpenAI / Anthropic / Bedrock
Self-hosted
Open-source (Llama, Mistral)
Orchestration
Vercel AI SDK / LangChain
Retrieval
pgvector / Turbopuffer
Browser automation
Puppeteer / Playwright
Scraping at scale
Proxy rotation + queues
Workflows
Inngest / Trigger.dev
Observability + testing
Langfuse + Evals
What you receive
Use-case validation
Before code: a written assessment of whether AI is the right answer here. Often it's not — we'll say so.
Production-ready LLM features
Streaming, retries, fallbacks, cost ceilings, abuse protection, structured JSON output. Not a prompt in a chat box.
PDF parsing, OCR, multi-language extraction, structured-output validation. Hours of manual review collapsed into a queue.
Scraping pipelines at scale
SERP, Google Maps, e-commerce data, social platforms. Puppeteer / Playwright with proxy rotation, headless browser fleet, queue-backed retries — running on Cloudflare Workers or cloud VPS.
LLM observability + evals
Per-call cost, latency, token use, golden-set test scores. Dashboards your CFO can read; eval suites that catch regressions before users do.
Timeline
Wk 1–2
Validation
Use-case fit, eval design, success criteria.
Wk 3–6
Build
Pipeline, retrieval, prompts, evals.
Wk 7–9
Hardening
Abuse, cost ceilings, fallbacks, observability.
Wk 10
Launch
Cutover, dashboards, runbook handoff.
FAQ
Should we even use AI for this?
Maybe. We open every engagement with a use-case assessment. If a regex or a database query gets you 90% of the way there, we'll tell you.
OpenAI, Anthropic, or open-source?
All three, depending on the workload. We architect to be model-agnostic so you can swap when costs/quality shift. We've shipped systems using GPT-4V and Claude on the same pipeline.
How do you handle hallucinations?
Structured outputs (JSON Schema validation), grounded retrieval, eval harness, and never letting a raw LLM response into a downstream system. Hallucinations don't disappear, but they become observable.
What about agents?
Sparingly. Most 'agent' problems are better solved as a directed workflow with a tight LLM step. We'll build true agentic loops only when the use case demands it.
