AI Consulting · GDL → LATAM
ocampor is an AI consulting and implementation firm based in México. We help mid-market and enterprise teams move from pilots to production — strategy, build, and managed operations, powered by Claude.
Services
Every engagement ends with something running in production — not a slide deck.
We map where AI creates measurable value in your operation, prioritize use cases by ROI and risk, and leave you with a roadmap your team can execute — with clear costs and owners.
We design and build AI systems on Claude: agents, document processing, customer-facing assistants, and internal copilots — integrated with your existing tools and data, in Spanish and English.
We operate what we build. Monitoring, evaluation, cost control, and continuous improvement — so your AI systems keep getting better after launch, not worse.
How we work
A fixed, transparent path. You always know what happens next and what it costs.
01 — Diagnose
Two weeks inside your operation. We identify the highest-value use cases and the fastest path to them.
02 — Prove
A real system on your real data, measured against a baseline you agree to before we start.
03 — Ship
Security review, integration, training, and launch — with your team involved at every step.
04 — Operate
Ongoing monitoring and iteration under a managed service agreement. Performance reported monthly.
Work
Selected projects. Client names available under NDA.
SaaS · Content optimization
Multi-model LLM infrastructure (Claude, GPT-5, DeepSeek) with evaluation and fallback, plus agentic developer tooling on Claude Code. ~20% compute savings, dev costs halved, new internal apps shipped in ~1 week.
Financial services · LATAM digital bank
Migrated legacy ML autoreply to an LLM-based RAG system with per-business-unit agents. Self-service up 40%, total customer contacts down 28%, inference cost cut 50%.
Fintech · Global neobank
Sanctions screening rebuilt with search + ML re-ranking, from prototype to production. Precision 0.03 → 0.65 at required recall; query time from 30 seconds to under 2.
Blog
What we are writing about applied AI right now.
What agentic coding is, the agentic coding tools I run daily on Claude Code, the end-to-end workflow, and a scorecard against the 2026 literature.
Six Claude Code subagents with a model each, one orchestrator that writes no code, a separate reviewer, and why I use skills instead of agent teams.
How I use Claude Code hooks to block an agent from ending its turn on a red check: exit code 2, a tree fingerprint, per-repo check.sh, and a trace log.
Context engineering for Claude Code: a short CLAUDE.md pointing at on-demand docs, briefs as the spec, skills as slash-only pipelines, and what I omit.
Production case study: Claude driving a 22-tool MCP server on FastAPI/Postgres, durable agent memory, dual-layer guardrails and a self-grading eval loop.
Why Claude
We build on Claude because our clients need AI that is accurate, secure, and dependable in production — especially in regulated industries like finance and healthcare.
Our team is trained on the Claude platform: the API, agent tooling, and enterprise deployment. That means faster builds and fewer surprises.
Contact
A 30-minute call. No pitch — we'll tell you honestly whether AI helps your case, and how.