Q3 2026 · 2 slots left

We build AI-native
systems that think,
act, and ship.

We build agentic workflows, LLM-powered products, and cloud-scale AI platforms — and advise the teams building their own. Everything we ship is AI-native from day one, whether we're delivering it or sitting next to your engineers.

Multi-agent systems · RAG pipelines · LLM orchestration · AI-native apps · Agentic workflows

Shipped
5+
AI systems in prod
Clouds
3
AWS · GCP · Vercel
Junior devs
0
senior only
live orchestration6 agents active
PLANNERCODERREVIEWERDEPLOYERMEMORYTOOLSORCHESTRATOR
msgs/s1,284 ▲ 12.4%
Stack we use
ANTHROPICOPENAILANGGRAPHAWSGCPVERCELSUPABASEPOSTGRESTYPESCRIPTPYTHONANTHROPICOPENAILANGGRAPHAWSGCPVERCELSUPABASEPOSTGRESTYPESCRIPTPYTHON
The problem

Most AI projects never
leave the notebook.

Impressive demos, hallucinating agents, cloud bills that surprise the CFO. The gap between a GPT wrapper and a production-grade AI-native system is where most teams stall — and where we begin.

AI-native, not AI-wrapped

We don't bolt LLMs onto legacy code. Every system is designed agentic-first — reasoning, planning, and acting as a first-class architectural concern.

LLM ops from day one

Prompt versioning, token budgets, eval suites, hallucination guards, and cost dashboards baked in — not bolted on after the first incident.

Shipped, not prototyped

Every agentic system we deliver runs under real traffic with rollback, runbooks, and evals. Clean code, written docs — no lock-in to us.

The outcome

Agentic AI in production. Fast.

4–16

Week engagements

From first prompt to production-grade agentic system. Not quarters — weeks.

5+

Systems shipped

LLM-powered products, agentic pipelines, AI-native platforms — shipped.

0

Junior handoffs

Senior engineers ship the work. You always talk to the people building it.

live orchestrationbuild in progress
PLANNERCODERREVIEWERDEPLOYERMEMORYTOOLSORCHESTRATOR
PLANNER spec the nav
CODER nav.tsx ✓
PLANNER spec the catalog
CODER filters wired
CODER grid rendered
REVIEWER low contrast on filter
CODER fix shipped
DEPLOYER deployed ✓
tokens/s18,420 stable
https://yourproduct.app
buildinglive
Shipped
prompt → production~6s demo · 2–4 wks real
Who builds it

Our team are AI-native developers — fluent in agentic workflows, model orchestration, and the full toolchain.

That's why we ship in weeks, not quarters.

How a real RAG pipeline runs

Chat with a PDF.
Watch the pipeline.

Five scenes, real round-trips. From the moment a user sends a message until the citations land in their chat: rate limit, query expansion, hybrid retrieval, generation, attribution, claim grounding, atomic persist. The participants on stage change with each phase — this is what production agentic AI actually looks like.

live RAG pipeline

section-4.pdf · 24 pages

01 · Inbound02 · Expand + Embed03 · Retrieve + Rerank04 · Generate + Verify05 · Persist + Audit
APPAPIREDISDBAPILLMEMBEDDERAPIDBRERANKERAPILLMAPPAPILLMDBWORKERS
APPAPI: POST /messages · section 4?
APIREDIS: check rate limit
REDISAPI: 992 tokens left
APIDB: INSERT user_message
APILLM: expand query
LLMAPI: 4 paraphrases
APIEMBEDDER: embed × 4
EMBEDDERAPI: 1536-dim × 4
APIDB: ANN (pgvector) × 4
APIDB: BM25 (tsvector) × 4
DBAPI: 200 hits · RRF fused
APIRERANKER: rerank top-5 · BGE
RERANKERAPI: top-5 ranked
APIDB: expand parents ±2
APILLM: chat_stream(prompt)
LLMAPI: token deltas...
APIAPP: ASSISTANT_MESSAGE_DELTA × N
APILLM: verify · piggyback
LLMAPI: attribution weights
APILLM: ground 3 claims
LLMAPI: 3 claims · 100% grounded
APIDB: INSERT + audit_log · COMMIT
APIWORKERS: dispatch LOO refinement
chat
preparingretrievinggeneratingverifying
what does section 4 say?
5 chunks found · recall@5 0.92
Financial institutions must maintain transaction records for at least 5 years [1] and provide them to regulators upon written request within 30 days [2].
attribution[1] 60%[2] 40%
3 atomic claims · 100% grounded
saliency: high · high
|
complete
  • retrieval_ms342
  • llm_ms1,840
  • total_ms2,212
  • assurancehigh
How we compare

Code Origin vs. the
usual suspects.

Big agencies move slow. Freelancers vanish. In-house hires take a quarter to ramp. Here's what changes when a small senior team owns the problem end-to-end.

★ Best choice

Code Origin

Competition

  • Multi-agent expertise

  • Time to first prod ship

    2 – 4 wks
    2 – 3 mos
  • Cloud architecture

    DEP
  • Senior engineers only

  • Clean handover

  • Ongoing partnership

Convinced we're the right fit?

Book a 30-min intro
What we build & advise on

Full-stack AI.
Build or advise.

Agentic systems need more than a good model. They need a solid app underneath, the cloud to run it reliably, and senior engineering judgement throughout — we deliver all four, end-to-end or alongside your team.

01

Agentic AI Systems

Multi-agent workflows, LLM orchestration, and RAG pipelines — designed to reason, plan, use tools, and act autonomously at scale.

  • Multi-agent orchestration & agentic loops
  • RAG, vector search, long-term memory
  • LLMOps: evals, traces, cost & safety guardrails
02

AI-Native Software

LLM-powered apps, APIs, and data pipelines built AI-first — not retrofitted. Production-grade from day one, owned by your team.

  • AI-native apps with Next.js, TypeScript, Python
  • Generative UI & streaming LLM interfaces
  • Internal AI copilots & developer platforms
03

AI Cloud Infrastructure

Scalable cloud platforms tuned for AI workloads — GPU pipelines, vector stores, inference APIs — observed and cost-controlled end-to-end.

  • AI-optimized infra on AWS, GCP, Vercel
  • Vector databases, embeddings pipelines, inference
  • LLMOps observability, SLOs, cost guardrails
04

Consulting & Advisory

Senior engineering judgement for teams building their own — software architecture, AI strategy, and code-level reviews. We sit next to your engineers when you don't need us to deliver.

  • Architecture audits & second opinions
  • AI strategy, build-vs-buy & roadmap reviews
  • Software engineering & AI development reviews
05

AI Training for Companies

Hands-on workshops and structured curricula that bring your engineers up to speed on AI coding tools — Claude Code, Cursor, GitHub Copilot, Codex, Gemini — and the production patterns around them.

  • Workshops on Claude Code, Cursor, Copilot, Codex & Gemini
  • RAG, evals & LLMOps bootcamps tailored to your stack
  • Curriculum tuned to your team's tools, level & codebase

Want this for your team?

Talk to an engineer
Products we ship

Tools that come
from the work.

We package what we learn for clients into shipping products — from a flagship training platform to open-source developer tools.

masterclaude.dev
Course12 lessons

Claude Code · Production patterns

  • Setting up Claude Code
  • Agentic workflows in practice
  • 03RAG, evals & grounding
  • 04Multi-agent orchestration
2 of 12 complete17%
Featured product

masterclaude.dev

Claude Code specialization courses

Hands-on, structured curricula for individuals and teams who want to ship faster with Claude Code. Built and maintained by the founder of Code Origin — always current with Anthropic's latest releases.

  • Structured tracks from beginner to advanced
  • Hands-on labs on real codebases, not toy demos
  • Updated continuously as Claude Code ships new features
Visit masterclaude.dev
~/your-project ❯ claude
$claude plugin marketplace add ./mighty-powers
Registered marketplace: mighty-powers
$claude plugin install mighty-powers
Installed 45 skills
Registered 6 specialist agents
Loaded 19 tools
Ready. Try /plan, /ship, /audit ...
Slash commands
/plan/design/ship/audit/secure/rescue
Featured product

Mighty Powers

Open-source Claude Code plugin · Full-lifecycle workflow

A unified Claude Code plugin that orchestrates the entire software lifecycle — from planning and design through implementation, testing, security audits and deployment. Built and battle-tested on our own client work.

  • 45 skills + 6 specialist agents + 19 Node tools, all wired together
  • Wave-based execution plans with parallel tasks and checkpointed resume
  • Safety guardrails block force pushes, destructive git ops and the usual footguns
View on GitHub
Who builds it

Senior engineers.
No handoffs.

Every project is staffed by named people you can talk to from day one. No bench shuffle, no junior offshore swap mid-sprint.

Anderson da Silva

Founder · Principal Engineer

Creator of masterclaude.dev. Builds AI-native systems for production and trains engineering teams to ship with Claude Code.

Who we work with

Built for engineers.
Accessible to operators.

For technical teams

Agentic AI without the regret.

  • Eval suites from day one — no vibe-checking LLM outputs in Slack
  • LLM tracing, token budgets & cost dashboards your CFO can read
  • Hallucination guards, retries, rate limits, fallbacks — out of the box
For founders & operators

AI strategy that becomes AI product.

  • Fixed-scope sprints. Weekly demos of working AI. No surprises.
  • One Slack, one PM, one tech lead — all senior, all AI-native
  • Outcomes tied to business metrics, not model benchmarks
Common questions

What we get
asked first

How much does an engagement cost?

Most engagements land between $30k and $250k depending on scope. Discovery is typically $8–15k for a one-week diagnostic with a written architecture and budget. We share a fixed fee before any build starts — no T&M surprises.

What does "AI-native" actually mean?

Agentic reasoning is a first-class architectural concern, not an LLM bolted onto a CRUD app. Prompt versioning, eval suites, traces, cost guardrails and hallucination grounding live alongside your application code from day one.

Who owns the IP and the code?

You do. Code, prompts, evals, infra-as-code, runbooks, and recorded walkthroughs are delivered to your repos under a standard work-for-hire clause. No lock-in to us, no third-party platform you cannot leave.

Which models, clouds, and stacks do you work with?

Anthropic Claude, OpenAI, and open-weights (Llama, Qwen, Mistral) on AWS, GCP, or Vercel. Vector stores: pgvector, Pinecone, Qdrant. Application stack: Next.js / TypeScript / Python. We pick what fits your team, not what we already know.

Do you sign NDAs and DPAs? What about EU data residency?

Yes to both. We sign standard mutual NDAs before discovery and DPAs before contract. EU data residency on AWS Frankfurt / GCP eu-west is supported on every engagement; SOC 2 Type II workflows on request.

What does post-launch support look like?

Optional retainer ($8–20k/month) covers LLMOps, model upgrades, eval maintenance, cost tuning, and on-call for the first 90 days post-launch. Or you take it over completely — your team has the runbooks and we are one Slack message away.

Pick a time

Book a 30-min intro call

We will not pitch you. We will ask sharp questions, sanity-check your architecture, and tell you whether we are a fit. If we are not, we will point you to who is.

Prefer to send notes instead? Scroll to the contact form

Start a project

Ready to build your
AI-native product?

Tell us a little about what you're building. We reply within one business day — typically with a few sharp questions and a proposed next step.

Response time
Within 1 business day
Engagement length
4 – 16 week sprints
What do you need?

We reply within 1 business day.

Free download

The Production-Grade RAG Checklist

A senior engineer's launch gate for RAG features — chunking, hybrid retrieval, grounding, evals, observability, cost control, and the boring compliance bits everyone skips.

  • Instant PDF download — no inbox waiting.
  • Built from real production shipments.
  • Use it as your launch checklist.

One email. We never share it.

Q3 2026 · 2 slots leftBook a call