AI systems that work
on your real workflows.
We design, build, and operate AI agents, RAG pipelines, and Claude-powered systems that connect to your tools, handle real tasks, and stay reliable in production.
production software
systems shipped
across deployments
production deploy
Autonomous agents that operate around the clock.
We build AI agents powered by Claude that handle real business workflows. Not chatbots — autonomous systems that classify, decide, execute, and report without human intervention.
- Multi-step workflow automation with error recovery
- Human-in-the-loop escalation for edge cases
- Monitoring, cost controls, and token management
14:32:01 ▶ New ticket received #TK-4891
14:32:01 ↳ Classifying intent...
14:32:02 ■ Intent: billing_dispute conf: 0.97
14:32:02 ↳ Pulling account data...
14:32:03 ✓ CRM lookup customer: Acme Corp
14:32:03 ✓ Invoice #INV-2847 found $4,200
14:32:04 ↳ Generating response with context...
14:32:05 ✓ Response drafted 312 tokens
14:32:05 ✓ Email sent to customer
14:32:06 ✓ Ticket updated: resolved
14:32:07 ── Completed in 5.8s · Cost: $0.004 · Awaiting next task
Your data, made AI-accessible.
We build retrieval-augmented generation pipelines that give your AI real knowledge from your data. Accurate, cited answers grounded in your documentation — not hallucinations.
- Vector database architecture & semantic search
- Citation tracking & source attribution
- Accuracy evaluation & continuous improvement
Deep Anthropic expertise.
Production-grade.
We don't just call APIs. We architect systems where Claude is a first-class component — with tool use, structured outputs, streaming, and multi-turn orchestration tuned for reliability and cost.
- Tool use, function calling & structured outputs
- Prompt engineering, evaluation & optimization
- Cost management & token budgeting at scale
// Claude with tool use — production config const response = await client.messages.create({ model: "claude-opus-4-6", max_tokens: 1024, tools: [{ name: "query_database", description: "Query the product database", input_schema: { /* ... */ } }], messages: [{ role: "user", content: prompt }] }); // Handle tool calls automatically if (response.stop_reason === "tool_use") { const result = await executeTool(response); // → query_database({product: "PRD-2847"}) // ✓ Response: 1.2s · Cost: $0.003 }
Useful AI systems usually live inside private processes.
We do not publish client data, screenshots, or names without permission. Instead, we show the production patterns we can deploy with synthetic data and privacy-safe examples.
Triage, answer, escalate.
Reads incoming tickets, classifies intent, searches docs and CRM, drafts a response, and escalates edge cases with full context.
- ✓Ticket classification and routing
- ✓Knowledge-base retrieval with citations
- ✓Human approval for sensitive replies
Answers grounded in your data.
Turns documentation, PDFs, tickets, Notion, Drive, or internal databases into cited answers your team can trust.
- ✓Ingestion, chunking, and metadata strategy
- ✓Semantic search, reranking, and source links
- ✓Answer evaluation and hallucination checks
Backoffice work between tools.
Validates data, reads emails or forms, updates systems, generates reports, and leaves an audit trail for every action.
- ✓API and database tool use
- ✓Exception handling and retries
- ✓Logs, cost controls, and fallbacks
Start with the workflow, not the model.
If the work is mostly reading and answering, build RAG. If it requires decisions and actions, build an agent. If it spans a role or department, design a digital worker with observability from day one.
RAG system
AI agent
Digital worker
Scale your team.
Without hiring.
Your team does the strategic work. AI workers handle the rest — 24/7, at a fraction of the cost. No recruiting, no onboarding, no turnover. Deploy a new digital worker in days, not months.
Always on
Digital workers run 24/7/365. No breaks, no holidays, no sick days.
Fraction of the cost
A digital worker costs 10-50x less than a full-time hire. Same output, predictable pricing.
Deploy in days
Hiring takes 3 months. A digital worker is live in 1-2 weeks. Scale from 1 to 50 overnight.
Trust comes from the way the system is built.
When public case studies are not possible, the process has to do more of the selling: architecture, evaluation, guardrails, monitoring, and a clear path from prototype to production.
See the full process →Workflow discovery
We map the real process, decision points, exceptions, tools, data sources, and where humans must stay in control.
Architecture blueprint
You get the system design before the build: integrations, data flow, security boundaries, failure modes, and costs.
Prototype with synthetic data
We validate behavior without exposing sensitive client data, then swap in real integrations when the workflow is proven.
Evaluation and guardrails
We test outputs, track accuracy, set confidence thresholds, and define when the system should ask for human review.
Observability from day one
Every action is logged with traces, costs, retries, errors, and enough context to debug production behavior.
Operate and improve
After launch, we monitor real usage, improve prompts and tools, and keep the system aligned with changing business rules.
AI readiness checklist for production
Before investing in AI, validate whether your workflow is ready for production. Includes data, architecture, cost, security, evaluation, and success criteria.
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Proof before promises.
Before selling AI systems, we spent years shipping and maintaining software used by developers. Public open source is the part of our work we can show without exposing client data.
on Packagist
across packages
on AI content
Ollama Laravel is exactly what I needed to integrate LLMs into my app. Clean API, well maintained, and the documentation is excellent.
Why clients
choose Cloudstudio.
Production-first engineering
Every system we build is architected for production from day one. Error handling, monitoring, cost controls, and graceful degradation — not bolted on later.
Full-stack ownership
Database, API, frontend, AI layer. End-to-end accountability. One team, zero handoff risk. No gaps between your AI and the systems it integrates with.
Claude-native expertise
Deep mastery of Anthropic's ecosystem. Claude Code, tool use, structured outputs, the Agent SDK — real orchestration, not wrapper APIs.
Structured methodology
Discovery, architecture, build, deploy, operate. Every engagement follows a proven methodology with documentation and knowledge transfer.
Let's map
your workflow.
Book a 30-minute technical discovery. We'll assess feasibility, identify the safest first workflow, and send you an architecture outline with timeline.
Book a call
30-minute technical discovery
Technical proposal
Architecture + timeline in 5 days
We start building
First deploy in under 4 weeks