Claude Code
Agentic coding workflows for architecture, implementation and review.
Production guardrails
// Senior-led · Remote / worldwide
AI Development Services for production-grade autonomous AI agents and software that scales.
DYNO SOFT engineers production-grade autonomous AI agents, multi-agent orchestration and RAG infrastructure alongside secure MCP servers and rigorous evaluation pipelines (evals). We also deliver scalable monoliths, microservices and full-stack web applications—combining strict guardrails, semantic caching, observability and cost control from architecture through production.
ENGINEERING_MODE: AI_NATIVE
Clear architecture. Useful automation. Software that earns its complexity.
A visual signal for thoughtful, resilient engineering.
DYNO SOFT is a software engineering and consulting studio that designs and builds AI agent systems, RAG platforms, web applications (React / Next.js, Vue / Nuxt) and scalable cloud-native backends (Kubernetes, serverless, edge functions). We work remotely with startups and product companies worldwide.
// Capabilities
The right engineering surface for the problem
01
AI Development
Production AI systems with tools, evals, guardrails, retrieval and cost control.
02
Web Applications
React, Next.js, Vue and Nuxt applications built for users, search and growth.
03
Scalable Backend
Event-driven backends, APIs and infrastructure with observability built in.
04
API Integrations
REST, GraphQL, gRPC, webhooks, sync pipelines and secure MCP servers.
05
Web3
Wallets, on-chain data pipelines and interfaces for decentralized products.
// Principles
How we make trade-offs
Architecture-driven
Security-first
Production-ready
AI-native workflow
// Stack
Tools chosen for the outcome
Frontend
ReactNext.jsVueNuxtTypeScriptTailwind
Backend
NestJSFastifyLaravelSymfonySyliusFastAPI
Cloud-native
AWSGCPKubernetesDockerServerlessEdge functionsTerraform
Data & messaging
PostgreSQLRedisRabbitMQMongoDBPrisma
AI
Claude / OpenAIOpen-weight modelsMCPRAGVector databasesEvals
Observability & quality
OpenTelemetryGrafanaPrometheusPlaywrightPlaywright testsJestk6
// FAQ
Straight answers
When should a company build a production-grade autonomous AI agent?
Build an agent when a workflow needs flexible tool selection, context retrieval or bounded decisions. DYNO SOFT first compares a deterministic workflow, then adds multi-agent orchestration only when it improves the measurable outcome.
How do you build reliable RAG infrastructures and AI knowledge assistants?
We implement ingestion, chunking, hybrid retrieval, citation-aware generation and evaluation pipelines (evals), with strict guardrails and traceable failure handling. Representative questions become regression tests before launch.
How do you reduce AI latency and repetitive API overhead?
We use Redis for semantic caching to reduce repetitive API overhead by up to 40% when request similarity and freshness rules permit. Dynamic model routing can fall back from Claude 3.5 Sonnet to open-weight alternative models to keep real-time budgets under 200ms per token.
Can you securely connect agents to existing systems?
Yes. We expose narrowly scoped capabilities through secure MCP servers, typed APIs and verified webhooks, with authorization, validation, audit logs and human approval for sensitive actions.
Can you take a product from idea to production?
Yes. Engagements can cover discovery, architecture, implementation, deployment and observability as one continuous track, including Next.js applications and cloud-native backends.
// Start with clarity
Have a system to build, repair or make intelligent?
Tell us what you are solving// // Agentic development
A modern engineering loop for AI-native teams
Codex
Fast, context-aware pair engineering for shipping production changes.
Production guardrails
DeepSeek Harness
Open-model workflows with tests, guardrails and measurable quality gates.
Production guardrails