// Services / ai-development
DYNO SOFT provides AI Development Services for production-grade autonomous AI agents, multi-agent orchestration and RAG infrastructures with secure MCP servers, evaluation pipelines (evals), strict guardrails, semantic caching and measurable quality, latency and cost boundaries.
// System shape
// What we do
01
We design tool use, orchestration and human handoffs around a clear job to be done—not a demo prompt.
02
We expose products and internal systems to agents through secure MCP servers, with explicit permissions, validation and auditability.
03
We build ingestion pipelines, hybrid retrieval and citation-aware answer flows, then evaluate retrieval quality with representative questions.
04
Structured outputs, function calling, streaming, caching, model routing and batching keep features useful and costs legible.
05
We set up a PRD → ADR → implementation workflow for AI-assisted delivery, with architecture review, progress tracking and durable decisions.
06
We map the agent attack surface, mitigate prompt injection and instrument model pipelines with cost, latency and quality budgets.
// Decision guide
| Approach | Use when | Watch for |
|---|---|---|
| Workflow | Steps are known and repeatable | Brittleness as exceptions grow |
| Single agent | One model can choose among bounded tools | Tool permissions and eval coverage |
| Multi-agent | Distinct roles need parallel context | Coordination cost and emergent failures |
// FAQ
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.
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.
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.
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.
Yes. Engagements can cover discovery, architecture, implementation, deployment and observability as one continuous track, including Next.js applications and cloud-native backends.
Yes. We can establish an AI-assisted delivery workflow with architecture records, guardrails and review practices that fit your existing team.
// Make the next decision easier
