LLM · AGENTS · EVALS
Production AI systems built for real operations
We help teams ship agentic assistants, retrieval pipelines, and workflow automation with clear contracts, offline evaluation, and production telemetry, so model behavior stays measurable after launch.
ABOUT
Engineering partner for AI led product and platform work
Dokyoai is a software studio that ships full stack applications, production LLM systems, and workflow automation. Our work is scoped to measurable outcomes, with staging paths, rollback plans, and runbooks included from day one.
Customer and internal web apps with performance, accessibility, and maintainable frontends aligned to your design system.
Agentic and RAG workloads with evaluation harnesses, tool schemas, and guardrails suited to support, GTM, and operations teams.
Workflow orchestration at scale: sub workflows, error handling, credential hygiene, and metrics that surface issues before users do.
Whether you are starting from a greenfield portal or hardening an existing stack, we deliver working software with documentation, monitoring, and a clear handoff plan.
SCOPE OF WORK
Capabilities
Agentic AI, retrieval systems, integration layers, workflow orchestration, and operator grade product surfaces, delivered as production systems, not experiments.
Agentic AI & copilots
Production assistants with tool calling, structured outputs, and human in the loop checkpoints, grounded in your tickets, docs, and CRM data with offline evals and production telemetry.
Integration fabric
Typed tool surfaces and HTTP contracts that connect LLMs and workflows to warehouses, CRMs, support stacks, and internal APIs, with governed access and least privilege.
Workflow orchestration & AI nodes
Graphs built for scale: sub workflows, explicit error and dead letter branches, queue friendly execution, streaming where UX needs it, and native AI agent nodes.
RAG & knowledge operations
Ingestion pipelines, chunking and metadata design, hybrid and vector retrieval, refresh jobs, citations, scope limits, and redaction.
Observability, evals & safety
End to end traces, prompt and graph versioning, token and latency SLOs, PII handling, and audit ready logs.
Product surfaces & internal tooling
Operator UIs on top of your automations: review queues, approvals, admin consoles, and customer portals.
OPERATING MODEL
Production bar for AI, workflows, and product
SLO minded delivery: KPI linked scope, failure mode aware automation, traceable model and graph changes, and one accountable team from UI through integration contracts.
KPI anchored scope
Engagements map to metrics you already instrument, including MTTR, lead velocity, cost to serve, and cycle time, so agent and workflow work is justified by production signals, not narrative alone.
Modern systems design
RAG with eval harnesses, typed tool interfaces, queue aware orchestration, and versioned prompts and graphs: architectures your platform team can trace, diff, and operate under real concurrency and failure modes.
Failure mode first automation
Idempotency keys, bounded retries, poison message handling, and crisp boundaries between orchestration, domain services, and persistence, so graphs degrade predictably when load and edge cases spike.
Single thread: UX and integrations
One accountable team ships operator UI and the integration layer together, with tighter feedback loops from first tool spec to a workflow your team runs daily.
ENGAGEMENT LIFECYCLE
From discovery through operated systems
Structured phases: risk framing, written contracts, staged integration, and trace driven iteration.
01
Discovery & risk framing
We align on outcomes, data classes, compliance boundaries, and the split between exploratory agents and deterministic workflow steps.
02
Architecture & contracts
We document retrieval strategy, tool schemas, workflow topology, SLOs, and how you will measure quality, cost, and reliability once traffic is real.
03
Build, integrate & stage
We ship UIs, workflow graphs, model routes, and connectors behind feature flags, synthetic and sampled test data, and rollback friendly release paths.
04
Operate, observe & iterate
Cutover includes dashboards and alerts; improvements are driven by traces, eval scores, and operator feedback, not ad hoc prompt edits in production.
REFERENCE
FAQ: production patterns
Tap a question to expand. For architecture reviews or procurement packets, use the contact page.
How do you ship RAG that survives production traffic?
Where do you draw the line between agents and deterministic workflows?
Which workflow patterns do you standardize for scale?
How do you keep LLM calls inside workflows maintainable?
What does observability and cost control look like end to end?
How do you satisfy security, residency, and vendor diligence?
Do you build operator software on top of automations?