We engineer the AI systems that actually ship to production.
Not chatbots. Not demos. Reliable, observable, business-impactful automation — built on the same software-engineering rigor we apply to mission-critical platforms.
An AI engineering practice — not a prompt shop
From discovery to evaluation, every layer of the system is treated like production software.
Multi-step workflow automation
Deterministic orchestration layers wrapped around LLMs. Reliable, retryable, observable — not demoware.
RAG over enterprise knowledge
Custom retrieval pipelines tuned to your data: chunking, hybrid search, re-ranking, and evals you can trust.
Agents & copilots
Domain-aware copilots that take action — with tool use, memory, guardrails, and human-in-the-loop checkpoints.
Data pipelines & vectorization
ETL into vector stores, real-time embedding updates, and freshness-aware retrieval at scale.
Evals, guardrails, and safety
Regression-tested prompts, output validators, and policy filters — the layer that makes AI shippable.
Observability for AI
Token-level tracing, latency budgets, and cost dashboards. You see what the model did, and what it cost.
What a Dunify AI workflow actually looks like
A reliable AI system is mostly software engineering. Here's how the pieces fit together.
Event, ticket, webhook, schedule
LLM + rules taxonomy
RAG over knowledge base
Policy + guardrails
Tool calls, system integrations
Human-in-the-loop (when needed)
Evals, feedback, regression tests
Reliable orchestration
Stateful, idempotent, retry-aware. Temporal or LangGraph as the backbone.
Composable tool layer
Tools and integrations as typed contracts. LLM picks, infra executes.
Eval-first delivery
Golden datasets, regression tests, and prompt versioning from day one.
The stack we reach for
Opinionated, but not dogmatic. We pick the tools that fit the problem — not the trend.
From idea to a production AI system — in weeks.
Bring us a problem; we'll come back with an architecture, a delivery plan, and a price you can sign.