Agentic Systems
Tool-using agents, workflow orchestration, state and memory patterns, approval gates, secure runtime boundaries and event-driven execution.
AI Engineering
Build AI systems that can be operated, governed and evolved.
Dewtech designs and implements production-oriented LLM, RAG and agentic architectures with provider abstraction, observability, evaluation, security boundaries and deterministic execution controls.
Build beyond the prototype
Production AI is a systems-engineering problem.
The model is one dependency. Reliable AI products also need context management, tools, authorization, data architecture, evaluation, observability, cost controls and operational boundaries.
RAG & Knowledge Architecture
Ingestion, chunking, embeddings, vector and graph retrieval, tenant-aware authorization, provenance and retrieval evaluation.
LLM Platform Engineering
Provider abstraction, routing, BYOK patterns, inference proxies, rate limits, cost governance and resilient integration across model vendors.
Evaluation & Observability
Task-level evaluation, deterministic engineering gates, traces, token/cost telemetry and production feedback loops.
Architecture principles
Keep the intelligence flexible and the controls stable.
Provider independenceReduce coupling to a single model vendor and preserve operational choice.
Verification by designUse deterministic checks where correctness can be tested rather than judged.
Security boundariesSeparate model reasoning from authorization, credentials and privileged execution.
Delivery
From system intent to verified operation.
01Design
Problem, workflows, constraints, data and measurable outcomes.
02Architect
Models, retrieval, tools, security boundaries and operating model.
03Build
Implement incrementally with tests, evaluations and observability.
04Operate
Monitor quality, cost, security and system behavior over time.
Typical engagements
- Enterprise AI assistant or agent platform
- RAG over proprietary or regulated knowledge
- LLM-provider abstraction and routing
- Migration from prototype to production
- Secure coding-agent or autonomous workflow architecture
DARE connection
- Structured Design → Architect → Review → Execute workflow
- Human-in-the-loop engineering checkpoints
- Deterministic validation gates
- Secure agent runtime patterns
- Traceable execution and evidence
AI Engineering
Move from demo to engineered system.
Tell us the workflow, users, data and level of autonomy you need. We can design the architecture, build the system or review an existing implementation.