“A promising LLM demo needs a production architecture, evaluation strategy and cost model.”
All consulting solutions
PROTOTYPE → GROUND → EVALUATE → SECURE → OPERATE
AI Engineering
Move AI, LLM, RAG and agentic applications from prototype behavior toward measurable, secure and operable production systems.
Typical client situations
Recognize the problem before choosing the solution.
These are representative situations, not prerequisites for engagement.
“RAG answers are inconsistent and the team cannot tell whether chunking, retrieval, reranking or prompting is the real problem.”
“Agents need tools or MCP integration, but permissions and failure modes are not yet controlled.”
“The organization needs to compare model / provider choices without coupling the whole product to one vendor.”
Consulting areas
AI / LLM architecture
RAG and retrieval
Vector search
Agentic workflows
MCP / tools
Evaluation
Observability
AI security
Production deployment
Cost / performance
Assessment scope
Use case and task boundaries
Model / provider dependencies
Prompt and context design
RAG ingestion / retrieval quality
Agent and tool permissions
Evaluation datasets and metrics
Tracing and failure handling
Data / security boundaries
Latency and cost
Representative engagements
Scope the engagement around the engineering decision.
Engagements can be short assessments, focused architecture work, implementation support or retained advisory.
AI architecture review
RAG quality assessment
Agent / MCP design review
Model / provider evaluation
AI security review
Production-readiness assessment
PoC / prototype engineering
Deliverables
Leave with decisions, evidence and a path forward.
Deliverables are selected during scoping. Not every engagement needs every artifact.
01
AI system architecture
02
Evaluation plan and benchmark set
03
RAG / retrieval findings
04
Agent / tool trust-boundary model
05
Provider / model trade-off analysis
06
Observability recommendations
07
Production roadmap
Intended outcomes
Measurable AI quality
More reliable retrieval and agent behavior
Reduced security and data risk
Clearer model / provider decisions
Better latency and cost control
Technology context
PythonLLMsEmbeddingsVector databasesRAGMCPAI AgentsEvaluationMLOps / LLMOpsObservability
PoCs and production hardening can be included, with model, provider and data-access choices kept explicit so the architecture does not silently lock into one vendor.
How we can engage
Assess
Independent technical assessment of architecture, reliability, security, performance or operational readiness.
Architect
Reference architecture, design decisions, technology selection and implementation roadmap.
Implement
PoCs, integration, migration, automation, optimization and selected hands-on engineering work where scope permits.
Advise
Ongoing technical advisory, architecture reviews, troubleshooting support and engineering guidance.
Start with the problem