The CloudOEngine Blog
Practical engineering for data and AI.
Technical guides on AI agents, RAG, data readiness, LLM costs, AI governance and video analytics, from the engineers at CloudOEngine.ai.
Build vs buy for enterprise AI platforms: how to decide
Buy the shared AI platform layer (data access, model gateway, permissions, audit, tracing) and build the workflows, prompts and tests that set you apart.
LLM OperationsSmall vs large language models: choosing the right size
Use a small language model for narrow, repetitive tasks you can test, and a large one for open-ended reasoning and broad knowledge. Decide on your own data.
AI GovernanceRole-based access control for AI assistants and agents
An AI agent should act with the permissions of the person it serves, capped by its own role, and every check belongs in retrieval and tools, not in the prompt.
RAG & RetrievalVector databases explained: what they do and when you need one
A vector database stores embeddings and finds the nearest ones fast with an approximate index. Below a few million vectors, your current database may be enough.
Vision & Video AIEdge vs cloud video analytics: latency, bandwidth and cost
Run video AI at the edge when uplink or response time is tight, in the cloud when you need heavy models and search, and in most sites, split between the two.
Data for AIData quality checks every AI project needs before launch
Before an AI launch, run automated checks for schema, nulls, duplicates, freshness, leakage, label errors and training-serving skew, and block on the worst.
AI AgentsAgent observability: what to trace in every AI agent run
Trace each AI agent run as one tree of spans: every model call, tool call and approval wait, with inputs, results, errors, tokens, cost and who it acted for.
AI StrategyAI ROI: how to measure whether an AI project paid back
Measure AI ROI against a baseline taken before launch and a comparison group, count every cost, and value freed hours only where the time is actually reused.
LLM OperationsHow to evaluate LLM applications: test sets, judges, regressions
Evaluate an LLM app with a test set built from real cases, cheap code checks first, an LLM judge you have checked against people, and a rerun on every change.
AI GovernancePrompt injection: what it is and how to defend AI agents
Prompt injection is text that hijacks an AI model's instructions. Agents can't be made immune, so limit what a fooled agent can reach and approve risky actions.
Data for AIData lineage explained: tracing an AI answer back to its source
Data lineage records where data came from and how it changed. For AI, join it to each run's record of documents, queries and dataset versions it used.
RAG & RetrievalChunking strategies for RAG: size, overlap and structure
Good RAG chunking splits documents on their own structure, keeps chunks a few hundred tokens with context, and tests size and overlap on real questions.
Vision & Video AIPeople counting with CCTV: how it works and how accurate it is
CCTV people counting detects people in each frame, tracks them, and counts line or zone crossings; accuracy depends on angle, crowding and light, so test it.
AI AgentsAI agents in production: what breaks and how to fix it
AI agents break in production through failing tools, loops, runaway cost and loose permissions. Limits, traces, approvals and evals keep them safe.
AI StrategyAI for mid-sized companies: where to start and what pays back
Mid-sized firms get AI payback by picking one frequent, measurable task with accessible data, buying the platform, owning the process and judging it at 90 days.
AI GovernanceAI governance in India: DPDP Act, audit trails, access control
Indian companies using AI on personal data must meet DPDP Act duties on consent, security, logs and breach notice, with most DPDP Rules in force from May 2027.
LLM OperationsControlling LLM costs: gateways, budgets, caching and routing
Control LLM costs by routing all calls through one gateway, caching repeated prompts, sending routine work to small models and tracking cost per task.
AI AgentsHuman-in-the-loop AI: when an agent should ask before it acts
An AI agent should ask a person before any action that is hard to undo, moves money, contacts outsiders or goes beyond its usual scope, and run the rest itself.
Data for AIIs your data ready for AI? A practical readiness checklist
Data is ready for AI when it is findable, accurate, fresh, traceable and access-controlled for a specific use case. Score yours with this 10-point checklist.
AI AgentsMCP explained: how Model Context Protocol connects AI to systems
MCP is an open standard that lets AI apps call tools and read data from your systems through one protocol, so long as you control permissions and inputs.
RAG & RetrievalRAG explained: why AI answers go wrong and how retrieval fixes it
RAG makes AI answer from your own documents by retrieving relevant passages first; most failures come from poor chunking, search, freshness or access rules.
Vision & Video AIVideo analytics accuracy: why AI counts fail and how to prove it
AI video counts go wrong through compression, occlusion, ID switches and line jitter; prove accuracy by matching each counted crossing to a human ground truth.