Insights on enterprise AI
Practical guidance, case studies, and analysis on AI strategy, RAG, agents, automation, and integration — written by IDS engineers and consultants.
Why your RAG system gets worse over time — and how to fix retrieval drift before users complain
The first 90 days, your RAG system feels accurate. By month five it’s firefighting. Four drift drivers, four detection signals, three embedding refresh strategies, and the operational practices that catch the regression in dashboards instead of customer complaints.
From RAG to GraphRAG: when vector search isn’t enough for legal, finance, and engineering docs
Vector search finds chunks similar to your query — that’s the whole mechanism. For legal contracts, financial filings, and engineering BoMs where relationships between entities matter, similarity isn’t structure. A practical guide to GraphRAG and the hybrid retrieval pattern that fits most enterprises.
Knowledge graphs + LLMs for Vietnamese enterprises: handling language nuance at scale
Vietnamese tone marks. Compound-noun word boundaries. Company-name conventions (Công ty Cổ phần / TNHH / JSC). Administrative restructuring of districts and wards. Code-switching with English. Regional vocabulary. Six realities that break off-the-shelf retrieval — and how a knowledge-graph layer handles them.
Computer-use agents vs. legacy RPA: where each one actually belongs
UiPath and Automation Anywhere aren’t dead — they’re still doing real work in real enterprises. But computer-use agents handle the tasks RPA was always bad at. A six-question allocation rule and the hybrid pattern most enterprises actually need.
The token economics of scale: keeping AI costs flat as usage 10×s
Token cost grows linearly with usage. Five well-known levers — model routing, prompt caching, response budgets, batch APIs, eval-driven downgrades — compound to flatten that curve. Most teams pull them out of order. The eval suite is the prerequisite for the biggest savings.
Beyond chatbots: agentic AI is finally crossing into core enterprise workflows
Agentic AI — models that plan, call tools, verify their own outputs — has crossed the threshold from demo to production. Three things change in the architecture, three workflows earn it first, and one rule of thumb tells you when not to reach for an agent.
The CFO's AI scorecard: measuring real ROI in the first 12 months
Most AI projects fail the CFO test not because they didn’t work but because nobody measured them in finance terms. Four buckets — revenue, cost, risk, capability — each with a baseline, a target, and a 30/60/90 cadence so the answer in month twelve doesn’t rest on storytelling.
Voice AI for Vietnamese customer service: dialects, code-switching, and brand voice
Voice AI works well in English. For Vietnamese customer service, off-the-shelf stacks miss three things — regional dialect variation, English/Vietnamese code-switching mid-call, and brand-appropriate Vietnamese register. Each one shows up in CSAT before the engineering team notices.
Five prompt injection patterns most security teams aren't testing for
Direct injection is the easy one. The four patterns that get past production red-teams — indirect injection via retrieved documents, tool-call hijacking, multi-turn context manipulation, encoding tricks — are the ones worth running before you ship.
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