Digital AI Solutions — Europe
Our invoice pipeline silently dropped 4% of events into a dead-letter queue nobody watched. Six weeks later, the finance team noticed. Here is the observability gap and the polling fix that caught it.
We tracked 1,200 brand citations across Perplexity and AI Overviews. Pages older than 90 days saw citation rates drop by 74%, regardless of domain authority. Recency is the new ranking signal.
Our research agent kept appending intermediate reasoning to its context window. After 12 tool calls, the scratchpad hit 9k tokens, turning every subsequent LLM call into a budget-crushing expense.
Adding eight few-shot examples improved our classification accuracy by 14%. It also collapsed our semantic cache hit rate from 82% to 11%. Here is how we measured the bleed and rebuilt our cache boundaries.
We saved weeks by sharing a Postgres instance across SME tenants. Then one customer ran a bulk export, and row-level security locks starved every other query on the server.
Forcing temperature to zero seemed like the obvious fix for brand consistency. It also cut our usable generation rate in half, replacing creative variance with repetitive dead ends.
Adding a cross-encoder reranker lifted our RAG recall from 68% to 89%, but the synchronous inference call tripled p99 latency. Here is the asymmetric retrieval pattern that cut delay without surrendering quality.
When a downstream API tightened rate limits, our deterministic pipeline retried itself into a 4-hour backlog. A per-key circuit breaker cut recovery time from hours to 90 seconds.
We wired up llms.txt and structured our FAQ schema perfectly. ChatGPT read it. Then it cited Wikipedia instead. Here is why structured answers alone do not win GEO citations.
Autonomous agents accumulate tool results until they hit the context limit and silently truncate system instructions. We fixed it by capping the working memory. Here is the exact threshold we chose.
Our multi-step agent hit a 94-token retry that bloated to 3,100 tokens per run. The culprit was not the LLM, but an unbounded context accumulation strategy we forgot to cap.
We bought an auth SaaS to handle roles and logins. It worked perfectly until we needed to migrate a 12-tenant database schema. The real build-vs-buy gap is never the login form; it is the data layer.
Forcing LLMs into strict JSON schemas feels safe, but the hidden token cost of schema repetition and refusal loops will quietly destroy your unit economics. Here is how we measured and fixed it.
Switching to pgvector HNSW cut our index build time by 80% but spiked RAG p99 latency to 4.2 seconds. The culprit was a single default we failed to override for production queries.
When our message broker replayed 12,000 events overnight, only 14 duplicate actions escaped. Here is how idempotency keys turned a costly incident into a minor log entry, and the implementation pattern we now mandate.
AI engines cite you in ways you never planned. We tracked 12 unexpected mention variants across Perplexity and ChatGPT, revealing a massive blind spot in standard GEO tracking.
When an LLM tool call fails halfway through a ten-step agent run, re-running from scratch doubles latency and cost. Checkpointing state at each step cut our retry spend by 62%.
We swapped an embedding model to cut latency by 40ms. Three weeks later, recall silently dropped 18%. Here is the exact measurement that caught it and the reindexing trade-off we chose.
Most SMEs treat auth as a solved problem you just outsource. Here is why building custom access control cost us 40 hours upfront but saved 200 hours of integration hell later.
Raising citation strictness to 96 % precision in grounded assistants cuts useful recall to 41 %. Here is how we calibrate the threshold and why 85 % precision is the pragmatic ceiling for most enterprise copilots.
Moving an LLM feature from demo to production requires more than prompt tweaks. Without structured evaluations and guardrails, you are deploying an unpredictable system and hoping for the best.
From blank page to a broadcast-ready 15-second master: the exact timeline of a disciplined generative video production — visual anchoring, multi-shot generation, licensed audio.
ChatGPT, Perplexity and AI Overviews now answer instead of your pages. Generative Engine Optimization decides whether you are their source — or their blind spot.
Agent demos impress; production forgives less. Lessons from eighteen months of agents in continuous operation: real failures, guardrails that work.