From Big Data to Generative AI: Scaling LLMs in Enterprises 2026

In October 2015, Madrid hosted the fourth edition of Big Data Spain. Additionally, more than 600 experts debated Spark, Hadoop and NoSQL. Ten years later, the conversation has changed radically. Now, it is no longer about storing petabytes, but about deploying large language models (LLMs) in production. Additionally, sub-second latencies, regulatory governance and measurable ROI are required.

At BAOSS we have accompanied this transition. What in 2015 was a data engineering challenge —on-premise Hadoop clusters, nightly ETLs, data lakes without governance— is now a different problem. That is, we now talk about autonomous agent architecture, enterprise RAG and optimized inference serving. The difference: in 2015, 80% of the effort went to data plumbing. In contrast, in 2026, 70% is concentrated on prompt engineering, continuous evaluation and security guardrails.

The real 2026-2027 problem: from POC to production

According to Deloitte’s State of AI in the Enterprise 2026, 67% of Spanish organizations have at least one generative AI pilot. However, only 12% have scaled to production with clear business metrics. The bottlenecks repeat themselves:

Inference latency and cost: GPT-4o costs $15/M input tokens and $60/M output tokens. This makes certain use cases unviable at scale.
Governance and compliance: EU Regulation 2024/1689 (AI Act) is demanding: it requires traceability, risk assessment and human oversight in high-risk systems.
Tool fragmentation: LangChain, LlamaIndex, Haystack, LangGraph, CrewAI, AutoGen, Semantic Kernel. Each framework solves one part. However, enterprise integration (SSO, audit, CI/CD) is left to the team.
Continuous evaluation: static benchmarks (MMLU, HumanEval) do not reflect quality in a specific domain. Eval-driven development with LLM judges and business metrics is missing.

At BAOSS we have seen projects stall for 8-12 months in the validation phase. The cause? Not addressing these four fronts from the initial architecture.

Our approach: modular architecture, evaluation first, production day-one

There is no silver bullet. What works is a reference pattern. We have refined it in 23 production deployments (2023-2025) in banking, insurance, logistics and the public sector:

Unified inference layer: vLLM + Ollama for open models (Llama 3.1 70B, Qwen 2.5 72B, Nemotron 3 Ultra) behind a gateway. This gateway performs semantic routing and fallback to proprietary APIs (GPT-4o, Claude 4 Sonnet) only when latency/cost justifies it. Additionally, the average inference cost reduction is 62%.
Enterprise RAG with MCP (Model Context Protocol): standardized connectors to internal sources (Confluence, SharePoint, GitLab, ERP, vector databases —Qdrant, Weaviate, Milvus— and knowledge graphs). Additionally, elimination of fragile custom connectors.
Agent orchestration with LangGraph + CrewAI: deterministic flows (LangGraph) for regulated processes. Then, collaborative agent teams (CrewAI) for exploratory tasks (research, offer analysis, code review). Additionally, AutoGen for multi-agent debate on critical decisions.
Continuous evaluation (EvalOps): automated pipeline: golden dataset curated by domain experts → LLM judges (GPT-4o-mini as evaluator) → business metrics. These metrics include accuracy in clause extraction, resolution time and internal NPS, with alerts in Grafana/Prometheus. Additionally, there is a weekly feedback cycle.
Observability and guardrails: OpenTelemetry + Langfuse + custom span processors provide full traceability. Thus we log prompt, retrieval, tool calls, latency, tokens and final decision. Additionally, programmatic guardrails (Regex, PII detection, policy-as-code with OPA) act before requests reach the LLM.

Case 1: Banking — Regulatory compliance with RAG + MCP

First, the context: a Spanish financial institution (top 10 by assets) with 3,200 employees in risk, compliance and legal areas. Need: answer regulatory queries (CRR, CRD, EBA guidelines, BdE circulars) in < 5 seconds. Additionally, with full traceability for audit purposes.

Then, BAOSS solution:

Ingestion of 12,400 PDF/HTML documents (regulation, internal circulars, case law) → semantic chunking with LlamaIndex. Additionally, late chunking to preserve legal structure.
Then, indexing in Qdrant (HNSW, binary quantization) + knowledge graph in Neo4j (article-section-reference relationships).
Additionally, unified MCP server: connectors to the internal regulatory repository, BdE API, EUR-Lex. Retriever agent with hybrid search (BM25 + dense + graph traversal).
Then, response generation with Llama 3.1 70B (vLLM, 4x H100) + citation enforcement via constrained decoding. Fallback to GPT-4o only for ambiguous queries (< 8%).
Finally, Teams/SharePoint interface with human-in-the-loop: the lawyer validates, edits or rejects. Every interaction feeds the golden dataset.

Metrics at 6 months (in production since March 2025):

First, 78% reduction in regulatory search time (22 min → 4.8 min/query).
Additionally, accuracy in correct citation (validated by experts): 94.3% (versus 67% for the legacy search engine).
Then, cost per query: €0.018 (vs €0.12 estimated with pure GPT-4o).
That is, ROI 3.4x in 6 months (savings in lawyer hours + reduction of sanction risk).
Finally, EBA audit passed with no observations on response traceability.

Case 2: Manufacturing — Predictive maintenance with autonomous agents

Context: Automotive plant (Tier 1) with 1,200 monitored assets (vibration, temperature, current, oil). That is, the goal is to reduce unplanned downtime and optimize maintenance windows.

BAOSS solution:

Edge-cloud architecture: local inference (Ollama + Llama 3.2 3B quantized Q4_K_M) on industrial gateways for alarm pre-filtering. Then, scaling to a central GPU cluster (vLLM + Nemotron 3 Ultra) for root-cause analysis.
Multi-agent system (CrewAI): Data Collector (OPC UA connectors, PI historian) and Diagnostician (RAG over OEM manuals, work order history, SAP PM tickets). Additionally, Planner (generates the work order with priority, spare parts, optimal shift) and Validator (checks safety, availability, calendar conflict).
Additionally, LangGraph for the deterministic escalation flow: alarm → diagnosis → planning → human approval → execution → feedback.
Finally, in-context training with 2,400 historical tickets labeled by senior engineers. Weekly evaluation with 150 hold-out cases.

Results at 9 months (in production since January 2025):

41% reduction in unplanned downtime (MTBF +34%).
Root-cause diagnosis accuracy: 89% (vs 62% for the previous expert system).
Average diagnosis time: 3.2 min (vs 47 min manual).
Estimated savings: €1.8M/year in lost production + €320k/year in unnecessary spare parts.
Operator adoption: 91% (interface integrated into the shift tablet, natural language).

Case 3: Logistics — Last-mile optimization with Bayesian + LLM

Context: E-commerce logistics operator (12M shipments/year). Challenge: dynamic re-routing in the face of incidents (traffic, absenteeism, demand peaks) with explainability for fleet managers.

BAOSS solution:

Bayesian optimization engine (PyMC + NumPyro) for probabilistic estimation of delivery times per route/hour/driver. Inputs: GPS history, Waze API, HR, AEMET weather.
LLM agent (GPT-4o-mini + RAG over operating manuals, agreements, low-emission zone) that translates the Bayesian model output into natural language and proposes actionable alternatives: “Reassign 3 routes to driver X (available, ADR certified), estimated savings 42 min, low risk”.
MCP for access to TMS (Transporeon), WMS (Manhattan), HR (Workday). AutoGen for debate between the optimizer agent and the risk agent before proposing.
Deployment on Kubernetes (EKS) with KServe + vLLM. Weekly canary releases with automatic evaluation (metrics: % of routes accepted, actual vs estimated deviation,

Prefer to talk to us directly? Contact the BAOSS team and tell us about your challenge.