From the Lab

The end-to-end pipeline for training SLMs that understand enterprise terminology and map user intent to system operations, at a fraction of frontier model costs.

A candid walk through the architecture decisions behind our production RAG pipelines including the approaches we tried first that did not work, and why each component exists to address a specific failure mode.

Commercial fleets generate massive volumes of telemetry data from TPMS, engine diagnostics, and fuel systems. We built a predictive maintenance pipeline that turns this raw data into calibrated alerts reducing unplanned downtime by 18% and improving fuel efficiency by 12%.

Single AI agents hit hard limits in complex enterprise operations. We examine the coordination patterns, architectural trade-offs, and practical decision framework for knowing when your problem demands a multi-agent system.

Practical lessons from generating synthetic training data for domain-specific small language models, covering teacher-student pipelines, quality filtering, and the mistakes that burn compute without improving results.

Most AI ROI frameworks fixate on headcount reduction. The real value of AI agents lies in task compression, error reduction, time-to-insight acceleration, and decision quality improvement. Here is how to measure what actually matters.

Multi-agent systems fail in ways that traditional monitoring cannot detect. We break down the three pillars of agent observability distributed tracing, continuous evaluation, and cost monitoring with practical debugging workflows from production deployments.

RAG evaluation seems straightforward until you try to do it well. We document three phases of our evaluation approach, each prompted by a failure the previous method missed.

Regulated industries demand audit trails, explainability, and human oversight from AI systems. These requirements are not obstacles they are architectural patterns that produce better, more trustworthy production systems.

Production RAG systems are far harder than demos suggest. We share lessons from building retrieval-augmented generation pipelines across device management, fleet compliance, and pharmaceutical quality.

An honest account of the failure modes, debugging nightmares, and hard-won patterns from building our first production multi-agent system for pharmaceutical quality investigation.

Most enterprise AI projects never reach production not because the models fail, but because governance, data readiness, integration, evaluation, and operations are missing. We outline the five gaps that kill AI projects and a maturity framework for closing them.

A practical guide to building MCP servers and A2A integrations for enterprise systems, drawn from our work across device management, fleet operations, and pharmaceutical platforms.

Practical lessons from fine-tuning small language models for enterprise domains using Unsloth AI, including model selection, synthetic data generation, and deployment trade-offs.

Enterprise software is powerful but unusable. The next interface paradigm is not better dashboards it is natural language agents that sit on top of existing systems and translate intent into action.

How we built a multi-agent AI system inside WeGuard that turns complex MDM operations into natural language interactions, reducing routine device management tasks by 70%.

Wenable has built software for over a decade. When we expanded into applied AI, we discovered which engineering skills transferred, which required relearning, and what was entirely new.

A practical architecture framework for building enterprise-grade agentic AI, from foundation infrastructure through model intelligence, orchestration, and governance.