Beyond the Prototype: How Undocked Delivers AI Solutions That Actually Ship

Designing, building, and rigorously testing enterprise AI — from strategy to production

AI is having its 'pilot purgatory' moment. Nearly every enterprise has a chatbot proof-of-concept, a generative AI experiment, or a RAG prototype sitting in a sandbox. Very few have AI that runs reliably in production, grounded in enterprise data, guarded against misuse, and trusted by the business.

The gap is not intelligence. The models are good enough. The gap is engineering discipline - the ability to design AI systems that integrate with enterprise platforms, develop them with modern architectures, and test them with the rigour we demand of any mission-critical software.

This is where Undocked operates. Our practice is built on three pillars - Implementation & Integration, Modernization & Engineering, and Strategy, Advisory & Transformation - all powered by Meridian. While our heritage is supply chain technology (WMS, OMS, TMS, automation), the same engineering DNA makes us uniquely suited to help enterprises design, build, and validate AI solutions end-to-end.

Strategy First:

Anchoring AI in the Business

AI projects fail when they start with the model and work backwards. They succeed when they start with the decision the model is supposed to support.

Our Strategy, Advisory & Transformation practice brings the same playbook we use for platform selection and architecture design to AI initiatives:

• Use-case prioritisation - identifying where AI actually pays back, versus where deterministic automation or a better search index would do the job.

• Vendor and architecture selection - evaluating model providers, RAG frameworks, vector stores, orchestration layers, and evaluation tools against concrete requirements.

• Transformation governance - standing up the PMO, architecture oversight, and programme leadership that keep AI programmes honest across quarters, not just sprints.

Data-driven diagnostics and AI-driven process analysis help clients see where intelligent automation creates leverage, and where it introduces risk without return. Strategy, done well, is the cheapest part of an AI programme - and skipping it is the most expensive mistake.

Design & Development:

Engineering AI Like Enterprise Software

Once the strategy is set, the real work begins. An AI solution in production is not one model - it is a distributed system of retrievers, prompt templates, orchestration logic, enterprise APIs, guardrails, monitoring, and feedback loops.

Our Modernization & Engineering practice treats AI workloads the way we treat any modern platform:

• Custom engineering and microservices that expose AI capabilities as enterprise-grade APIs, not fragile notebooks.

• Legacy modernization and business-logic preservation, so AI features can be layered onto existing ERP, WMS, and CRM systems without destabilising them.

• Data and intelligence pipelines - embedded analytics, observability, and data-driven systems that let the business see what the AI is doing and why.

• AI-assisted migration and code translation, where we use AI to accelerate the modernization of the very systems that will eventually host new AI features.

On the Implementation & Integration side, we connect AI solutions into the operating fabric of the enterprise - WMS ↔ OMS ↔ ERP ↔ TMS, API-led and event-driven, with the same discipline we apply to enterprise integrations. An AI assistant that cannot reliably reach the order system is a demo, not a product.

Testing:

Where Most AI Programmes Quietly Fail

Here is the uncomfortable truth about enterprise AI: most teams do not know how to test it.

Traditional QA assumes deterministic output. Given input X, the system should produce output Y. LLM-powered systems break that contract. The same prompt can return subtly different answers across runs. Responses can be fluent but factually wrong. Guardrails can hold for a hundred prompts and fail on the hundred-and-first.

This is why our AI testing practice is built on a dedicated, solution-agnostic QA Automation Framework - one we have proven on real client engagements and designed to be the unified quality gate for every AI-powered solution across the enterprise.

The Framework, in Plain Terms

The framework is built for onboarding new AI solutions with zero framework changes - add a feature file and a CSV of test data, and the pipeline takes care of the rest.

Under the hood:

• A BDD approach using BehaveX with Gherkin feature files keeps test scenarios readable by both engineers and business analysts.

• Both API-level and UI-level testing (Playwright-based) validate the chatbot the way real users and real integrations experience it.

• Two independent LLM evaluation frameworks - DeepEval and RAGAS - run in parallel for cross-validated scoring.

• CSV-driven test data lets business stakeholders add or modify prompts without touching code.

• A central threshold config defines the pass bar for every metric, so quality gates are explicit, not tribal.

• A full reporting pipeline - BehaveX HTML, Allure, custom dashboards, DeepEval-vs-RAGAS comparison reports, and email notifications.

What We Actually Test For

• Semantic consistency - paraphrased questions must produce semantically equivalent answers. Target: ≥85%.

• Red teaming and guardrails - prompt injection, jailbreaks, and off-topic prompts must all be refused cleanly. Target: 100%.

• PII and data privacy - injected credit card numbers, emails, and phone numbers must never be echoed or leaked.

• RAG accuracy and grounding - responses must be traceable to retrieved enterprise data. Target: ≥90% faithfulness.

• RAG contextual recall - the retriever must fetch the right context chunks before generation begins. Target: ≥90%.

• Intent classification and routing - user queries must be mapped to the correct flow. Target: ≥95% accuracy.

• Negative and edge cases - invalid inputs must degrade gracefully, not crash or leak.

• Hallucination detection - queries about non-existent products must return 'not found,' not invented data. Target: ≤5%.

• Responsible AI - biased or sensitive prompts must produce neutral, ethical responses.

• Role-based access - Admin, Employee, Retail, Trade, and Guest personas must each see only what they are entitled to.

• Escalation and handoff - conversations must transfer to a human with full context preserved. Target: ≥95%.

• Multi-turn context retention - follow-up questions must remain coherent across the conversation.

4. Powered by Meridian

Across all three pillars - Implementation, Modernization, Strategy - our work is Powered by Meridian. Meridian is our internal use of AI to accelerate our own delivery:

• Faster requirement structuring and design coherence during the implementation phase.

• Accelerated testing, data conversion, and validation - including the LLM-evaluation scaffolding described above.

• AI-assisted migration and code/logic translation during modernization.

• Faster analysis and insight generation for strategy work, leading to data-backed decisions.

The meta-point: we do not just build AI for clients. We use AI to build better - and then pass that velocity through to the engagement.

Why This Matters

The enterprises that will win with AI over the next few years are not the ones with the most models. They are the ones with the discipline to:

• Start from the business problem, not the technology.

• Engineer AI as a first-class enterprise system, integrated with the platforms of record.

• Test AI with the rigour its failure modes demand - semantic consistency, grounding, guardrails, privacy, and everything in between.

• Govern AI programmes with the same architectural oversight used for any transformation.

Undocked brings a rare combination to this work: deep supply chain and enterprise-systems heritage, a modernization and engineering practice that treats AI as a build-and-run discipline rather than an experiment, and a proven, solution-agnostic testing framework that gives AI the quality gate it has been missing.

If your AI programme is ready to move from pilot to production - with the confidence that it will hold up under real users, real data, and real scrutiny - we would like to talk.