Blog — werea

Blog

The problems we run into taking AI systems to production, the architecture decisions we make, and the technical ground real projects cover.

No marketing language: engineering- and implementation-focused writing that lights the way on your own AI adoption journey.

Architecture decisions3 From the field3 Integration2 Technical deep dives2

FOCUS AREAS

Content categories

Architecture and decision notes

Which model did we pick, and why? The technical and financial rationale behind infrastructure decisions.

Technical deep dives

Practical guides on RAG architectures, model evaluation methodology, data pipelines and system monitoring.

Lessons from the field

Real case experience with processes that work in theory but stall in live production.

Architecture decisions3 min

Documenting technical decisions: why we write our architectural choices down

A technical decision is not finished when it is discussed — it is finished when it is written down with its reasons.

11 August 2026

Integration3 min

AI-assisted automation in document and data processing pipelines

Where OCR gives up on documents whose layout keeps changing, pipelines backed by language models take over.

11 August 2026

From the field3 min

Cost optimisation in AI projects: managing API and infrastructure spend

If the budget line is not drawn while the architecture is being designed, token spend gets away from you in production.

11 August 2026

From the field3 min

Managing risk in production: real-time model monitoring and rollback

Going live is the beginning, not the end — and a rollback path is a requirement, not a preference.

11 August 2026

Architecture decisions3 min

Data security and RAG architecture in closed systems

Finding the right answer is not enough: the model must not answer from a document the asker cannot see.

11 August 2026

Integration3 min

Modernising legacy systems: how AI integration actually gets done

Modernisation through an intelligent layer placed in between, without stopping the system that works.

11 August 2026

Technical deep dives3 min

Decisions from measurement, not instinct: how do you measure LLM success?

Any line of code written before the success criteria and test cases exist is a line written too early.

11 August 2026

Technical deep dives3 min

Effective context management with vector databases

Why semantic search works, why it slows down under load, and how that bottleneck gets cleared.

11 August 2026

Architecture decisions3 min

Choosing the right architecture for enterprise data: fine-tuning or RAG?

The cost and flexibility gap between locking data into a model's parameters and fetching it on demand.

11 August 2026

From the field3 min

From prototype to production: why LLM projects stall on the way

What keeps an impressive demo from going live is rarely technical — it is a split between two teams.

11 August 2026

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See More Than the Writing — See It Live

Instead of reading about it in theory, experience directly how AI creates value inside your own business processes.

In a 45-minute remote session, with no preparation needed on your side, we test a working system on screen together with your own data and your own scenario.

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