Skip to content
Resource Center

What we know,
written down.

Frameworks, guides, and technical deep dives from the work of building production AI systems. No gated content. No email capture. Just the thinking.

FeaturedRES_07

Agentic Software Development Is the New Default

Three independent data sets, MIT Technology Review Insights (2026), Stanford HAI AI Index (2026), McKinsey State of AI (2025), converge on the same direction: software engineering is being redesigned around agents gated by human architects.

98% of senior engineering leaders expect pilot-to-production acceleration (avg 37%)
SWE-bench Verified: agent performance climbed from 60% to ~100% in one year
Fewer than 10% of vertical AI use cases reach production, operating-model change is the differentiator
7 min
ArchitectureRES_01
8 min

Why Your Recommendation Engine Keeps Underperforming

The problem is almost never the algorithm.

The problem is almost never the algorithm. It's the feature pipeline upstream of it, sparse identity data, cold-start gaps, and missing real-time session context.

Sparse identity data kills personalization before the model runs
Cold-start gaps and how to bridge them with session context
Read article
FrameworkRES_02
6 min

The 6-Artifact Discovery Sprint: What You Own When We're Done

A Discovery Sprint that ends with a slide deck is theatre.

Ours ends with 6 portable, client-owned artifacts: architecture blueprint, data audit, risk register, cost model, evaluation plan, and a build roadmap.

6 portable artifacts you own at handoff
Architecture blueprint, data audit, risk register, cost model
Read article
Technical Deep DiveRES_03
10 min

MLOps Is Not DevOps. Stop Treating It That Way.

CI/CD pipelines for software don't translate cleanly to ML.

Data drift, model decay, feature skew, and retraining triggers require a fundamentally different operational model.

Why standard CI/CD breaks for ML systems
Data drift, model decay, and feature skew explained
Read article
DomainRES_04
7 min

Clinical AI Accuracy Is the Wrong Primary Metric

A model that's 97% accurate but misses 60% of critical cases is a liability.

We explain why sensitivity/specificity calibrated to clinical risk thresholds is the only metric that matters in production.

Why accuracy alone misleads in clinical contexts
Sensitivity/specificity calibrated to risk thresholds
Read article
GuideRES_05
5 min

The AI Readiness Questions Every CTO Should Answer Before Signing a Contract

Four dimensions that determine whether your AI project ships or stalls.

Data ownership, labelling infrastructure, integration architecture, and team capability, these four dimensions determine whether your AI project will ship or stall.

Data ownership and labelling infrastructure checks
Integration architecture readiness signals
Read article
DomainRES_06
9 min

Identity Fragmentation Is the Hidden Killer of Personalisation

Users have 4–8 digital identities across your stack.

Until you unify them, your personalisation system is working from an incomplete picture, and the model performance reflects that.

How identity fragmentation silently degrades model performance
The identity unification patterns that unlock personalization
Read article
Self-serve tools
AI Readiness Quiz

Score your project's readiness across the dimensions that predict AI success.

Delivery Benchmark

Remilink vs. traditional agencies vs. in-house, across timeline, cost, and risk dimensions.

Got a specific question?

We're happy to talk through a technical challenge before any engagement. That's just the way we work.

Start a conversation