Insights
Pillar guides, cost models and buyer's frameworks on AI development, data platforms, MVPs and custom software, written by the engineers who deliver them, not a content team.
Each topic cluster has one comprehensive guide. Read it first, then go deeper with the supporting articles.
Buyer's guides · Pillar guide
The pillar buyer's guide: why listicles mislead, a weighted scorecard, an RFP template and the reference calls that reveal how a vendor behaves under pressure.
Decision · 12 min read ·
Machine learning & GenAI · Pillar guide
The pillar guide to buying ML: use-case selection, the delivery lifecycle, MLOps expectations, cost bands and how return is actually measured.
Consideration · 13 min read ·
Data engineering · Pillar guide
The pillar guide to data platforms: reference architecture, build sequence, governance model, cost control and the anti-patterns that produce expensive shelfware.
Consideration · 14 min read ·
Startups & MVP · Pillar guide
The pillar guide to MVPs in the AI era: how to scope one, what to build versus assemble, an eight-week plan and the evidence investors actually ask for.
Awareness · 13 min read ·
Software engineering · Pillar guide
The pillar guide to custom software: build-versus-buy economics, scoping, delivery models, pricing, governance and the risks that sink projects.
Consideration · 14 min read ·
AI development · Pillar guide
The pillar guide to buying AI engineering: capability model, delivery model, pricing structures, evaluation criteria and the failure modes that kill AI programmes before production.
Consideration · 14 min read ·
Buyer's guides
A 12-month transformation blueprint: how to pick the first portfolio, stand up governance, build capability and report progress the board believes.
Decision · 13 min read ·
Buyer's guides
Rates, time-zone overlap, communication cost, legal exposure and delivered value compared, with a decision matrix by project type and risk profile.
Decision · 10 min read ·
Buyer's guides
Models, real costs, contracts, IP, security and the weekly operating rhythm that separates successful outsourcing from expensive disappointment.
Consideration · 12 min read ·
Buyer's guides
A differentiation test, five-year TCO comparison and lock-in analysis, plus the hybrid pattern that lets you buy the commodity and build the edge.
Decision · 11 min read ·
Buyer's guides · Pillar guide
The pillar buyer's guide: why listicles mislead, a weighted scorecard, an RFP template and the reference calls that reveal how a vendor behaves under pressure.
Decision · 12 min read ·
Software engineering
Rewrite, replatform, refactor or encapsulate? An assessment method, the strangler-fig pattern in practice, and how to keep the business running throughout.
Consideration · 13 min read ·
Machine learning & GenAI
Agents are workflow software with a probabilistic core. The architecture, approval gates and observability that make them safe, and the processes to start with.
Consideration · 12 min read ·
Machine learning & GenAI
What an LLM is, in language a board can act on: capability, cost, failure modes, vendor lock-in and the governance questions to ask before deployment.
Awareness · 10 min read ·
Data engineering
Three disciplines, constantly confused in job specs. Responsibilities, deliverables, tools, hiring order and the team structures that actually ship.
Awareness · 9 min read ·
Startups & MVP
The diligence checklist for choosing a build partner: team seniority, decision rights, IP, exit terms and the contract clauses that protect a startup.
Decision · 11 min read ·
Software engineering
Modular monolith, services or events? A decision framework grounded in team size, change rate and failure domains rather than conference fashion.
Awareness · 12 min read ·
Software engineering
Multi-tenancy, billing, entitlements, compliance and onboarding. The decisions taken in month one that determine whether a SaaS can scale internationally.
Consideration · 13 min read ·
Machine learning & GenAI · Pillar guide
The pillar guide to buying ML: use-case selection, the delivery lifecycle, MLOps expectations, cost bands and how return is actually measured.
Consideration · 13 min read ·
Data engineering
The unglamorous work behind good AI answers: inventory, permission-aware retrieval, chunking strategy, freshness and the metrics that prove readiness.
Consideration · 12 min read ·
Data engineering
Six pipeline patterns, when each applies, and the reliability practices (idempotency, backfills, contracts, observability) that keep them from waking you at 3am.
Awareness · 11 min read ·
Data engineering · Pillar guide
The pillar guide to data platforms: reference architecture, build sequence, governance model, cost control and the anti-patterns that produce expensive shelfware.
Consideration · 14 min read ·
Data engineering
Every stalled AI programme has the same root cause. Data engineering explained in business terms, with a maturity test you can run this week.
Awareness · 10 min read ·
Startups & MVP
Four models for technical leadership, what they cost in cash and equity, and the ten questions a non-technical founder can use to audit any build.
Awareness · 10 min read ·
Startups & MVP
AI compresses some parts of a build by 40% and adds risk to others. Here is the honest split, with the guardrails that keep velocity from becoming rework.
Awareness · 10 min read ·
Startups & MVP
Scaling is a sequence, not a heroic rewrite. Here is the order to do things in, and the technical debt that is worth taking deliberately.
Awareness · 12 min read ·
Startups & MVP · Pillar guide
The pillar guide to MVPs in the AI era: how to scope one, what to build versus assemble, an eight-week plan and the evidence investors actually ask for.
Awareness · 13 min read ·
Software engineering
A defensible cost model: rates by region and seniority, line items most quotes hide, three worked budgets and the five levers that actually move the number.
Decision · 12 min read ·
Software engineering · Pillar guide
The pillar guide to custom software: build-versus-buy economics, scoping, delivery models, pricing, governance and the risks that sink projects.
Consideration · 14 min read ·
Machine learning & GenAI
A decision matrix across cost, accuracy, freshness and maintenance, plus the hybrid pattern most mature teams end up with.
Consideration · 11 min read ·
Machine learning & GenAI
A full RAG reference: chunking strategies, hybrid retrieval, reranking, citation, caching, evaluation, and the seven mistakes that make RAG look bad.
Consideration · 13 min read ·
Machine learning & GenAI
Most internal assistants die of low adoption, not bad models. The architecture, permission model and adoption design that keep usage above 50%.
Consideration · 12 min read ·
Startups & MVP
Three worked MVP budgets, the hidden line items founders forget, and the four levers that reduce cost without producing something unlaunchable.
Decision · 11 min read ·
AI development
Advisory decks do not ship. Delivery without strategy ships the wrong thing. Here is how to sequence the two and what each should cost.
Consideration · 10 min read ·
AI development
A weighted scorecard, the RFP questions that actually discriminate between vendors, and eleven red flags worth walking away from.
Decision · 11 min read ·
AI development
Most generative AI never leaves the demo. This is the production path: architecture, evals, guardrails, cost control, release management and the metrics that justify the next quarter.
Consideration · 13 min read ·
AI development
Off-the-shelf AI stops where your workflow starts. Here is the reference architecture, team shape and delivery sequence behind custom AI products that survive contact with real users.
Consideration · 12 min read ·
AI development
A cost model you can defend in a board meeting: what drives AI application budgets, what the line items actually are, and three worked examples from pilot to platform.
Decision · 12 min read ·
AI development · Pillar guide
The pillar guide to buying AI engineering: capability model, delivery model, pricing structures, evaluation criteria and the failure modes that kill AI programmes before production.
Consideration · 14 min read ·
Migrating an AI-generated prototype? Read how we take a vibe-coded MVP to production.
Next step
Bring the problem to a discovery call and we will tell you what we would build first, and what it costs.
The engineering notes
The findings from the apps we audit, the fixes that worked, and what each one cost, written by the person who did the work. No roundups, no reposts.
