Blog

Field notes from an AI-first engineering practice

How we ship LLM features, run agentic workflows, structure AI MVPs, and operate AI products in production, written by the people doing it.

Hire or Automate: Which Costs Less?

Automation usually costs less than hiring when the work is repetitive and follows clear rules. Hiring is the better choice when the work needs judgment or changes every week.

Daniela Cazac
Daniela Cazac

Which Business Process Should You Automate First?

Start with the process that repeats often, follows clear rules, wastes the most hours, and connects to only one or two tools. Here is a 10-minute way to pick yours.

Daniela Cazac
Daniela Cazac

How Much Does Business Process Automation Cost?

What decides the price of automating a business process, how to compare it with hiring, and the questions to ask any vendor before you sign.

Daniela Cazac
Daniela Cazac

The AI Discovery Mission: de-risk AI in four weeks

A four-week structured discovery is how we turn a vague AI ambition into a build-ready plan, vision, evals, architecture, and a defensible budget.

Nicu Mardari
Nicu Mardari

Why we ship AI MVPs in six weeks, not six months

Validated learning beats elaborate roadmaps. The cadence we use to put an AI product in front of real users, and real evals, in six weeks.

Daniela Cazac
Daniela Cazac

Fixed price vs. T&M for AI engineering: LLM budgets

AI projects break the assumptions behind fixed-price contracts. Here's a pricing framework that aligns incentives between AI vendors and clients.

Nicu Mardari
Nicu Mardari

AI agents in manufacturing: from pilot to production

Production-grade AI agents are quietly absorbing the manual coordination tax in factories. Here's the architecture that's actually working.

Alessandro Merola
Alessandro Merola

AI-native automation across CRM, ERP, and IoT

What changes when LLMs and agents sit at the centre of your business systems, not bolted on, but architecturally native.

Daniela Cazac
Daniela Cazac

Adrenaline: building an AI debugger on top of OpenAI

How we partnered with the Adrenaline team to ship an LLM-powered debugger from MVP to AWS-scale production, in months, not years.

Alessandro Merola
Alessandro Merola

Why senior engineers with AI beat junior copilots

AI copilots compress the productivity gap, but they amplify the architectural one. Why we lean on senior-led pods for AI work.

Daniela Cazac
Daniela Cazac

The art of an AI project handover

Handing over an AI product isn't just code and docs. It's evals, prompt versioning, and the operational runbooks that keep models trustworthy.

Nicu Mardari
Nicu Mardari

Running AI engineering pods across continents

How we operate distributed AI teams without losing the tight feedback loops that LLM work demands.

Daniela Cazac
Daniela Cazac

When to switch your AI engineering partner

Five honest signals that your current vendor is holding back your AI roadmap, and how to make a clean transition.

Nicu Mardari
Nicu Mardari

AI engineering outstaffing, done properly

Cost is not the headline. Velocity, ownership, and AI fluency are. How we set up nearshore AI pods that actually ship.

Daniela Cazac
Daniela Cazac

Lessons from London Tech Week: an AI-first lens

What we heard about agentic workflows, evaluation, and AI go-to-market, and what we're betting on next.

Nicu Mardari
Nicu Mardari

Why MVP first? The case for minimum viable products

Starting lean, learning fast, and building what users actually need, the MVP approach is the most efficient path from idea to product-market fit.

Daniela Cazac
Daniela Cazac

WordPress vs. custom development: how to choose

WordPress can launch a web presence in days. Custom development can build a competitive moat that lasts years. Here's how to decide which one your business actually needs.

Daniela Cazac
Daniela Cazac

Osai: revolutionising AI interactions with 7Code

How we built Osai, a prompt-sharing platform that unlocks collective intelligence across teams working with GPT-4, Midjourney, and DALL-E.

Daniela Cazac
Daniela Cazac

Low-code vs. custom development: how to choose

Low-code platforms are powerful for standard needs. Custom code is the only answer when your product demands performance, flexibility, and long-term viability.

Daniela Cazac
Daniela Cazac

Dev cost savings through nearshoring and outstaffing

10–30% cost savings are possible, but that is not the headline. Velocity, continuity, and access to senior talent are.

Daniela Cazac
Daniela Cazac

Migrating users between Auth0 tenants in Python

A practical guide to the pitfalls, edge cases, and Python patterns for moving users from one Auth0 tenant to another, without data loss or downtime.

Alessandro Merola
Alessandro Merola

AI-native logistics: beating the coordination tax

Generic logistics platforms cover 60% of the workflow and force teams to absorb the rest in spreadsheets. AI-native, integrated systems absorb the coordination tax and turn real-time data into faster, better operational decisions.

Daniela Cazac
Daniela Cazac

AWS Beanstalk to ECS Fargate: AI-engineering recipe

AI services need a deploy story Beanstalk cannot give them. A practical recipe for moving a containerised AI workload to ECS on Fargate, with VPC, load balancer, and a GitHub Actions deploy pipeline.

Alessandro Merola
Alessandro Merola

How to Hire Nearshore AI Engineers: A Practical Guide for CTOs

Hiring nearshore AI engineers is faster and cheaper than most CTOs expect — if you evaluate the right signals and avoid the three most common mistakes.

Nicu Mardari
Nicu Mardari

Top 5 Nearshore AI Development Agencies in Europe (2026)

A structured comparison of the leading nearshore AI engineering agencies serving UK and EU clients in 2026, based on delivery track record, team seniority, and AI specialism.

Nicu Mardari
Nicu Mardari
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