Straightforward guidance on AI, SaaS architecture, cloud platforms and reliable software delivery, from the team that builds them.
28 articles
Latest articleDelivery
How to work well with an offshore or nearshore team in another time zone: which hours to protect, how to write updates and handoffs, and which meetings are worth keeping.
A first-month plan for onboarding a new development partner: access and accounts, environments, documentation, the first sprint, the meeting rhythm and early warning signs.
What sprints, planning, demos, retrospectives and the backlog look like when you hire an outside team, how much time the product owner needs and how to give feedback the team can act on.
Why software estimates go wrong, and how ranges, discovery, smaller pieces of work, honest buffers and regular re-estimates turn them into something you can plan around.
Why scope creep happens, a simple change-request process you can copy, and how to trade scope, time and budget without derailing the project.
The sections a software statement of work needs, from scope and acceptance criteria to change control, IP and payment terms, with a checklist for reviewing a draft.
How fixed price, time and materials and a monthly team fee differ, who carries the risk in each, and how to combine them on one project.
A one-page weekly status report template for software projects, and how to back it with demos, a RAID log, honest RAG status and a clear escalation path.
What a software testing strategy should set out: test levels, what to automate, environments and test data, regression, release criteria and who owns quality.
The DORA metrics in plain words, three business measures to add, how the numbers get gamed and a one-page monthly dashboard for leaders who don't write code.
A practical checklist for keeping your code, data and ideas safe with an outside development team: NDAs, IP assignment, repositories you own, access control, secrets and open-source licences.
What to hand over when a software project changes hands, how to run shadowing and reverse shadowing, how long to keep support in place and the signs that a handover isn't finished.
For SaaS and software teams: how to choose AI features worth building, design them so users trust them, test their quality and keep model costs under control.
How a custom software project runs from discovery to launch, what makes it cost more or less, and how to decide between building and buying.
The data model, caching, queue, observability and testing practices that let software handle more users, more data and a bigger team without a rewrite.
How a mid-size company can replace manual work and disconnected tools one process at a time, with clear owners and results you can measure.
Who is in a managed squad, what a normal week looks like, how reporting and the flat monthly fee work, and how to get one started.
How the three main ways to add outside engineers differ, when each one fits, and the questions to ask a vendor before you sign.
Seven habits that help product teams ship useful software often: discovery before code, small releases, clear metrics and quality in every sprint.
A practical checklist for a growing company's website or web app, covering speed, SEO basics, accessibility, security, hosting and analytics.
Which security checks belong at each stage of a delivery pipeline, how to keep them fast, and how to split the work between developers, platform and security teams.
How to coordinate AI agents, business systems and people in one workflow, with approval points, limited tool access, audit logs and a plan for when a step fails.
A step-by-step way to estimate the return on a cloud modernisation, with a worked example in round numbers and the costs that are easiest to miss.
How to choose a first AI use case, put a number on its value, plan for the costs that are easy to miss, and decide whether to build, buy or bring in a partner.
A practical sequence for taking an AI use case from first test to live system: data, security, evaluation, rollout and monitoring, plus the usual reasons pilots stall.
Where enterprise AI tends to pay off first in operations, finance, customer service, HR and sales, and which decisions should stay with people.
A side-by-side comparison of microservices and monolithic architecture, the signs that it is time to split, and how to migrate without a rewrite.
Enterprise AI is AI built into a company's own systems, data and processes, with rules for who sees what. Here is how it differs from consumer AI tools and what it needs to work.
The Coretus blog covers practical software engineering: enterprise AI and AI agents, SaaS and product development, cloud modernisation and DevOps, software architecture and security. Each article explains one decision or method in plain English, with steps you can apply and links to related services and published case studies.
It is written for founders, CTOs, product owners and engineering leads who need to make a build decision, plan a migration or add AI to a product. We assume you know your business well but not every technical term, so articles explain the trade-offs before the jargon.
Each article is written by a member of the Coretus leadership team, based on our project work since 2013. Sandeep Thesiya, our Full-Stack AI Engineer, writes about AI, engineering and cloud. Anil Kamani, our Chief Engineer, writes about strategy. Ravi Dadhaniya, our Chief of Design, writes about running projects and delivery.
Ask an engineer. We'll give you a straight answer, even if it's not a project for us.