Technology Consulting

Technical due diligence is an independent assessment of a software system or technology stack, produced for a decision-maker who needs to understand what they're inheriting or investing in. The most common trigger points: pre-acquisition (what is the technical liability of the company we're buying?); pre-investment (is this startup's technology actually built, or is it held together with duct tape?); pre-rebuild (does this system actually need to be replaced, or can it be extended?); and pre-AI deployment (is this stack ready to integrate with AI agents, or does it need modernisation first?). The output is a written assessment covering architecture, security posture, scalability limits, technical debt inventory, dependency risks, and a prioritised list of findings with estimated remediation effort. ITMTB has conducted technical due diligence for fintech platforms processing millions of transactions, startup technology stacks ahead of funding rounds, and legacy enterprise systems being evaluated for AI-readiness.

A stack migration is a change to the underlying technology that runs a system — examples: Python 2 to Python 3, .NET Framework to .NET 8, Java EE to Spring Boot, monolith to microservices, on-premises to cloud, one cloud provider to another. The process has consistent steps regardless of the specific migration: (1) inventory — map all dependencies, interfaces, and integration points of the current system; (2) risk assessment — identify which components are most likely to behave differently after migration and what the failure modes are; (3) migration runbook — define the exact sequence of changes, the testing approach at each step, and the rollback procedure if a step fails; (4) execution — migrate progressively rather than as a big-bang cutover; (5) validation — test the migrated system against the original behaviour baseline. The biggest migration risk is undocumented behaviour: things the system does that nobody wrote down. We find those during the inventory phase before migration starts.

Performance consulting is an investigation into why a software system is slower, more expensive to run, or less reliable than it should be — and a set of specific, prioritised recommendations for fixing it. It's worth doing when: application response times are degrading as usage grows and the cause isn't obvious; cloud costs are increasing faster than load; a system that worked fine at small scale is becoming unreliable at production load; or you're about to add AI inference to a system and need to understand whether the current infrastructure can handle the additional load. Our performance consulting engagements start with instrumentation — adding the observability needed to diagnose the problem rather than guessing. Common findings: N+1 query problems that only appear under concurrent load, cache invalidation logic that's too aggressive, database indices that cover development data volumes but not production, and synchronous API calls that should be async.

Most of the time, extending is the right answer — and most organisations that rebuild from scratch find themselves 18 months later with a new system that has different problems but is no faster or cheaper. Rebuilding is genuinely warranted when: the existing system uses technology that is no longer maintained (Python 2, .NET Framework with no upgrade path, a vendor whose product is sunset); the architecture makes the system fundamentally unable to meet new requirements (synchronous-only when you need real-time, single-region when you need multi-region); or the cost of adding any new feature is so high due to technical debt that incremental extension is more expensive than rewrite over a 3-year horizon. In all other cases, a strangler-fig extension approach — wrapping the legacy system with a new interface and migrating functionality progressively — delivers the modernisation benefits without the timeline and risk of a full rebuild. We assess which path is appropriate during the discovery sprint, with a 3-year TCO comparison of both options.

Yes — for organisations without a full-time CTO or where the CTO needs a specialist partner for a specific domain (AI deployment, cloud architecture, security), we provide fractional technology advisory. This covers: attending architecture review sessions and providing independent assessment of proposals; reviewing vendor RFPs and evaluating responses; advising on build vs. buy decisions with a documented recommendation; advising on hiring — technical interview panels and role definition for engineering hires; and providing a second opinion before major technology investment decisions. The engagement is structured as a fixed monthly retainer with a defined scope of advisory hours and access to ITMTB's delivery team for specific investigations. Most fractional CTO engagements run 3–6 months during a period of significant technology decision-making.

Pricing follows the engagement model. Discovery sprints are fixed-price for a fixed timebox. Build engagements are scoped after discovery — fixed-scope, time-and-materials, or outcome-linked. Managed services are billed monthly with defined SLAs. Total cost depends on scope, integration complexity, QA depth, and post-launch support — we publish a transparent breakdown for buyers evaluating their options.

Every engagement starts with a fixed-scope, fixed-price discovery sprint — typically 2 weeks. You leave with a written roadmap, risk register, cost estimate, and timeline before committing to a build. We then offer four engagement models: fixed-scope builds, time-and-materials, outcome-linked retainers, and managed services post-launch. If discovery doesn't justify proceeding, you can stop there — no obligation.