Technology Decisions That Affect Growth, Operations, and Enterprise Value
Practical insights on technical due diligence, operational transformation, software architecture, AI, and technology leadership.
The areas we work in
Start with the kind of decision or operating problem you are trying to understand.
Investment & Due Diligence
Technology risk, operating reality, and the evidence behind investment decisions.
Explore topicOperational Transformation
How critical business flows can be understood and improved before technology is added.
Explore topicArchitecture & Modernization
Practical choices for systems that need to become more reliable, maintainable, or ready to scale.
Explore topicAI & Automation
Where automation and AI fit, and where a simpler operating change is the better answer.
Explore topicTechnology Leadership
The leadership, decisions, and delivery conditions behind sustainable technical progress.
Explore topicA current perspective from Sharp Logica
The 30-Minute Task That Takes Three Days: Where Operational Time Actually Goes
A task that takes 30 minutes to complete can still take three days to reach an outcome. The difference is usually found in waiting, handoffs, missing information, rework, and exception handling across the wider business flow.
Why ATS Filters Aren't Enough for Hiring Decisions
Applicant Tracking Systems (ATS) were supposed to make hiring simpler. They collect resumes, store candidates, and apply keyword filters so overwhelmed HR teams can quickly shortlist people who “match” the job description. The problem is that matching keywords is not the same as making good hiring decisions.
Read Article5 Signs Resume Screening Is Burning Out Your HR Team
Resume screening is one of those tasks everyone agrees is important… and almost nobody actually enjoys doing. When it goes well, your team feels like a strategic partner to the business. When it goes badly, it turns into late evenings, rushed decisions, and a nagging fear that the best candidates slipped through the cracks.
Read ArticleSurviving LLM Rate Limits: Building Backpressure
Once you move beyond a toy demo and start running real workloads on top of a large language model, rate limits stop being a theoretical concern and become a very practical constraint. At small scale you can mostly ignore them. At medium scale you start seeing occasional 429 errors and retriable failures. At larger scale your whole system can suddenly feel brittle: bursts of errors, retries piling up, and users waiting far longer than they should.
Read ArticleBuilding Reliable AI Pipelines on Azure
Modern AI systems fail more often than most engineers expect. Not because the models are fragile, but because the infrastructure surrounding them is. Network latency, cold starts, concurrency spikes, and the notorious 429 rate-limit errors all come into play.
Anyone who builds LLM-powered systems quickly learns the same lesson: in production, retries are not optional, they're architecture.
How to Avoid Early-Stage Tech Debt in Your Startup
In the early days of a startup, speed is everything. Getting a working product to market quickly often feels like the only priority that matters. Founders race against time and funding, building fast, cutting corners, and stacking features to satisfy users or investors. And while this velocity may win you early traction, it can silently lay the foundation for a trap: early-stage technical debt.
Read ArticleMicroservices vs Monoliths: Right Choice for 2026
Few engineering debates stir up more passion than the question of microservices versus monoliths. It’s been a hot topic for over a decade, with architects, developers, and CTOs drawing battle lines over modularity, scalability, team autonomy, and operational complexity.
Read ArticleNeed a clearer view of a technology or operational problem?
Sharp Logica works with investors and operating companies on technical diligence, operational transformation, architecture, and technology leadership.
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