Technology Decisions That Affect Growth, Operations, and Enterprise Value
Practical insights on technical due diligence, operational transformation, software architecture, AI, and technology leadership.
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How We Approach Operational Transformation
How We Approach Operational Transformation
Companies are under growing pressure to automate and adopt AI, but technology is rarely the best place to start.
The first step is understanding how work actually moves through the business, where time and value are being lost, and which parts of the process should change before any technology is introduced.
AI-Powered Processing of 500+ Page Bid Packs at Scale (3/4)
Big organizations drown in documents: 500-plus-page PDFs, scanned annexes, tables, and forms that arrive not once, but all the time. Shoving entire files into an LLM is slow, expensive, and hard to defend. This post shows a better way with AI: extract a small, testable catalog of requirements, index the documents locally, retrieve only the few passages that matter, and demand verbatim, page-linked evidence.
We use procurement as the running example, but the same pattern applies anywhere you process large volumes of pages: vendor risk and security due diligence, contract and policy review, healthcare and regulatory dossiers, M&A data rooms, insurance claims, ESG reports, and more.
AI-Powered Processing of 500+ Page Bid Packs at Scale (2/4)
Big organizations drown in documents: 500-plus-page PDFs, scanned annexes, tables, and forms that arrive not once, but all the time. Shoving entire files into an LLM is slow, expensive, and hard to defend. This post shows a better way with AI: extract a small, testable catalog of requirements, index the documents locally, retrieve only the few passages that matter, and demand verbatim, page-linked evidence.
We use procurement as the running example, but the same pattern applies anywhere you process large volumes of pages: vendor risk and security due diligence, contract and policy review, healthcare and regulatory dossiers, M&A data rooms, insurance claims, ESG reports, and more.
AI-Powered Processing of 500+ Page Bid Packs at Scale (1/4)
Big organizations drown in documents: 500-plus-page PDFs, scanned annexes, tables, and forms that arrive not once, but all the time. Shoving entire files into an LLM is slow, expensive, and hard to defend. This post shows a better way with AI: extract a small, testable catalog of requirements, index the documents locally, retrieve only the few passages that matter, and demand verbatim, page-linked evidence.
We use procurement as the running example, but the same pattern applies anywhere you process large volumes of pages: vendor risk and security due diligence, contract and policy review, healthcare and regulatory dossiers, M&A data rooms, insurance claims, ESG reports, and more.
Stop Reading Every Resume: Focus on Right Candidates
You already have an ATS. You already use job boards. You already spend too many evenings skimming resumes that all start to look the same after a while. The real problem isn’t getting more candidates into the funnel. The real problem is deciding who deserves your attention without burning out.
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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.
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5 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.
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Sharp Logica works with investors and operating companies on technical diligence, operational transformation, architecture, and technology leadership.
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