Executive Summary (TL;DR) Generic large-language-model (LLM) sessions forget everything at the end of each prompt, forcing users to retransmit context and burning tokens.¹ Personalization layers exist today via Retrieval-Augmented Generation (RAG), which queries an external vector database on every turn—incurring ≥ 200 ms network latency and extra cost per call.² Cache-Augmented Generation (CAG) stores high-value […]
Executive Summary (TL;DR) Poor contracting siphons ≈ 9 % of annual revenue from companies worldwide, mostly via missed obligations and hidden risk. Corporate counsel now face review fees ranging $250–$350 per hour—spikes to $2,500 at elite firms—just to keep up. The AI Corporate Contract Analyzer ingests vendor, employment, and partnership agreements, flags risky clauses, and […]
LAST UPDATE DATE: Mar 19, 2025 Executive Summary (TL;DR) Inventory distortion—out-of-stocks and overstocks—bleeds $1.77 trillion from global retailers every year. In North America alone, out-of-stock items siphon 5.9 % of grocery sales and fuel costly brand erosion (NielsenIQ OSA Benchmark, 2021). The AI Inventory Replenishment Advisor embeds in merchandising systems, predicts demand for perishables and […]
Executive Summary (TL;DR) U.S. hospitals spend $19.7 billion a year on administrative appeals of denied claims (OIG 2023), while roughly 15 % of all initial claims are rejected (STAT 2024). Those denials jeopardise ≈ 3.3 % of net patient revenue (≈ $4.9 million per 250-bed facility). The AI Billing-Code Optimizer plugs into the Electronic Health […]
Financial institutions across the United States and Canada channel $61 billion each year into financial-crime and regulatory-compliance programs, yet non-compliance fines still land at 2.7× the cost of simply staying compliant (USA $42 B, Canada $19 B).¹ ²
The AI Financial Contract Compliance Checker steps in as a SaaS gatekeeper: it ingests loan agreements and investment contracts, flags clauses that violate Securities and Exchange Commission (SEC), Office of the Comptroller of the Currency (OCC), or Anti-Money Laundering (AML) rules, and recommends fixes before execution. Early pilots point to a 20–30 % cut in contract-review hours and a 15 % drop in regulatory findings, achieving ROI within 9–12 months.
A high-discipline execution framework for prompt and agent engineering, aligned with the IMPACT AI Product Management framework. The Impact Prompt + Agent Engineering Framework empowers AI teams to operationalize both prompt workflows and lightweight agent behaviors the way product and ML teams structure intelligent systems — with intent, metrics, and repeatability. Built for LLM-native teams deploying production-grade outputs. This framework is comprised of 8 structured stages that guide you from goal identification to agent-integrated, production-grade engineering. This is the operations layer for LLM-native systems. Build with intention. Optimize with discipline. Ship with clarity.Iterate, fork, or phase-out stale agents/prompts based on lifespan and metric performance
AI product development is breaking the boundaries of traditional software product frameworks. While most AI strategies stall in experimentation or scatter in execution, IMPACT AI PM Framework delivers a structured, repeatable system to drive alignment from model to market. Designed for product managers, AI leads, and cross-functional teams, it turns abstract ML ambition into shipped, measurable outcomes. From intelligent use case discovery to adaptive MLOps, it drives the full cycle from idea to infrastructure. This is the operating system for building AI products that don’t just launch — they lead.
Modern technology projects suffer from familiar failure patterns: missed deadlines, unclear scope, fragmented communication, and stakeholder disappointment. These aren’t issues of incompetence — they stem from misalignment, mis-scoping, and lack of shared truth.
The IMPACT Technical Project Management framework restores clarity, alignment, and trust to how work moves from vision to launch. Built for TPMs, engineers, delivery leads, and stakeholder-heavy teams, it turns complexity into velocity without bureaucracy. This is not a theoretical model. It’s a practical execution architecture forged from real-world scars — and ready for immediate deployment.
Modern AI workflows are breaking the seams of traditional DevOps. While most MLOps frameworks either overfit to academic rigor or collapse under organizational friction, the IMPACT Vertex AI MLOps frameworkis engineered as a pragmatic execution system: scalable, composable, and deeply integrated with Google Cloud’s Vertex AI ecosystem. It exists to answer a clear need: a repeatable, production-ready framework that empowers product managers, data scientists, ML engineers, and platform leads to build intelligent systems on GCP Vertex AI that not only ship — but evolve.