Real Results. Real Impact.

Challenge: Healthcare providers face increasing operational pressure due to fragmented systems and time-consuming manual documentation. Clinicians are burdened by subjective wound measurement and disconnected workflows, which reduce patient interaction time and lead to inconsistent records, inter-rater variability, compliance gaps, and clinician burnout.

Challenge: Rehabilitation and post-acute care delivery are often fragmented, with disconnected systems, manual vitals tracking, and unstructured workflows creating operational inefficiencies. These challenges lead to inconsistent care coordination and variable patient outcomes across the rehabilitation lifecycle.

Challenge: Financial institutions and verification teams faced operational inefficiencies due to fragmented systems and manual workflows. Loan intake required extensive manual data entry, field verification lacked standardization and auditability, and report generation delays impacted turnaround times. Limited visibility into field executive activities reduced accountability and SLA tracking, while manual billing processes increased reconciliation errors and operational overhead.

Challenge: Modern consumers face information overload and limited transparency when evaluating sustainable products and ethical brands. ESG insights are often fragmented across multiple sources, making it difficult to understand the environmental and governance impact behind products and parent companies. Traditional scoring models lack personalization and fail to reflect individual values, while real-time analysis is needed to deliver meaningful insights during live shopping experiences.

Challenge: Patients with kidney health issues often face overwhelming amounts of technical medical information, making it difficult to find clear and trustworthy answers. Traditional healthcare resources lack accessible, conversational support, and there is a growing need for systems that can accurately interpret user intent and provide relevant, contextual guidance about kidney-related conditions and treatments.

Challenge: Our client, a mid-size financial services firm, relied on their analysts to manually scan hundreds of news articles each morning before markets opened. Analysts would read through headlines, skim articles, and try to gauge whether coverage of key holdings was positive, negative, or neutral. This process was slow, inconsistent, and heavily influenced by individual bias. Two analysts reading the same article would often come away with different sentiment readings. The firm needed a way to process news at scale with consistent, quantifiable sentiment scores that could feed directly into their trading models and risk dashboards.

Challenge: A government agency responsible for managing criminal justice records was struggling with a massive backlog of unclassified documents. Their records system contained millions of entries spanning decades, including arrest reports, court filings, sentencing documents, and parole records. Each document needed to be classified by offense type, severity, and jurisdiction to support searchability, compliance reporting, and inter-agency data sharing. The problem was that only a small fraction of the records (roughly 5%) had been manually labeled by staff over the years. Hiring a team to label the remaining millions of records would have taken years and cost millions of dollars. Traditional supervised learning approaches could not achieve acceptable accuracy when trained on such a small labeled subset, and the agency could not afford to wait for a larger labeled dataset.

Challenge: The Government of Andhra Pradesh managed IT assets across 15,000+ offices statewide. Asset tracking was handled through a patchwork of spreadsheets, paper forms, and email threads. When audit time came around, staff spent weeks manually reconciling records, visiting locations to verify equipment, and chasing down discrepancies. Assets would go missing, warranties would expire without anyone noticing, and procurement decisions were based on outdated or incomplete data. The organizations needed a centralized system that could give leadership real-time visibility into what they owned, where it was, and what condition it was in.

Challenge: Our maritime client operated a ship pooling arrangement where multiple vessel owners share revenues based on each ship's performance and contribution to the pool. Calculating and sharing these results required gathering data from multiple sources: voyage reports, fuel consumption logs, port records, and market rate feeds. The existing process was manual. Analysts would compile data into spreadsheets, run formulas, and distribute results via email, often days or even weeks after the fact. Pool participants had no way to see how their vessels were performing in real time, and disputes over calculations were common because the methodology was opaque. The delay in sharing results also slowed decision-making about vessel deployment and commercial strategy.

Challenge: When the Paycheck Protection Program launched, CRFG was flooded with loan applications almost overnight. Their existing loan processing workflow was designed for a fraction of the volume they were now facing. Each application required extracting data from submitted documents (tax returns, payroll records, bank statements), validating the information against program rules, and preparing the loan package for SBA submission. Staff were working around the clock, manually entering data and cross-checking figures. Applications were piling up, and borrowers who desperately needed funds were waiting weeks for processing. CRFG needed to dramatically accelerate throughput without sacrificing accuracy, because SBA compliance requirements meant that errors could result in rejected applications or clawbacks.

Challenge: The organization operated across multiple departments and regional offices, each with its own cadence of governance meetings, reviews, and check-ins. Leadership had established a set of required governance rhythms (weekly stand-ups, monthly reviews, quarterly planning sessions), but there was no centralized way to track whether these were actually happening. Department heads would self-report compliance, but the data was inconsistent and often late. When rhythms slipped, problems would go unnoticed until they became crises. Leadership needed a tool that could enforce accountability without adding administrative burden to already-stretched teams.

Challenge: Chaikin Analytics, a MarketWise subsidiary serving hundreds of thousands of retail investors, was running critical financial analytics infrastructure in an aging data center. Performance limitations, scaling constraints, and the operational burden of managing on-premises hardware were threatening the company's ability to serve its growing user base. The team needed a partner who could migrate their entire stack to AWS without disrupting the real-time analytics products their customers depended on daily.