An HR tech company processing 2 million resumes per month needed 94% field accuracy across 47 languages. Here is how to build resume parsing that actually works on messy real-world CVs.
A financial services firm automated 78% of their monthly content production while maintaining brand compliance and reducing time-to-publish from 12 days to 2 days. Here is how to build content generation systems enterprises actually trust.
A SaaS company knew their churn rate was 18 percent annually but could not predict when specific customers would leave. Survival analysis gave them a 90-day early warning system that saved $2.1 million in ARR.
AI inference traffic is fundamentally different from web traffic, variable request sizes, GPU-bound processing, and model-specific routing create challenges that standard load balancers cannot handle.
A manufacturing client had 14 million sensor readings but only 2,000 labeled failure events. Traditional supervised learning could not work. Self-supervised pre-training unlocked the value of all that unlabeled data.
A commercial real estate firm was spending $4,200 per property on manual appraisals. Our AI valuation system reduced the cost to $340 per property while matching appraiser accuracy within 3 percent.
A community health system reduced ER wait times by 34% and improved triage accuracy by 28% using AI-assisted clinical decision support. Here is how to deliver triage systems that clinicians actually trust.
A streaming platform with 85,000 video assets had no consistent metadata. Discovery was broken, recommendations were useless, and content was invisible. Here is how we fixed it with AI.
A regional hospital network needed to extract structured data from 2.3 million unstructured clinical notes. Here is exactly how we scoped, built, and delivered the NLP system, and how your agency can do the same.
A B2B software company's sales team was researching prospects manually, 45 minutes per account. Our AI sales intelligence platform cut that to 3 minutes with richer, more actionable insights.
Models degrade silently. By the time someone notices, the damage is done. Here is how to deliver monitoring platforms that catch problems before they cost your client millions.
A private equity firm needed to analyze 3,400 portfolio company documents monthly. Manual review was consuming 240 analyst hours. Here is how we built the NLP system that cut it to 35.
The data lakehouse is replacing both the data warehouse and the data lake for AI-forward organizations. Here is exactly how your agency delivers lakehouse projects that succeed.
A fraud detection system deployed for a fintech client started at 96 percent accuracy. Six months later, it was at 81 percent. Fraudsters had adapted. We built a continual learning system that maintains 93+ percent accuracy indefinitely.
A Fortune 500 company with 147 ML models in production had no centralized visibility into model risk. A governance platform reduced model-related incidents by 73% while cutting model approval time from 8 weeks to 12 days.
A regional insurer cut claims processing time from 14 days to 36 hours using AI automation. Here is the complete playbook for building claims processing systems that insurers actually trust.
Under-provisioned AI infrastructure means missed SLAs and lost revenue. Over-provisioned means wasted budget. Here is how to deliver capacity planning that hits the sweet spot and keeps clients running smoothly as they scale.
Deploying a new AI model to 100 percent of traffic is a gamble. Canary deployments let you test with 5 percent of traffic first. Here is the complete framework for canary deployments that catch problems before they become disasters.
When your client's AI systems are documented in scattered wikis, outdated Confluence pages, and tribal knowledge, every team change is a crisis. Here is how to deliver documentation platforms that keep AI systems maintainable.
Data scientists spend 30 percent of their time just finding and understanding data. A well-delivered data catalog eliminates that waste and unlocks AI use cases that were impossible without data discovery.
One unsafe AI output can destroy years of trust and millions in brand value. Here is how your agency delivers safety testing platforms that catch dangerous behaviors before they reach production.
When a mid-market e-commerce client asks you to fix their terrible site search, you need more than a vector database. Here is how to deliver AI search ranking systems that actually move revenue.
In regulated industries, non-compliant AI is a ticking time bomb. Here is how your agency builds AI architectures where compliance is baked in from day one, not bolted on as an afterthought.
AI systems introduce attack surfaces that traditional security does not address, adversarial inputs, model theft, training data poisoning, and prompt injection. Here is how to deliver security architectures that protect AI systems end to end.
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