An insurance company processing 18,000 claims documents monthly had a 12-day average processing time. Our AI document workflow cut it to 2.8 days and eliminated 73 percent of manual data entry.
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A SaaS company with 2.3 million lines of code had documentation that was 18 months out of date. Our AI documentation system brought it current in 3 weeks and keeps it updated automatically.
When your client's AI systems are documented in scattered wikis, outdated Confluence pages, and tribal knowledge, every team change is a crisis.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.
A SaaS company built a churn prediction pipeline that identified at-risk accounts 45 days before cancellation, saving $2.3 million in annual revenue. Here is how to deliver predictive analytics that actually ships.
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.
Shipping AI without systematic evaluation is like launching a bridge without load testing. Here is how your agency builds evaluation harnesses that give clients confidence in their AI systems.
A B2B distributor added AI recommendations to their ordering portal and saw a 23% increase in average order value within 60 days. Here is how to build recommendation engines that actually move revenue.
A regional delivery company serving 3,400 stops per day cut fuel costs by 22% and reduced late deliveries by 67% with AI route optimization. Here is how to build it.
Without systematic experimentation, AI teams fly blind — guessing at improvements instead of proving them. Here is how to deliver experimentation platforms that accelerate AI development 3x.
A healthcare payer combined AI document understanding with their existing RPA and increased straight-through processing from 34% to 78%, saving $4.1 million annually. Here is how to make RPA actually intelligent.
Every enterprise wants RAG. Few get it right. Here is the complete blueprint for delivering retrieval-augmented generation systems that actually work in production — not just in demos.
When every team in the enterprise is writing their own prompts with no versioning, no testing, and no governance, chaos is inevitable. Here is how to deliver a platform that brings engineering discipline to prompt management.
A 340-store specialty retailer was losing $12 million annually to markdowns caused by bad demand forecasts. Our AI analytics system cut markdown losses by 34 percent in the first season.
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 B2B SaaS company's sales team was chasing 4,800 leads equally. Our propensity model scored every lead, and the top 20 percent converted at 5x the rate of the bottom 80 percent.
Responsible AI is moving from aspirational to mandatory. Here is how your agency delivers the tooling platforms that let enterprises operationalize AI ethics — and why this service will define the next decade of AI consulting.
A warehouse robotics company used RL to optimize pick-and-pack sequencing and increased throughput by 31% over their hand-tuned heuristics. Here is how to deliver RL solutions that actually work in production.
Every enterprise deploying multiple AI models needs a gateway layer. Here is how your agency builds AI gateways that control cost, enforce governance, and give clients a single pane of glass over their AI infrastructure.
A logistics company replaced their 24-hour-old reports with an AI-powered real-time dashboard and caught a $340,000 routing error within 8 minutes of it happening. Here is how to build dashboards that actually drive decisions.
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.
Your client's model is accurate. Now it needs to serve 10,000 predictions per second at under 50ms latency without bankrupting the company. Here is the complete inference optimization playbook.
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.
Most AI maturity assessments collect dust in a drawer. Here is the complete framework for delivering assessments that drive real transformation — and generate six-figure follow-on engagements.
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