Ship production AI with the engineers who have delivered document automation, voice intelligence, and recommendation systems for companies ranging from regulated enterprises to high-growth consumer brands.
Schedule a Strategy SessionBoards, customers, and competitors expect AI-native products. Yet most proof-of-concepts stall because data is messy, edge cases matter, and deployment is risky.
We embed senior ML engineers and architects alongside your product and data teams to move from idea to production impact. That begins with data consolidation, data clarity, and lakehouse-ready architecture, so every experiment builds on trusted signal. In 6-10 weeks we ship a launch-ready model, real-world evaluation, and an enablement plan your business can trust.
From underwriting to digital experiences, we build the models that move revenue, reduce risk, and unlock scale.
Segment, extract, and validate high-volume documents such as loan tapes, policies, or onboarding packs with human-in-the-loop controls.
Transcribe and analyze conversations to surface risk, coaching opportunities, and customer sentiment in near real time.
Detect defects, anomalies, or unsafe conditions across images, video, and sensor data to improve quality and compliance.
Ground large language models in your knowledge base with retrieval, guardrails, and evaluation frameworks that keep answers trustworthy.
Predict demand, recommend next-best actions, and optimize pricing or churn strategies with interpretable models.
Detect anomalies, fraud, and policy breaches with models that integrate into your governance workflows.
Rebuilt document AI pipeline ingesting 5,000-page loan tapes.
Automated financial statement analysis with multimodal RAG.
AI-driven lead intelligence engine for revenue teams.
Lakehouse design, canonical models, and quality gates ensure data clarity before models ever hit production.
A no-cost discovery sprint confirms ROI, data readiness, and governance so you invest with confidence.
Model documentation, compliance reviews, and fallback playbooks keep security and audit teams on side.
Monitoring, alerting, and milestone-based billing mean you see results—and approve each step—before we invoice.
A transparent, co-creative process keeps business stakeholders, data teams, and compliance aligned.
Clarify the business objective, define success metrics, and map data sources with your domain experts.
Audit data quality, label gaps, and technical constraints. Create a delivery plan and governance checklist.
Prototype, evaluate, and harden models using your data, with automated tests and human reviews for edge cases.
Deploy in your cloud, integrate with downstream systems, enable teams with runbooks, and iterate as metrics evolve.
We blend open-source frameworks with managed cloud services so your models move from prototype to production without friction.
Let's discuss your AI/ML needs and explore how we can help you achieve your business goals
Schedule an AI/ML Strategy Call