From generative AI and autonomous agents to predictive analytics and computer vision, we help you separate genuine capability from hype and ship AI that holds up in production.
We build retrieval-augmented generation (RAG) pipelines and LLM-powered applications grounded in your enterprise knowledge — policy documents, claims, tickets, contracts — so answers are accurate, cited and current.
Index enterprise documents into a governed vector store.
Pull the most relevant, permissioned context per query.
Produce grounded, cited answers via your chosen LLM.
Log, evaluate and monitor every response for quality & safety.
Beyond chat: autonomous, tool-using agents that plan multi-step tasks, call internal systems and APIs, and escalate to humans when confidence is low — automating case triage, claims processing and back-office workflows.
Agents decompose goals into ordered, verifiable steps.
Secure connectors into CRMs, cores and internal APIs.
Policy checks and approval gates before high-risk actions.
Full trace of every agent decision, for audit and debugging.
Forecasting, propensity, churn and risk models — paired with prescriptive optimization that recommends the next best action, not just the next best guess.
Illustrative benchmarks from ProtoGene analytics deployments; actuals vary by engagement.
Image and video models for quality inspection, safety monitoring, and automated extraction from scanned claims, invoices and identity documents.
Extract structured data from unstructured documents and images.
Real-time defect and anomaly detection on production lines.
Classic and transformer-based NLP for the language-heavy processes every enterprise runs on — tickets, calls, complaints and contracts.
Auto-route and prioritize inbound requests by intent and urgency.
Extract clauses, obligations and risk flags from legal documents.
Production AI needs the same engineering discipline as production software: versioning, CI/CD, monitoring and rollback — for both traditional models and LLMs.
Reproducible training runs with full lineage.
Automated CI/CD into staging and production.
Track drift, latency and quality continuously.
Trigger retraining and rollback automatically when needed.
Every model we ship comes with the explainability, bias testing and audit trail needed to satisfy regulators, risk teams and your customers.
Every prediction traceable to the features that drove it.
Documentation aligned to model risk management standards.
Public LLM APIs aren't an option when your data is regulated, sensitive or simply too valuable to leave your perimeter. We deploy open-weight and licensed models on infrastructure you own — cloud VPC, on-prem, or air-gapped — and fine-tune them on your own content.
Nothing leaves your environment. Full control over storage, logging and retention.
Models adapted to your terminology, products and regulatory context.
Fixed infrastructure economics instead of unpredictable per-token billing at scale.
Role-based access, encryption in transit & at rest, and full audit logging.
Audit your data readiness, use cases and risk posture to prioritize the highest-value AI opportunities.
Stand up a production-grade pilot in weeks, on real data, with clear success metrics.
Harden the pipeline, add MLOps/LLMOps discipline, and roll out across teams or geographies.
Institutionalize monitoring, explainability and compliance so AI keeps performing as it scales.
Let's scope a pilot — from generative AI assistants to predictive risk models.