AI Automation Pipelines
We design and deploy end-to-end automation pipelines that replace manual decision workflows. From data ingestion through feature engineering to real-time inference — fully managed, fully observable.
- Real-time streaming inference (< 200ms p99)
- Auto-scaling compute with zero-downtime deploys
- Full audit trail and explainability layer
- Integrates with your existing data warehouse
Data Intelligence Layer
Transform raw event streams into structured intelligence. Our semantic enrichment layer adds context, entity resolution, and anomaly signals to every record before it reaches your models.
- Entity resolution across 50+ data sources
- Semantic enrichment with domain ontologies
- Anomaly detection with < 0.3% false positive rate
- Schema evolution without pipeline restarts
ML Pipeline Engineering
We build and operate the training, evaluation, and deployment infrastructure your models need to stay accurate as data distributions shift. No MLOps debt, no stale models.
- Automated retraining triggers on drift detection
- Shadow deployment and A/B testing framework
- Model registry with lineage tracking
- Champion-challenger routing at inference time
Decision Engine API
A production-grade API layer that takes your model outputs and turns them into structured, auditable decisions. Built for compliance-heavy industries where every choice needs justification.
- Decision API with sub-50ms cold-start latency
- Rule override layer for compliance teams
- Structured decision payloads with confidence scores
- GDPR / HIPAA compliant audit logging
How We Work
From audit to
production in 30 days.
Our deployment methodology is designed to eliminate risk at every phase. Shadow mode validation means you never flip a switch without confidence.
Pipeline Audit
48 hoursWe spend 48 hours mapping your current data flows, identifying latency bottlenecks, and quantifying the cost of manual touchpoints.
Architecture Design
1 weekOur engineers design the target-state pipeline architecture — including compute topology, data contracts, and SLA definitions.
Staged Deployment
2–4 weeksWe deploy in shadow mode first, running parallel to your existing system until confidence thresholds are met. Zero risk cutover.
Continuous Optimization
OngoingPost-launch, our platform monitors drift, triggers retraining, and surfaces optimization opportunities automatically.
Typical time to value: 30 days
Our fastest deployment was 11 days from kickoff to production. The average across all clients is 28 days. We don't bill until you're live.
Avg. deployment
Teams deployed
Everything Included
One platform.
No integration debt.
Every service tier includes our full platform — no feature gating, no add-on modules. You get the complete stack from day one.
Schedule Architecture Review