What we build for you.
We don't do discovery calls or free consultations. We do work. Every engagement is scoped, priced, and delivered — no retainer theater.
Eliminate your cloud AI bill. Own your inference.
Most companies are paying $5K-50K/month for AI inference that they could run on hardware they own, at a fraction of the cost. We deploy on-premise LLM systems that are API-compatible drop-in replacements — your applications don't change, your bill does.
We build multi-engine architectures so you're never locked to one model or one provider. Swap models without rewriting application code. Scale to multiple machines without redesigning your stack.
"Reduced AI inference costs from $18K/month to $0/month"
Consumer-grade GPU server, open-source models, on-premise deployment
- ✓ OpenAI-compatible inference gateway
- ✓ Multi-engine routing (swap models without app changes)
- ✓ GPU optimization for maximum throughput
- ✓ VRAM budget management for mixed workloads
- ✓ Monitoring and alerting integration
- ✓ Runbooks and documentation
Our own stack runs on consumer hardware at $0/month cloud cost. Every model we use is open-source. Every service we run is on-premise. See how we operate →
"System detects and recovers from failures before on-call is paged"
Tiered watchdog with automated remediation and escalation only when truly needed
- ✓ Core watchdog (rules-based, no LLM in critical path)
- ✓ Pattern detection for anomalies and degradation
- ✓ Automated remediation playbooks
- ✓ LLM escalation for edge cases only
- ✓ Accountability logging for every automated action
- ✓ Integration with your existing alerting stack
Sentinel — our own watchdog system — monitors our entire operation with 5-minute resolution, self-heals automatically, and has operated without human intervention since deployment. See how we operate →
Systems that heal themselves. Ops teams that sleep.
The best on-call rotation is one that never gets called. We build self-monitoring infrastructure with automated remediation — tiered so that simple problems are handled by rules, complex patterns by ML detection, and truly novel failures escalated to humans.
The critical principle: no LLM in the critical path. Autonomous systems that depend on inference latency for recovery will fail when you need them most. We architect reliability first.
ML systems that make decisions and act on them.
Most ML projects produce notebooks. We produce systems. There's a significant gap between a model that works in a Jupyter notebook and a pipeline that ingests live data, detects regime changes, generates signals, and executes actions — reliably, daily, without you.
We build the entire pipeline: data engineering, feature design, model selection (with evidence), experiment tracking, deployment, and automated retraining. You get a system, not a model file.
"Prediction pipeline running daily with automated retraining and zero manual intervention"
End-to-end system from raw data to actionable signal
- ✓ Data pipeline from source to feature store
- ✓ Model comparison framework (A/B with evidence)
- ✓ Reproducible experiment registry
- ✓ Automated retraining and model promotion
- ✓ Monitoring for model drift and data quality
- ✓ Decision/action integration layer
ShadowQuant runs multi-asset, multi-timeframe ML pipelines across 36 years of market data — in production, daily, with automated execution. See the case study →
Not sure which fits your problem?
Tell us what you're trying to solve. We'll tell you how we'd approach it — and whether we're the right fit.
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