0-to-1 Product Development
Ship net-new AI products with typed service boundaries, eval harnesses, and release gates. We replace demo-grade prototypes with versioned APIs, observability, and rollback-safe deploy pipelines.
We build high-dimensional AI architecture for production systems.
First-principles engineering for teams outgrowing fragile prompt wrappers. We design deterministic control planes, typed tool interfaces, and auto-scaling inference layers so your AI stack behaves like enterprise infrastructure—not a chat experiment.
systems_approach
Execution areas engineered for production load.
Custom LLM Infrastructure
Design inference stacks with routing, caching, and cost controls. Fine-tune and distill models where it matters; enforce SLOs with queueing, batching, and hardware-aware autoscaling.
Agentic Workflow Automation
Orchestrate multi-step agents with explicit state machines, tool contracts, and human-in-the-loop checkpoints. Failures surface as structured events—not opaque stack traces in chat logs.
live_engine_status
Telemetry report — simulated production signals
status: operational
isometric-engine / telemetry / us-east-2refreshed: 2026-05-20T14:32:08Z
Latency Reduction−38.4%
p99 vs baselineoptimized
Token Efficiency2.17×
output / inputstable
Pipeline Throughput14.2k
req/minnominal
Eval Pass Rate97.8%
regression suitenominal
Cache Hit Ratio64.1%
semantic cachestable
Error Budget92.3%
remaining (30d)healthy
stream.logtail -f
- [engine] routing_policy=v2.4 shard=replica-us-east-2
- [inference] batch_size=32 queue_depth=12 latency_p99=41ms
- [eval] suite=production_regression passed=412 failed=9
- [cost] token_spend_usd=1247.33 budget_utilization=0.61
- [agents] workflow=invoice_reconciliation state=awaiting_approval