Implementation Engineering

Production-Grade AI Engineering.

Bridging the gap between experimental models and the SLAs your business actually runs on. We design, build, and operate AI systems that hold up under load.

LATENCY_P99 42ms across deployed services
UPTIME_SLA 99.99% three-9s, every quarter
deploy.log
→ rolling out v2.1.4 to prod
→ canary: 5% → 25% → 100%
→ p99 latency: 38ms ✓
→ ready_
Case Patterns

Engineering Success Stories

FINTECH

LLM Integration for FinTech

Compliance-aware retrieval layer serving 1M+ queries/day with audit-grade traceability.

visibility LOGISTICS

Edge Vision

YOLOv8-based detection running on warehouse edge nodes with sub-50ms inference.

speed

Inference Optimization

Quantization + batching reducing GPU spend by 62% with no accuracy loss.

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INFRASTRUCTURE

Hardened AI Infrastructure

terraform.tf
resource "aws_eks_cluster" "inference" {
  name     = "ls-inference"
  version  = "1.30"
  encryption_config { ... }
}_
Engineering Rigor

The Pillars of Our Practice

01

Scalability by Design

Every system is sized for 10× the day-one load. Capacity planning happens before the first commit, not after the first incident.

02

Deterministic Reliability

Non-deterministic models, deterministic operations. We invest in eval harnesses, shadow traffic, and rollback paths so behavior change never surprises you in prod.

03

Security-First Context

Tenancy boundaries, prompt isolation, and data-egress controls are first-class architectural concerns — not features layered on after audit pressure.

Ready to move from POC to Production?

Bring us your stuck prototype, your scaling wall, or your green-field architecture. We'll engineer the path forward.

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