LogisticsExpertise
We build last-mile delivery platforms, warehouse management systems, and routing automation engineered for real-world constraints, not just optimized routes on paper.
Trusted by engineering teams across 15+ countries
From startups to Fortune 500 companies
Production Reality
Where Logistics AI Projects Fail
Only practitioners who've shipped real systems handling live fleets and warehouses can flag these accurately.
Route optimization that ignores real-world constraints
We build optimization models against your actual operational constraints, not theoretical ones.
Error handling breaks on edge cases
We build explicit fallback chains and human-in-the-loop checkpoints for every critical workflow.
No visibility when an automated dispatch decision goes wrong
We build structured logging and alerting into every automated dispatch decision, so failures surface immediately.
Forecasting that breaks during demand spikes
We build fallback logic and human override for conditions outside the model's validated range.
IS THIS YOU?
Three situationsthat bring logistics teams to us
If any of these sound familiar, we've solved this in production, not just in theory.
Dispatchers are manually re-routing shipments that follow a predictable pattern.
You have fleet or warehouse data that could flag delays or inefficiency automatically, but it's still reviewed manually.
You're automating dispatch or routing decisions and hitting reliability issues once real-world variability hits.
Delivery Structure
POC to Production Process
Discovery
POC Build
Production Architecture
Deploy & Monitor
Technical Stack
Models & Frameworks
Preferred for deterministic business workflow agents where state management and branching logic matter.
Multi-agent conversations where agents critique and revise each other's outputs.
Role-based agent teams with defined task sequences and human-in-the-loop checkpoints.
Managed vector store for production workloads that need fast ANN search at scale.
Hybrid search combining BM25 + vector, when keyword precision matters alongside semantic similarity.
Local and dev-stage retrieval for rapid prototyping before committing to a managed store.
GPT-4o for tool-use tasks requiring fast structured output and broad API ecosystem compatibility.
Claude for long-context tasks, instruction following, and workflows where refusal behavior needs to be controlled.
Multimodal inputs — document, image, and video understanding in a single model call.
REST and GraphQL integrations so agents can read and write to your existing systems without rebuilding them.
Event-driven triggers that let agents respond to external signals in real time.
Direct database read access for agents that need structured data alongside unstructured retrieval.
When to use it.
The Team
Engineers Behind the Work
Discipline-specific AI engineers not generalists. Each has shipped real systems in production.
Built a multi-agent loan underwriting system handling 12k daily decisions across 3 financial institutions. Designed a full audit trail with structured logging, HITL override flows for edge cases, and explicit fallback chains for every critical decision node. Integrated compliance checkpoints at each agent handoff to satisfy PCI-DSS requirements. System has been running in production for 18 months with zero critical incidents.
Designed a hybrid BM25 + vector retrieval pipeline for a legal tech platform processing 2M+ documents across 14 jurisdictions. Built a custom chunking strategy per document type, tuned embedding models for legal language, and implemented multi-tenant index isolation. Achieved sub-200ms p95 latency with real-time index updates under concurrent write load. Reduced hallucination rate by 62% compared to the previous naive RAG baseline.
Shipped a production LLM router across OpenAI, Anthropic, and Gemini with automatic provider fallback, per-token cost tracking, and latency-based model selection per request type. Built a unified streaming interface consumed by 4 downstream product teams without requiring any client-side changes. Added circuit breakers per provider, retry logic with exponential backoff, and a real-time cost dashboard. Reduced average inference cost by 34% within the first quarter.
Common Questions
Everything you needto know
Which industries do you specialize in?
We have deep expertise in Fintech, Healthcare, Logistics, and E-commerce. Each practice area is staffed with domain specialists who understand regulatory requirements, industry-specific architecture patterns, and the competitive landscape — so your team isn't educating ours from scratch.
Not sure if Logistics AI is right for your use case?We'll tell you in 30 minutes.
No pitch. No obligation. Just an honest answer about whether we can help.