The architect on your kickoff is the architect on your cutover. We’re senior engineers and Solutions Architects, with a network and bench depth across the U.S. and near shore to scale up when delivery demands it. We’ve shipped a Spark-based visual data-pipeline platform in the Databricks category, modernized lakehouses and data warehouses for enterprise shippers, and authored data-and-AI roadmaps for federated logistics holdings. Our work is grounded in three decades of building and modernizing the data, AI, and SaaS systems that real businesses run on — with the audit evidence to back every change.
Boards and investors are pushing every logistics company onto the AI train. There are three places you can be.
Silent. Scrambling. Losing share to competitors who got there first. Every quarterly call ends with the same investor question and you don't have a confident answer.
Pilots running. Tools adopted. Vendor licenses signed. But you're riding the route someone else picked, on someone else's timeline, with someone else's data leaving your perimeter.
Setting the pace. Defining the route. Choosing where AI goes and where it doesn't — based on your data, your customers, your contracts. The operator your peers benchmark against.
We help with the glide path to the driver's seat — stabilizing the data underneath, building the operational memory, putting predictive analytics on the dashboard, and putting AI in the loop only where it earns its place. With the controls your board reports on and your auditors recognize.
We converge on solutions from a library of components, concepts, and delivery patterns built over decades of logistics, data, and AI work — tailored to your actual operation. The opposite of shoehorning your business into the assumptions of a one-size-fits-all platform. Modern tooling has vastly shortened the path from spec to working software, and the patterns we’ve productized into Cayos and B1 shorten it further. Power BI and Tableau make your team adapt to the canvas; we build the inverse — dashboards and operational applications that map your process, your exceptions, and your hand-offs. The architect designing the system is the engineer shipping it; the work converges fast because it isn’t being handed across silos to converge.
Most logistics organizations don't fail at AI because the models are bad. They fail because the data underneath isn't ready. We build the ladder, in order — and at each rung, the work pays for itself.
Trust the numbers first. Modern data lakes and warehouses, governed pipelines, automated tests on every ingestion job, monitoring that doesn't fail silently. The dashboards your operations team relies on stop lying — and the data underneath is classified, lineaged, and audit-trailed from day one.
Build operational memory. Cross-system reconciliation, federated catalogs, M&A integration, semantic layers — years of trustworthy history in one place. Without this, you can't see the patterns that matter.
Math where math works. Demand forecasting, ETA prediction, lane rates, predictive maintenance, carrier scoring, risk models. Deterministic and explainable — the workhorse layer most AI pitches skip.
Put AI in the loop with kill switches and audit logs. Document automation, RAG over freight regs and SOPs, exception-handling agents — and air-gapped AI for the workloads where customer and shipment data can't leave your network. Designed for agents-as-attack-surface, not just as teammates: prompt-injection-aware, read-only by default, with the segregation of duties your auditors expect.
Logistics doesn't run on greenfield. You have an AS/400 your team won't ship without, EDI VANs with trading partners going nowhere, on-prem ERPs that took a decade to customize, and TMS overlays nobody has the appetite to rip out. We don't ask you to migrate. We layer.
Real-time change-data-capture into modern data lakes. No application changes upstream. Your green-screen operators keep working; your data team gets queryable, governed history.
Trading-partner schemas mapped into a clean canonical model. Tendering, status, invoicing, ASNs, customs filings — translated, validated, and audit-trailed without disrupting the partner network.
API wrappers and event streams that expose legacy systems to modern data and AI surfaces. Your customizations stay; your dashboards, agents, and predictive models get a clean read.
Lifted into platforms with lineage and version control, without breaking the team's workflow. The Excel chaos becomes a real platform — and the team that runs it doesn't need to relearn their job.
The four-stage ladder above is built to run on top of these. That's how the work actually gets done in logistics.
Two active research efforts that feed directly into our logistics work — and translate into customer-facing capabilities.
AI-forward data workflow engine
Built around execution islands — the bounded, well-shaped pieces of solid ground inside operational chaos, where AI can engage safely. The chaos is full of actionable bits; the work is isolating them and defining what touches them. Level 1: foundational components — streaming and processing engines like Kafka and Spark, alongside connectors for the systems your business runs on (TMS, ERP, EDI, partner APIs). Level 2: those components chained into DAGs (Directed Acyclic Graphs), where each step feeds the next. Level 3: a library of reusable building blocks for the patterns operations teams hit constantly — rate updates, exception handling, cross-system reconciliation. AI handles the judgment calls inline, so ops can automate repetitive data work without writing code.
Agentic AI harness for air-gapped workloads
An AI agent runtime — compiled binary, strict sandboxing, local-first execution — the foundation for air-gapped AI. Where general-purpose harnesses like Claude Code optimize for an engineer at a workstation, B1 inverts the defaults: capability allowlists are per-tool and default-deny, outbound network is closed unless whitelisted, every tool boundary emits an audit record, kill switches halt agents mid-action. The compiled distribution lands at roughly one-tenth the footprint of the script-based harnesses currently in market — and tampering is loud, not silent. Agent fleets run in branch-isolated worktrees with adversarial builder-and-auditor pairings — one agent does the work, another reviews before anything merges. The right design for regulated logistics workloads where contracts forbid third-party model providers.
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