The robots are becoming general. The physical world still isn’t.
SignalWeave is building an AI deployment engineer that turns robot, facility, and task context into verified deployment decisions for the existing robotics stack.
Before the fleet runs, someone has to make the deployment work.
Robot, facility, and task: operating limits, footprint, payload. Layout, routes, clearances. Pickup, drop-off, route.
- Interpret the requirement
- Ground it to the site
- Verify what is physically true
Then the decision hands off. OEM tools, planners, and fleet managers still commission and operate the robot. If the robot, site, task, or layout changes, qualify again.
OTTO 1500 data sheetPASS applies to the specific constraint that was checked. It does not mean whole-facility readiness, safety certification, or deployment approval.
Architecture
Heterogeneous evidence becomes grounded physical constraints, then configuration for the existing stack.
Why now
transportation and logistics robots sold worldwide in 2024
Transportation and logistics was the largest professional service robot application group reported by IFR.
IFR World Robotics 2025companies in the World Robotics 2025 Service Robots reporting sample
A growing robot ecosystem makes deployment context and system boundaries increasingly important.
IFR World Robotics 2025major engineering areas before an AMR deployment
Workflow, layout, tooling, navigation, maps, custom integration, IT, and testing must all be reconciled before operation.
OTTO Motors, May 2024less time to set up facility maps and workflows
Saving hours of commissioning time by identifying and remapping only the region that changed.
OTTO Motors, June 2025kinds of workers: AI agents, robots in the field, and humans
SignalWeave starts with adapting the machine to the site and task before operation.
Y Combinator, Requests for Startups, Fall 2026Questions we get
Will this robot actually work in this facility?
SignalWeave evaluates the specific robot configuration, facility geometry, and task against supported deployment constraints. The current wedge starts with measurable spatial checks like aisle clearance.
Can SignalWeave make up a measurement?
No. AI can help determine what should be checked, but measurable conditions are computed from explicit geometry using deterministic tools. If the evidence is incomplete, the result should be REVIEW, not a fabricated number.
How do you know where a requirement came from?
Each supported check should preserve the robot model, source document or record, the requirement, the facility entities assessed, the measurement method, and the resulting margin.
Does SignalWeave replace the OEM or fleet manager?
No. SignalWeave is designed to prepare reviewable deployment decisions and configuration inputs. Existing OEM, planner, simulation, and fleet systems still commission and operate the robots.
What happens when the site, robot, task, or payload changes?
Those changes can alter what the deployment must satisfy. SignalWeave is being built to identify which prior checks may need review instead of treating deployment assumptions as permanent.
Find the incompatibility before commissioning finds it for you.
If a deployment is still qualified by hand, talk to us.
Talk to the founder
I've spent my career at the intersection of machines, geometry, and reliable AI.
I started in robotics and mechanical engineering, then spent three years at Dassault Systèmes building 3D geometry and measurement capabilities for industrial software. At Columbia and Everest, I moved into multimodal and production AI, focused on grounding, evaluation, traceability, and reliability.
SignalWeave is the product I'm uniquely positioned to build: software that interprets robot and site context, verifies the physical constraints with geometry, and turns that into deployment decisions engineers can trust.