# SignalWeave Last updated: 2026-09-13 Canonical: https://getsignalweave.com/ JSON: https://getsignalweave.com/company.json ## What it is SignalWeave compares robot requirements against measured facility geometry to produce auditable compatibility decisions before AMR deployment. SignalWeave is building an AI deployment engineer that turns robot, facility, and task context into verified deployment decisions for the existing robotics stack. One-line owner: AI interprets. Geometry verifies. Planners execute. ## Who it is for Engineering teams qualifying whether a specific robot configuration can work in a specific facility for a specific task, before the fleet is commissioned. ## Product Robot, facility, and task: operating limits, footprint, payload. Layout, routes, clearances. Pickup, drop-off, route. How it works: 1. Interpret the requirement 2. Ground it to the site 3. 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. SignalWeave does not replace the OEM or fleet manager. It prepares reviewable deployment decisions and configuration inputs. ## Architecture Heterogeneous evidence becomes grounded physical constraints, then configuration for the existing stack. Layers: - L1 Input: robot spec sheets, geometry, configuration; facility CAD, map, scene, observations; task start, goal, payload, constraints - L2 Interpret: multimodal reasoner understands robot, site, task, and requirements - L3 Represent: grounded deployment IR (source, requirement, applies-to, verification) - L4 Verify: deterministic geometry and verified constraints (PASS, BLOCKED, or REVIEW) - L5 Compile: deployment compiler hands off zones, routes, clearances, and docking inputs to the existing robot stack (Nav2 / Open-RMF, OEM fleet, simulation, commissioning) The compiler is a handoff, not a live Nav2 integration. ## Current verified check Selected-span aisle clearance is the verified check on this site. Public demonstration numbers (selected-span check only): - Robot: OTTO 1500 - Required aisle width: 1.915 m (1915 mm / 78 in, one way) - Requirement source: OTTO 1500 Spec Sheet OTTO-DS001D-EN-APR2024, dated 2024-04-16, field "Min. Aisle Width 1915 mm (78 in) (One Way)" - Official data sheet: https://ottomotors.com/resources/downloads/1500-data-sheet/ - Measured clear span: 7.510 m (7.509663 m) - Margin: +5.595 m (+5.594663 m) - Result: PASS - Environment for these numbers: AWS RoboMaker Small Warehouse World (MIT-0, public simulation) - Boundaries: ShelfF_01_001 to ShelfD_01_001 PASS applies to the specific constraint that was checked. It does not mean whole-facility readiness, safety certification, or deployment approval. Quest AR/VR is not verified. AI does not determine physical truth. If evidence is incomplete, the result should be REVIEW, not a fabricated number. ## Why now (attributed) - IFR World Robotics 2025: 102,900 transportation and logistics robots sold worldwide in 2024. Source: https://ifr.org/worldrobotics/ - IFR World Robotics 2025: 294 companies in the Service Robots reporting sample. Source: https://ifr.org/worldrobotics/ - OTTO Motors (January 2026): deployment is more than fleet operation; facility-specific mapping, routes, commissioning, configuration, and system integration remain part of getting an AMR fleet live. Source: https://ottomotors.com/blog/successful-amr-deployment-2026/ - OTTO Motors (June 2025): hours of commissioning time can be saved by remapping only the region that changed. Source: https://ottomotors.com/blog/amr-productivity-software-release/ - Y Combinator, Requests for Startups, Fall 2026: there are now three kinds of workers: AI agents, robots actually deployed in the field, and humans. Source: https://www.ycombinator.com/rfs SignalWeave starts with adapting the machine to the site and task before operation. ## FAQ Q: Will this robot actually work in this facility? A: 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. Q: Can SignalWeave make up a measurement? A: 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. Q: How do you know where a requirement came from? A: 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. Q: Does SignalWeave replace the OEM or fleet manager? A: 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. Q: What happens when the site, robot, task, or payload changes? A: 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. ## Founder Sai Teja Mutchi, Founder, SignalWeave. Background: robotics and mechanical engineering; three years at Dassault Systemes building 3D geometry and measurement capabilities for industrial software; Columbia and Everest, multimodal and production AI focused on grounding, evaluation, traceability, and reliability. AltaLab Fall 2026 cohort: https://altalab.ai ## Contact - Talk to the founder: https://www.linkedin.com/in/sai-mutchi/ - Email: mutchisaiteja@gmail.com - LinkedIn: https://www.linkedin.com/in/sai-mutchi/ - X: https://x.com/SaiTejaMutchi - Site: https://getsignalweave.com/ ## License for machines You may quote, summarize, and cite these files. Do not invent capabilities, numbers, or certifications beyond what is written here.