Failure observability
- — Synchronized multimodal timeline for each robot run
- — Evidence lineage to policy, planner, controller, firmware, and hardware versions
Failure observability for physical AI
Find where a failed robot task actually broke.
Telivron connects what the robot saw, predicted, planned, commanded, and physically did—so engineers can find where a failed task broke and decide what to change next.
01 / THE PROBLEM
A failed pick is easy to see. Why it failed is not.
The camera sees a red cup. The policy picks a grasp point. The arm closes the gripper. The cup slips during lift.
The cause could sit in perception, planning, control, sensing, firmware, hardware, the training data, or the inference inputs. Each layer logs to a different place, on a different clock, under a different version. A task result tells you it failed; it doesn't tell you which boundary to inspect.
PERCEPTIONPLANNINGCONTROLSENSINGFIRMWAREHARDWARETRAINING DATAINFERENCE DATA
02 / HOW IT WORKS
Reconstruct the attempt, then narrow it down.
Using the red-cup attempt as the example. The prototype runs this flow on an illustrative sample run, not connected customer data.
01
Connect the run
Load camera/depth, wrist force, gripper and joint state, plus policy, planner, and firmware versions for the failed attempt.
02
Reconstruct
Put detection, grasp prediction, planned path, commanded force, and measured motion on one clock.
03
Diagnose
Separate observed evidence from hypotheses and identify the component boundary to test first.
04
Test counterfactuals
Telivron can autonomously initiate the analysis: an approved, privacy-controlled reconstruction is sent to a connected world model or simulator, which replays the scenario and tests targeted single factors and high-value combinations.
05
Sign off
Telivron compares outcomes, ranks likely contributing causes, and presents the evidence, simulation results, support score, and suggested corrective actions for engineer review and sign-off.
03 / WHY EXPLAINABILITY MATTERS
Pass/fail says it failed. Explainability says where to look.
An explanation here means the evidence chain behind a failure: what was recorded, what is only inferred, which component boundary to inspect, and which regression test or data change to run next.
Once plausible failure factors are identified, the proposed flow lets Telivron autonomously initiate a root-cause analysis by sending an approved, privacy-controlled reconstruction to a connected world model or simulator.
It is not causal proof, and Telivron doesn't change the robot. Engineers confirm the cause by running the next test.
PICK RED CUP → SHELF · FAILED ATTEMPTILLUSTRATIVE SAMPLE
OBSERVED
- Wrist-camera visibility dropped as the gripper approached.
- Predicted grasp point moved toward the cup edge.
- Commanded grip force was below target.
- Cup slipped during lift.
HYPOTHESES TO TEST
- Camera occlusion degraded the grasp estimate.
- Policy has no close training example for this cup orientation.
- Grip-force threshold is set too low for this object.
- Firmware timing between force feedback and actuation.
Illustrative pilot output — replace with connected run data.
HOW THIS HELPS
Let the model run the variations. Keep the decision with the engineer.
Once a failed run is reconstructed and plausible failure factors are identified, Telivron can autonomously initiate a root-cause analysis: an approved, privacy-controlled reconstruction is sent to a connected world model or simulator. The model replays the scenario, tests targeted single factors and high-value combinations where needed, and compares the outcomes. Telivron ranks the likely contributing causes, then flags the case to a human engineer.
The handoff: Telivron presents the observed evidence, simulation results, a confidence/support score, and suggested corrective actions for engineer review and sign-off. It never autonomously changes policy, firmware, hardware, training data, or the robot. Observed hardware evidence, generated simulation output, and unresolved unknowns stay separate throughout.
This makes Telivron a force multiplier for engineers: it turns a vague robot failure into an evidence-backed, prioritized next step before a human is interrupted. It is the missing investigation layer between your robot data tools and the engineering decision—an engineer's first responder, not a guaranteed fix.
WORLD-MODEL ANALYSIS IS NOT MAGIC
Confidence is a support and ranking score under the selected model and scenarios—not probability of truth, causal proof, or a safety certification. Results depend on model fidelity, calibration, scenario coverage, and reconstruction quality. More simulation is useful only when the model is calibrated against physical results, and predicted success does not prove the physical robot will succeed.
Proposed product direction unless a real simulator is connected. Today's outputs are hypotheses and support scores, not causal proof or a safety certification.
04 / WHAT WE OFFER
Four layers, one investigation.
Telivron works alongside your robot-data visualization, experiment tracking, simulators, and model-monitoring tools. It reads from them; it doesn't replace them.
Failure explainability
- — Failed vs. successful and sim vs. real comparison
- — Likely failure boundary to inspect
- — Observed facts kept separate from hypotheses
Counterfactual analysis
- — Autonomously initiated: an approved, privacy-controlled reconstruction sent to a connected world model or simulator
- — Targeted single-factor tests followed by high-value combinations
- — Generated output separated from physical observations and unknowns
Engineering sign-off
- — Suggested root-cause investigation and proposed corrective actions
- — Engineer approval before any change
- — Investigation records and regression cases
05 / MARKET CONTEXT
The category exists, but the workflow is fragmented.
Good tools already exist for replaying robot data, tracing AI applications, and tracking experiments. A robot failure investigation usually spans several of them, plus firmware logs and hardware notes, and the connective work between them is often done by hand.
See sources06 / WHERE TELIVRON IS DIFFERENT
Robot data workspaces
Strong at collecting, visualizing, replaying, and searching multimodal robot data.
General AI observability
Strong at tracing model and application calls, running evaluations, and comparing experiments.
Experiment trackers
Strong at logging runs, artifacts, and model versions.
Telivron
Focused on the cross-layer failure investigation: synchronize robot evidence, tie it to policy, planner, controller, firmware, and hardware versions, separate evidence from hypotheses, and turn findings into engineering actions and regression records.
This describes where we focus, not an exclusive claim. Many teams will use Telivron next to the tools above.
07 / MARKET OPPORTUNITY
A still-forming market. We start with a narrow wedge.
Public forecasts point to scale, but they describe adjacent markets—not Telivron's addressable market. We treat the size of our own wedge as unvalidated and revisit it as pilot conversations sharpen pricing and scope. Public sources are linked below.
THE BROADER OPPORTUNITY
Robotics and physical-AI software is growing quickly, and third-party estimates vary widely. Robot software forecasts range from roughly $10B to $24B in 2025, with different research houses projecting into the tens of billions by 2030–2035. Goldman Sachs Research projects the humanoid robot market alone could reach roughly $38B by 2035 in its base case, with a blue-sky scenario near $154B. The installed base is already large: more than 4.6M industrial robots operate worldwide, with over 500,000 new installations a year. These are cited for context only—they describe adjacent markets, not Telivron's addressable market.
OUR INITIAL WEDGE
Engineering and infrastructure teams at:
- — Robot OEMs
- — Warehouse and logistics robotics
- — Industrial automation
- — Robotics infrastructure teams
These teams ship learned policies and own the failure investigations around them. The serviceable market inside this wedge has not been measured and requires validation.
NEAR-TERM MONETIZATION WEDGE
Paid design-partner pilots first, then annual enterprise software contracts priced around failure investigations, deployments, or robot programs. This describes our proposed pricing direction—not current revenue or customer commitments.
08 / ARCHITECTURE
Your systems stay yours.
Robots, simulators, telemetry, and source data stay with you. The robot should not depend on a network connection. A production counterfactual run would require customer approval, redaction, access policy, retention settings, and an approved endpoint.
PROPOSED OFFLINE / STANDALONE DIRECTION
Capture evidence locally, reconstruct and rank hypotheses offline, then export an encrypted investigation bundle through an approved workstation or removable media. When connectivity is available, heavier world-model or simulator analysis can run separately and the result can be imported for engineer review. This is not implemented today.
CUSTOMER-OWNED EVIDENCE
RobotsTelemetrySource dataAccess policy
APPROVED DATA SCOPE
TELIVRON-OWNED ORCHESTRATION
Redaction + normalization
Evidence lineage
Intervention plan
Comparison + confidence report
CONTROLLED REQUEST
CUSTOMER-APPROVED EXTERNAL COMPONENT
World modelSimulatorCounterfactual rolloutsRetention controls
GENERATED OUTPUT + UNCERTAINTY
TELIVRON WORKFLOW
Keep observed evidence, generated output, and unknowns separate → compare outcomes → rank likely contributors → suggest an investigation and corrective actions → engineer sign-off
No autonomous change to policy, firmware, hardware, training data, or the robot.
PLANNED ADAPTERS
MuJoCoIsaac SimGazeboROS 2GitHubGitLabInternal stacks
09 / WHO IT IS FOR
Teams whose robots fail in ways logs alone don't explain.
We start narrow: one robot program, one recurring failure mode, one evidence bundle, one paid pilot. If the investigation is useful there, the same mappings carry to the next failure.
01
Teams training learned policies
02
Humanoid & manipulation companies
03
Warehouse & industrial automation
04
Autonomous mobile robots & drones
05
Research labs moving from sim to hardware
10 / PILOT OFFERING
Bring us one failure you still cannot explain.
A paid pilot scoped around one recurring failure on one robot or simulator. We map your evidence, investigate the failure with your engineers, and hand back a report and a regression plan.
Proposed pilot scope—not validated customer results.
Discuss a pilot4–6 week paid pilot01
One robot or simulator evidence source02
Normalized evidence schema03
Proposed sample of 20–50 representative runs04
One recurring failure mode investigated05
Evidence-backed failure report and regression test plan06
Reusable connector profile and prioritized action list07
11 / COMPANY
Built for the teams shipping physical AI.
Telivron is built by a technical founder with extensive experience across physical AI and platform engineering, including work in the Stanford ecosystem. The founder also brings product executive experience: leading product strategy, translating complex technical systems into usable workflows, and working with engineering and customer stakeholders.
We are looking for design partners who want a more rigorous, repeatable way to investigate difficult robot failures.
CONTACT
Talk with us about a failure you’re chasing.
We use this information only to respond to your inquiry.
12 / FAQ
Direct answers.
Does Telivron tell me the root cause?
No—it suggests. After reconstructing a failed run and identifying plausible failure factors, it can autonomously initiate a root-cause analysis by sending an approved, privacy-controlled reconstruction to a connected world model or simulator. The model replays the scenario, tests targeted single factors and high-value combinations, and compares outcomes. Telivron ranks likely contributing causes, then presents the evidence, simulation results, support score, and proposed corrective actions for engineer review and sign-off. Unresolved unknowns stay visible, and an engineer confirms the cause with the next physical test.
Does Telivron automatically fix the robot?
No. It does not autonomously change policy, firmware, hardware, training data, or the robot. Engineer sign-off is required before changes.
Does a world-model result prove the cause?
No. This capability is proposed product direction unless a real simulator is connected. Confidence is a support and ranking score under the selected model and scenarios—not probability of truth, causal proof, or a safety certification. More simulation is useful only when the model is calibrated against physical results.
Does the robot need a network connection?
No. In a proposed standalone deployment, the robot records evidence locally. An approved workstation can reconstruct and rank hypotheses offline, export an encrypted investigation bundle through an approved path, and import optional simulator results later for engineer review. This is a proposed direction, not an implemented capability.
Does it replace Foxglove, W&B, or our simulator?
No. Those tools keep doing what they do. Telivron reads their outputs and connects them around a single failed task.
Does Telivron certify safety?
No. It is an engineering investigation tool, not a safety certification body.
Who owns the data?
You keep ownership of source data. Telivron owns its platform, normalization, and analysis logic.
What do we need for a pilot?
One recurring failure, access to the logs from one robot or simulator, roughly 20–50 representative runs, and an engineer who owns the problem.