Real-time insights
Use incoming operational data to surface meaningful changes while they are still actionable.
Large-scale time-series intelligence
Real-time insights.
Reliable
automation.
Built for high-throughput, time-series-intensive operations. AOCA combines real-time, data-driven insights with autonomous intelligence that acts, verifies outcomes, and adapts as conditions change.
Signals. Insights. Action.Illustration
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Insights and action, across operations.
AI OpsSecurityManufacturingFinance
Why AOCA
From live data
to reliable action.
AOCA connects investigation, reasoning, and action in ongoing autonomous operation. It checks actual effects, learns from results, and adapts subsequent responses.
Operational context
Connect related signals over time. Respond to developing system conditions, not just isolated alerts.
Evidence-led decisions
Make the basis for an insight clear, keep uncertainty visible, and distinguish a useful conclusion from a plausible answer.
Reliable automation
Turn real-time insights into autonomous, goal-directed work. Verify outcomes against operating conditions and adapt as those conditions change.
Changing conditions
Use feedback from outcomes to improve future responses. Evaluate reliability as conditions change, not only on familiar examples.
Verified outcomes
Check actual effects, not simply whether an action was triggered. Use the result to guide continuation or recovery.
Insight is the starting point.
Reliable action is the goal.
Performance & evidence
High throughput.
Evidence that matters.
High-volume time-series processing, with an upcoming AI Ops benchmark focused on diagnostic quality and speed.
Processing throughput
High-throughput processing
- Frames per second
- 10,000
- Time series per frame
- 110
Processing multivariate frames at high frequency: many signals, considered together as new data arrives.
Workload-specific throughput. Hardware, test duration, processing scope, and latency are not yet published.
Results forthcoming
AI Ops benchmark
Our forthcoming benchmark evaluates fault identification, root-cause diagnosis, and time to diagnosis from operational data.
Evaluation focus
- Fault identification and root-cause localization
- Operational evidence supporting each diagnosis
- Time to diagnosis and comparison baselines
Scores and evaluation conditions will accompany the report.
Measure the whole job.
Evaluate processing throughput, latency and compute requirements; diagnostic quality and time to diagnosis; and successful autonomous recovery, unintended effects and resource efficiency in the operating environment.
Define your evaluationApplications
Different operations.
Insights that drive action.
Across domains, AOCA's focus is the same: turn continuously arriving data into useful insights and autonomous, goal-directed action.
Digital infrastructure
AI Ops
Detect developing service degradation, investigate its causes, take corrective action, and verify recovery. Improve reliability and resource use as conditions change.
From service degradation to verified recovery.
Security operations
Security
Relate changing behavior across signals to support investigation and controlled response workflows.
Focus attention. Coordinate a response.
Industrial systems
Manufacturing
Use equipment and process time series to detect developing issues and guide timely, controlled interventions.
Connect changing conditions to useful action.
Financial environments
Finance
Financial and operational time series, connected to timely insights and reliable process automation.
Timely insight. Consistent operations.
AI Ops at work
Illustrative scenarioService latency rises as operating conditions change.
Investigate
Examine changes in demand, system health and dependencies.
Act
Select and carry out a corrective response appropriate to the conditions.
Verify
Check whether service performance recovered and whether the response caused other problems.
Adapt
Continue, revise the response or recover from a failed action according to observed results.
Working in another time-series-intensive environment? Let's explore the fit
A practical starting point
Evaluate AOCA on a defined operational problem.
Define the target system, available data and operational goals. Establish a baseline and evaluate AOCA's insights, autonomous actions and recovery against observed outcomes.
Evaluation deliverable
A documented assessment of diagnostic quality, autonomous execution and operational impact.
Ground the conversation in
The operational outcome
What decision, response, or workflow needs to improve?
The relevant data
Which time series, operating context, and system relationships matter?
The automation opportunity
Which goals and workflows should AOCA pursue autonomously?
The success criteria
How will you measure insight quality, response time, and successful workflow outcomes?
The company behind AOCA
Intelligence that
does useful work.
Taijitu AI develops intelligence and automation technology for practical, real-world use. AOCA focuses that work on operations driven by continuous time-series data.
Explore Taijitu AILet's talk
What would you like
to automate?
Let's discuss your data, goals, and the work you want AOCA to carry out autonomously.