Unstable call audio
Background noise, volume differences, and connection quality all affect recognition.
Technology & R&D
Putting voice AI into real operations, not just demos.
AnyTaxi's R&D focuses on validating voice processing, order workflows, and dispatch systems within the same operating context. We define the problem and failure modes first, then choose testable technical approaches.
Technical problems
The hard part of voice orders is rarely a single model.
Call audio, spoken addresses, operational workflows, and existing systems are interconnected. Instability in any one stage shows up in order quality and manual workload.
Spoken address variants
Homophones, abbreviations, partial descriptions, and local names mean addresses cannot be matched literally.
Balancing latency and review
Excessive automation amplifies errors; excessive manual work removes the value of process improvement.
Integration cost
Dispatch systems, data formats, and permission models vary. Interface boundaries must be defined first.
Concept architecture
Layered, so every stage can be tested and replaced independently.
Voice entry, voice processing, the operations core, and observability connect through explicit interfaces. This lets us replace voice services, adjust extraction rules, or use human review where a stage is not yet stable.
Disclosure boundaries
This website does not disclose server addresses, credentials, keys, internal admin URLs, customer personal data, or sensitive deployment details.
Voice entry
Inbound callVoice workflow
Voice processing
Speech-to-textIntent & field extraction
Operations core
Order serviceDispatch interfaceHuman review
Data & observability
Operations dataEvent logTest samples
Cross-cutting: access control · logging · monitoring · data minimisation
Engineering approach
Make the system inspectable before scaling it.
Testing, records, and human review create a feedback loop. Without a comparable baseline, it is difficult to know whether a change actually improves the workflow.
Fixed test sets
Use manually labelled call samples to create a repeatable evaluation baseline.
Event records
Record outcomes and failure reasons at each stage to support tracing and debugging.
Human review
Low-confidence or conflicting information goes to human confirmation rather than straight into an order.
Data minimisation
Retain only what the workflow needs, with access control and retention limits.
Milestones
Progress described by inspectable deliverables.
Milestones are not replaced by dates or outcomes that have not happened. Status reflects current progress and is updated as testing proceeds.
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Order workflow and field definitionsCompleted
Workflow scripts, required fields, and exception scenarios serve as the development and test baseline.
Verification：Workflow documents and field lists
Completed -
Offline call sample testingIn validation
Fixed samples are used to check speech-to-text and address extraction. Accuracy figures are not yet published.
Verification：Fixed test set and manual annotation comparison
In validation -
End-to-end prototypeIn development
Integrating voice input, field extraction, and order creation to check hand-offs.
Verification：Integration test from voice input to order creation
In development -
Dispatch interface integrationIn development
Defining interface specifications between orders and dispatch, validated in a test environment.
Verification：Interface specification and sandbox validation records
In development -
Pilot operations metricsPlanned
Planning observable metrics such as call throughput, human review rate, and order completion rate.
Verification：Metrics defined with pilot partners
Planned
Work with us
Need technical cooperation or more R&D detail?
If you are evaluating voice workflows for fleets, AI automation, or system integration, get in touch to discuss a verifiable scope.