AI for telecom, evaluated on the audio you actually carry
Transcription benchmarks are run on clean studio audio. Your calls are 8 kHz, packet-lossy, and accented. We evaluate on your recordings, because a model that scores well elsewhere can be unusable here.
What changes here
Artificial Intelligence in telecommunications is not the same engagement
Evaluated on your codecs, not on a benchmark
Word error rate measured on real recordings across the codecs and conditions you carry, so the decision to deploy is made on evidence from your network.
Summaries and dispositions written back to the ticket
Call summary, intent, and next action pushed into the CRM or ticketing system automatically, which is where the operational saving actually is.
Triage that routes on the fault, not the keyword
Classification over ticket text and call transcripts that separates a provisioning fault from a quality complaint before it reaches a queue.
What the regime actually requires
Call recordings are among the most sensitive data an operator holds — subscriber identity, location, and call detail all live in the same place — and the rules around them are specific.
- Customer proprietary network information handled under FCC CPNI rules, including who may access it
- Recording consent that matches the jurisdiction, including all-party consent states and Canadian requirements
- Retention and deletion of transcripts on the same clock as the recordings they came from
- PII redaction before transcripts reach any downstream analytics or model training path
The work itself
Full artificial intelligence pageLLM applications and RAG over your own data
Retrieval-augmented systems on your documents, tickets, and records — chunking, embeddings, a vector store, and citations, so answers can be traced back to a source.
Machine learning models and forecasting
Classification, scoring, and demand or churn forecasting trained on your history, with feature pipelines and retraining scheduled rather than remembered.
Document, voice, and workflow automation
Intake, extraction, classification, and summarization wired into the systems the work already lives in, with a human review step where the cost of being wrong is high.
Evaluation sets and regression testing
A scored test set built with your subject-matter experts and run on every change, so a prompt or model swap has to prove it improved things.
Guardrails, grounding, and audit trails
Input and output filtering, retrieval grounding, refusal paths, and logged prompts and responses — the record you need when someone asks why it said that.
Latency, token cost, and model routing
Caching, batching, and routing cheap work to smaller models, with spend and p95 latency on a dashboard rather than a surprise invoice.
Telecommunications questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.