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Frontier LLMs are great copilots for analysts. Production telecom care and network operations expose four hard limits a fine-tuned SLM is built to remove.
Triaging or assisting on millions of calls and chats per day at $5–$30 per 1K calls is unaffordable. A 7B SLM serves the same workload at a fraction of the cost — predictable, capacity-based.
Agents and customers expect answers in well under a second. A small quantized SLM on dedicated GPUs delivers consistent low latency without the variable tail of a public API.
Generalist LLMs do not know your latest plans, devices, promotions or fee schedules. A fine-tune retrained nightly on the current catalog answers from today's offers, not last year's training data.
Generalists translate your plans and policies inconsistently across markets. A fine-tune that knows your terminology in every language keeps every market on the same offer set with one model.
From plan catalogs, KB articles and contact-center conversations to a deployed telecom SLM — in your own environment.
Plan catalogs, device specs, KB articles, conversations, network/outage data, complaint tickets
Layout-aware parsing, ASR for calls, PII scrubbing, dedup, language tagging
Intent / NBA / wrap-up training pairs, plan-grounded Q&A, multilingual instruction sets
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, daily / weekly retrain on plan catalog
Intent F1, NBA acceptance, plan-QA grounding, multilingual quality, churn-signal recall
vLLM / SGLang on VPC GPUs at low latency, guardrail SLM, observability into your CCAI stack
Same dataset hash → recipe → model → scorecard lineage as the rest of InsightLM. Care ops gets fast retrains tied to your current plan catalog; quality gets version-pinned production behavior.
Each card shows the task, input, output and a target quality / cost bar.
Answer "is unlimited 5G included on this plan?" or "how is my international roaming charged?" — grounded in today's plan catalog and rate cards, with cited source per claim.
Classify inbound intent (billing, technical, sales, retention, fraud, port-out, complaint) within the first turn so the call lands in the right queue with relevant context attached.
Surface next-best-action to the agent as the conversation unfolds; auto-draft the after-call wrap-up note; pre-populate disposition codes — with grounded citations to KB and policy.
Compress the noisy fragmentary stream of network alarms, incident notes, and bridge-call transcripts into a structured incident timeline for NOC, customer comms, and exec updates.
Read post-call transcripts, chats, and complaints to surface dissatisfaction signals, churn-risk reasons, and policy / pricing pain points — for save-team queues and product teams.
Draft personalized retention offers and outbound win-back messages grounded in the customer's plan, usage, and complaint history — respecting your tone and approved offer matrix.
The bar an InsightLM telecom SLM is designed and evaluated to. Customer-specific scorecards are produced from held-out evaluation sets during a pilot.
| Telecom Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) | InsightLM Fine-Tuned 7B |
|---|---|---|---|
| Plan / billing QA grounding | Citation precision | ~80% | ≥ 95% (target) |
| Intent classification | Top-1 accuracy | ~84% | ≥ 92% (target) |
| Agent-assist NBA | Acceptance rate | ~50% | ≥ 70% (target) |
| Outage summarization | NOC rating (1–5) | ~3.6 | ≥ 4.3 (target) |
| Churn signal recall | Recall @ 5% FPR | ~0.65 | ≥ 0.80 (target) |
| Median latency (agent-assist) | p50 / p95 | ~900ms / ~3.5s | ~120ms / ~500ms (target) |
| Cost per 1K calls (typical task) | USD | ~$5–$30 | ~$0.005–$0.10 (target) |
Bedrock Claude / Copilot GPT for analysts; existing CCAI (NICE, Genesys, Five9) for orchestration; InsightLM for the high-volume, low-latency, plan-grounded layer.
Plan QA, intent classification, agent-assist, churn extraction, retention messaging. Tasks where per-call cost, sub-second latency and plan currency are decisive.
The SLM serves the bulk of care; Bedrock Claude or Copilot GPT picks up complex multi-policy reasoning the SLM flags as low-confidence. Plugged into your CCAI orchestrator.
Product strategy, market research, exploratory reporting. Frontier LLMs are the right tool here — an SLM would be over-engineering. InsightLM does not try to win these.
Pick the pattern that matches your data classification, CCAI vendor, and traffic shape.
vLLM / SGLang on dedicated GPU instances inside your AWS, Azure or GCP VPC, sized for stable p95 latency. Plugged into your CCAI orchestrator (NICE, Genesys, Five9, AVAYA).
Fully-private InsightLM with on-prem GPU clusters, no egress to public APIs. Standard pattern where data residency or sovereign-cloud requirements apply.
SLM serves high-volume care; the orchestrator routes complex multi-policy reasoning to Bedrock Claude. One observability stack in your CCAI vendor's reporting.
Quantized GGUF / AWQ models on retail-store hardware for in-store agent-assist with full offline capability when WAN drops. Same model and prompts as the cloud deployment.
InsightLM curation pipelines turn each source into model-ready training data — with PII scrubbing and lineage tracked end-to-end.
Plan catalogs, device specs, rate cards, KB articles, troubleshooting trees, policies, fee schedules.
Call transcripts, chat logs, IVR flows, complaint tickets, post-call wrap-ups, agent dispositions.
Network alarms, outage incident notes, bridge-call transcripts, RCA reports, customer-comms templates.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your contact-center data inside your environment, and produces a real scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
In your VPC or on-prem • Works at contact-center volume • Plugs into your CCAI