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Bedrock Claude and Copilot GPT are excellent generalists. P&C work exposes four specific gaps that a fine-tuned domain SLM is built to close.
Generalist models confidently misread endorsements, exclusions and state-form language because they were never trained on your policy wordings. The cost of one wrong coverage answer is real money and real complaints.
Claim notes and FNOL transcripts are full of PII and sometimes PHI. Sending raw text to a hosted generalist API triggers a privacy-review cycle each quarter, plus state-DOI questions on data residency and AI use disclosures.
A 7B fine-tuned SLM serving FNOL classification and adjuster summarization at scale typically runs at 10–100x lower per-call cost than a hosted frontier LLM — with predictable, capacity-based pricing instead of per-token meters.
Inside an adjuster's workflow, sub-second response matters. A small, quantized SLM running on your own GPUs delivers consistent low latency without the variable tail of a public API.
From your existing data sources to a deployed, monitored P&C SLM — in your own environment.
Policy wordings, ACORD forms, claim notes, FNOL transcripts, underwriting guidelines
Layout-aware parsing, OCR, PII / PHI scrubbing, dedup, glossary alignment
Q&A pairs, instruction sets, reasoning traces, hard-negatives from your corpus
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, DPO for refusals & grounding
P&C task suite, LLM-as-judge with rubrics, regression gating, red-team probes
vLLM / SGLang on your VPC GPUs, guardrail SLM, monitoring & drift alerts
Every step is reproducible: each model artifact is linked back to its dataset hash, training recipe and code commit. Your audit team gets a clean lineage; your engineering team gets predictable retrains.
Each card shows the task, the input, the output and a target quality / cost bar — the actual conversation you'll have with your AI program office.
Classify a first notice of loss the moment it arrives — line of business, peril, severity tier, special-handling flags — so it routes to the right adjuster queue without manual review.
Answer "is this covered?" questions for agents, adjusters and customers — always grounded in the actual policy form, endorsements and state amendments, with citations back to the page.
Extract insured name, policy number, peril, loss date, coverage limits, deductibles and endorsements from ACORD-25 / 27 / 125 / 140 and carrier-specific PDFs — into a clean schema for your claim system.
Compress months of adjuster notes and call transcripts into a structured claim summary — status, key events, open actions, next steps — refreshed every time the file changes.
Surface claims that warrant SIU review or hold subrogation potential, by reading notes and structured signals together — so investigators see the right files and recovery dollars are not left on the table.
Draft denial, partial-coverage and status letters in plain, compliant language — tuned to your tone, your state-by-state disclosure rules, and your prior-letter style guide.
The bar we design and evaluate a P&C SLM to. Customer-specific scorecards are produced from the held-out evaluation suite during a pilot.
| P&C Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) |
InsightLM Fine-Tuned 7B (Qwen / Llama / Mistral base) |
|---|---|---|---|
| FNOL peril classification | Top-1 accuracy | ~85% | ≥ 92% (target) |
| FNOL severity tier | Top-1 accuracy | ~78% | ≥ 88% (target) |
| Coverage Q&A grounding | Citation precision | ~80% | ≥ 95% (target) |
| ACORD extraction | Field-level F1 | ~88% | ≥ 96% (target) |
| Claim summarization | Adjuster rating (1–5) | ~3.7 | ≥ 4.3 (target) |
| Median latency (agent-assist) | p50 / p95 | ~900ms / ~3.5s | ~150ms / ~600ms (target) |
| Cost per 1K calls (typical task) | USD | ~$5–$30 | ~$0.02–$0.20 (target) |
InsightLM is not a Bedrock or Copilot replacement — it slots in where a fine-tuned, owned, in-VPC SLM is the better answer, and yields gracefully where a frontier LLM is.
FNOL triage, ACORD extraction, claim summarization, policy Q&A, denial-letter drafts. Tasks that run thousands or millions of times, on PII-heavy data, with predictable shape. The combination of cost, latency and data-control makes a fine-tuned SLM the right tool.
Agent-assist where the SLM answers the common 80% in-VPC, and Bedrock Claude is invoked for the long-tail. The orchestration layer is yours; InsightLM emits clean confidence signals so routing to the frontier model is a one-line policy.
One-off underwriter research, exploratory analytics, a copilot drafting an internal memo. Low volume, broad reasoning, no PII. Bedrock Claude or Copilot GPT is the right tool here — an SLM would be over-engineering.
Pick the pattern that matches your data classification, GPU strategy and compliance posture.
vLLM / SGLang on managed GPU instances inside your AWS, Azure or GCP VPC. Training on the same accounts, datasets stored in your S3 / ADLS / GCS. Bedrock and Copilot remain available for tasks where they're the better fit.
InsightLM SLM serves the high-volume in-VPC workload; an orchestrator (your existing one or a thin layer we ship) routes low-confidence cases or rare-domain queries to Bedrock Claude. Single audit log, single cost dashboard.
For carriers with strict data-residency requirements or on-prem mandates from group security: fully-private InsightLM with on-prem GPU clusters, no egress to public APIs, optional fully air-gapped operation.
Quantized GGUF / AWQ models run on adjuster laptops or handhelds for catastrophe-response or claim-inspection scenarios where connectivity is unreliable. The same model, same prompts, same eval suite as the cloud deployment.
InsightLM curation pipelines turn each source into model-ready training data — with PII / PHI scrubbing and lineage tracked end-to-end.
Policy wordings, endorsements, state amendments, underwriting guidelines, manuals and bulletins.
ACORD forms, FNOL records, adjuster notes, estimates, photos & reports, settlement letters.
Call transcripts, chat logs, agent emails, Salesforce / Guidewire ContactManager notes, complaint records.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your data inside your VPC, and produces a real scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
In your VPC • Your data never leaves your network • Works alongside Bedrock and Copilot