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Hosted frontier LLMs are productivity tools for staff. Citizen-facing and student-facing services hit four hard requirements a fine-tuned sovereign SLM is built to meet.
State, federal, and many education programs require data and AI inference to stay in-jurisdiction. A sovereign-cloud or on-prem InsightLM deployment satisfies these requirements without an indefinite procurement-review cycle.
Generalist LLMs translate forms and benefits language inconsistently across the languages your community actually speaks. A fine-tune that learns your terminology in every supported language keeps every citizen on the same content.
Plain Writing Act, accessibility standards and reading-level guidance shape the content citizens get. A model fine-tuned on your accessibility playbook produces compliant content by default, not as a manual rewrite.
OMB guidance and state policies require traceability for AI in benefits, eligibility and education decisions. InsightLM's dataset hash → recipe → model → scorecard lineage gives you what auditors need.
From statutes, forms and benefits handbooks to a deployed citizen / student-facing SLM — in your sovereign environment.
Statutes, regulations, forms, benefits handbooks, SOPs, case files, curricula, prior comms
Layout-aware parsing, OCR, PII scrubbing on case data, dedup, version + jurisdiction tagging
Citizen / student Q&A pairs, eligibility reasoning traces, plain-language rewrite pairs
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, multilingual mixture across served languages
Citation precision, eligibility accuracy, plain-language reading level, multilingual quality
vLLM / SGLang on sovereign cloud or on-prem GPUs, guardrail SLM, immutable audit log
Reproducible end-to-end: each model is linked back to its dataset hash, training recipe and code commit — the lineage your IG, OIG and accreditation reviewers need to validate releases.
Each card shows the task, input, output and a target quality / cost bar.
Answer "how do I appeal a benefits decision?" or "what scholarships am I eligible for?" — grounded in the actual statute, handbook or program page, with cited source per claim.
Walk citizens through forms in plain language; pre-check eligibility against the program's published rules; surface missing information — with reasoning the case-worker can audit.
Translate statutory or policy language into plain-language explanations at the appropriate reading level — for portals, forms instructions, and outreach materials — with the original source preserved as the authoritative reference.
Translate forms, notices, and outreach across the languages your community speaks — with consistent terminology and tone across channels and a fall-back to human review for legally-binding text.
Generate tutoring explanations and formative assessments aligned to your curriculum standards (Common Core, state, IB, etc.), with citations to the standard and difficulty progression.
Compress long case histories into a structured summary for the next case worker, intake / appeals routing, and supervisor review — with audit log of every AI-suggested decision.
The bar an InsightLM public-sector SLM is designed and evaluated to. Customer-specific scorecards are produced from held-out evaluation sets during a pilot.
| Public-Sector Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) | InsightLM Fine-Tuned 7B |
|---|---|---|---|
| Citizen / student QA grounding | Citation precision | ~80% | ≥ 95% (target) |
| Eligibility-rule accuracy | Top-1 accuracy | ~85% | ≥ 95% (target) |
| Plain-language reading level | Target-level compliance | ~70% | ≥ 95% (target) |
| Multilingual translation | Terminology compliance | ~80% | ≥ 95% (target) |
| Case-worker summarization | Usefulness rating (1–5) | ~3.7 | ≥ 4.3 (target) |
| Median latency | p50 / p95 | ~900ms / ~3.5s | ~150ms / ~600ms (target) |
| Cost per 1K calls (typical task) | USD | ~$5–$30 | ~$0.005–$0.10 (target) |
Bedrock Claude / Copilot GPT for staff productivity in approved environments; InsightLM for the sovereign, citizen / student-facing layer that must stay in-jurisdiction.
Citizen / student QA, eligibility checks, plain-language rewrites, multilingual comms, case work. Tasks where data residency, plain-language compliance and audit trails are decisive.
Citizen / student-facing services run on the sovereign SLM; back-office staff use Bedrock Claude or Copilot GPT in approved environments for productivity tasks. Clear boundary, single oversight.
Drafting internal memos, ad-hoc research with no resident data. Frontier LLMs in your approved environment are the right tool here. InsightLM does not try to win these workloads.
Pick the pattern that matches your sovereignty requirements, accreditation environment and IT posture.
Fully-private InsightLM with on-prem GPU clusters or in a sovereign-cloud region (AWS GovCloud, Azure Government, GCP Assured Workloads, or local equivalents). No egress to public APIs.
vLLM / SGLang on managed GPU instances inside a FedRAMP / IL5 / StateRAMP-authorized region appropriate to your data classification.
Sovereign SLM serves citizen / student-facing workloads; an approved Bedrock or Copilot environment serves back-office staff productivity. One observability stack, one audit log.
Quantized GGUF / AWQ models on case-worker laptops or remote-school hardware where connectivity is unreliable. Same model and prompts as the central deployment.
InsightLM curation pipelines turn each source into model-ready training data — with PII scrubbing on case data and lineage tracked end-to-end.
Statutes, regulations, executive orders, agency forms, official notices, jurisdictional amendments.
Benefits handbooks, eligibility manuals, agency SOPs, internal training materials, accessibility guidelines.
Case files, prior decisions, appeal records, citizen / student comms, complaints, multilingual templates.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your statutes, handbooks or curricula inside your sovereign environment, and produces a real scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
Sovereign-cloud or on-prem • Resident data stays in-jurisdiction • Complements approved frontier-LLM environments