- contact@verticalserve.com
Frontier LLMs are useful research assistants. Production legal work surfaces four hard requirements a fine-tuned legal SLM is built to meet.
Client-privileged content cannot be shipped to a public API without a careful review. An in-network SLM removes the conflict and lets your matter teams treat AI as an internal tool, not a third-party disclosure.
Generalist LLMs invent cases, statutes and section numbers. A model fine-tuned on your firm's grounded reasoning patterns and forced to cite back to retrieved sources removes the most embarrassing failure mode.
Reviewing thousands of contracts per month, processing discovery, or scanning regulatory filings at $5–$30 per 1K calls is a budget killer. A 7B SLM serves the same workload at a fraction of the cost.
Every firm has its own playbooks, fallback positions, and clause libraries. A generalist LLM defaults to generic redlines; a fine-tuned SLM produces work-product that matches the partner's standard.
From contracts, case law and regulatory filings to a deployed legal SLM — in your own environment.
Contracts & MSAs, case law, regulatory filings, internal playbooks & memos, prior redlines
Layout-aware parsing, OCR, privilege detection, dedup, jurisdiction tagging
Clause-extraction pairs, redline-rationale traces, regulatory-tagging instructions
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, DPO for refusals & grounding
Clause F1, redline acceptance, citation precision, jurisdiction-aware regression suite
vLLM / SGLang on VPC GPUs, guardrail SLM, audit log, drift alerts
Reproducible end-to-end: each model artifact is linked back to its dataset hash, training recipe and code commit — the lineage your innovation council and risk team need to validate releases.
Each card shows the task, input, output and a target quality / cost bar.
Extract clauses (term, termination, indemnity, IP, confidentiality, governing law, etc.) and tag obligations and risks against your firm's taxonomy — from MSAs, NDAs, vendor contracts and SOWs.
Compare third-party paper against your firm's playbook: flag deviations from approved positions, suggest fallback language, and produce a clean redlined version — ready for associate or partner review.
Summarize a case or a set of cases against a research question, with quotes and pin-cites grounded back to the source — refuses to answer when no supporting authority is retrieved.
Watch the regulators you care about, classify each new release by topic and applicability, and produce a draft impact assessment for your in-house policies, contracts and controls.
Answer "can we do X?" questions for product, marketing and engineering teams — grounded in your privacy policies, regulatory commitments and historical guidance, with cited source per claim.
Prioritize discovery review by responsiveness probability, flag privileged documents for second-pass review, and propose redactions — with full audit trail for each decision.
The bar an InsightLM legal SLM is designed and evaluated to. Customer-specific scorecards are produced from held-out evaluation sets during a pilot.
| Legal Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) | InsightLM Fine-Tuned 7B |
|---|---|---|---|
| Clause extraction | F1 | ~88% | ≥ 95% (target) |
| Redline acceptance | Partner accept w/ minor edits | ~50% | ≥ 80% (target) |
| Case-law citation precision | Cited claims correct | ~75% | ≥ 95% (target) |
| Invented citations rate | % | ~5–10% | 0% (target) |
| Regulatory tagging | Top-1 accuracy | ~80% | ≥ 92% (target) |
| Discovery prioritization | Recall @ 30% review | ~0.78 | ≥ 0.92 (target) |
| Cost per 1K calls | USD | ~$5–$30 | ~$0.05–$0.50 (target) |
Bedrock Claude or Copilot GPT for ad-hoc research; specialized legal AI tools for some workflows; InsightLM for the firm-specific, confidential, high-volume layer.
Contract review, redlining, regulatory tracking, compliance QA, discovery review. Tasks where confidentiality, citation grounding and per-doc cost are decisive.
The SLM handles in-firm bulk; Bedrock Claude or Copilot GPT picks up complex multi-document reasoning where the SLM signals low confidence. Single observability, single cost dashboard.
Brainstorming, public-record research, training content drafting. 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 client confidentiality posture and IT environment.
Fully-private InsightLM with on-prem GPU clusters, no egress to public APIs. Standard pattern for AmLaw firms and large in-house legal departments with strict client-confidentiality posture.
vLLM / SGLang on managed GPU instances inside your AWS, Azure or GCP VPC; document corpora stored in your S3 / ADLS / GCS. Standard pattern for in-house teams already cloud-native.
SLM serves the bulk in-network; the orchestrator routes non-confidential research or rare-domain queries to Bedrock Claude. One audit log, one cost dashboard.
For ethically-walled matters or sensitive M&A: deploy SLM instances in matter-isolated namespaces with per-matter audit log and dataset access controls.
InsightLM curation pipelines turn each source into model-ready training data — with privilege detection and lineage tracked end-to-end.
MSAs, NDAs, vendor contracts, SOWs, leases, M&A docs, prior redlines, executed-contract repository.
Court opinions, statutes, regulations, regulator filings, secondary sources, internal research memos.
Firm playbooks, fallback positions, clause libraries, deal databases, training materials, partner annotations.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your contracts and matter files inside your environment, and produces a partner-rated scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
In your firewall • Privileged content stays in-network • Complements Bedrock and Copilot