Key facts
| API surface | OpenAI-compatible /v1/chat/completions with streaming and JSON mode |
| Data grounding | Embeddings and RAG are live for clause and precedent retrieval |
| Long context | Long-context models are available for whole-contract reasoning |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Access control | Scoped API keys and usage analytics; enterprise SSO and RBAC options |
| Auditability | Request, retrieval and response logging for review trails |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Endpoint roadmap | Audio, images, moderation, batch and fine-tuning are coming soon |
TL;DR
- Keep matter data inside your boundary with private deployment options.
- Retrieve clauses and precedent with embeddings so answers cite real sources.
- Use long-context models for whole-contract review and JSON mode for extraction.
- Log retrieval and responses to create a review trail.
- Never treat model output as legal advice without qualified human review.
How it works, step by step
- Agree with risk and compliance which data classes may reach which deployment mode.
- Choose the target environment: cloud for public law, VPC or on-prem for client matters, air-gapped for the most restricted.
- Build a precedent library with clean metadata: jurisdiction, matter type, date and outcome.
- Embed and index the library, then test clause retrieval against real review questions.
- Design extraction schemas in JSON mode for clauses, dates, parties and obligations.
- Require citations in every answer and route drafted output to a qualified reviewer.
- Track correction rates and update the library as new precedent is approved.
Try it yourself
Open the RAG architecture builder →
High-value legal workflows
Legal work rewards retrieval and structure more than open-ended generation:
- Contract review: find non-standard clauses, missing terms and deviations from your playbook.
- Clause extraction: return parties, dates, obligations and termination rights in a fixed schema.
- Matter and precedent search: answer questions from prior work product with citations.
- First drafts: assemble a starting point from approved templates for lawyer editing.
Each workflow keeps a qualified person in control of the final position.
RAG over precedent with citations
Index approved work product with metadata that matters to lawyers: jurisdiction, practice area, counterparty type and date. Use multilingual embeddings if the practice spans languages, and keep the vector store inside the same boundary as the documents.
Require the model to answer only from retrieved passages and to name them. When nothing relevant is found, the assistant should say so rather than generate a plausible clause from memory.
Confidentiality, privilege and access
Ethical obligations around confidentiality and privilege are the reason private deployment matters. VPC and on-prem modes keep prompts and documents inside your tenancy; air-gapped environments remove outbound connectivity entirely. Deploy the same OpenAI-compatible calls in every mode, so a pilot on public material translates directly to the production environment.
Scope API keys per team or matter system, rotate them, and keep an access log for the console. See the AI governance framework for a policy skeleton.
Drafting with verification
Treat drafts as research memos, not filings. Use JSON mode for structured output that a checklist can validate, show the retrieved sources next to each claim, and make reviewer edits feed back into the evaluation set. Over time, measure how often drafts survive review unchanged; that number, not benchmark scores, tells you whether the tool is working.
Honest comparison
| Capability | Plugsky | Consumer AI tools | Building in-house |
|---|---|---|---|
| Confidentiality | VPC, on-prem, air-gapped deployment options | Vendor cloud only | You control fully |
| Precedent retrieval | RAG with citations over your library | No access to work product | You build ingestion and evals |
| Structured extraction | JSON mode for clause schemas | Unreliable format control | Custom post-processing |
| Long documents | Long-context models in the 30+ catalogue | Limited context | Depends on hosted model |
| Audit trail | Request and response logging | Limited | You build logging |
| Pricing | Flat monthly self-serve plans; see live pricing | Per-seat | GPU plus operations cost |
Frequently asked questions
Does this replace legal judgment?
No. It retrieves, summarizes and drafts. A qualified lawyer must review every output before it informs advice, a filing or a negotiation position.
Can client matter data stay in the firm?
Yes. VPC, on-prem and air-gapped deployments keep prompts and documents inside your environment, and the API shape does not change between modes.
How do we avoid hallucinated clauses?
Restrict answers to retrieved passages, require citations, allow a clean refusal when nothing matches, and validate structured output against a schema.
Is fine-tuning useful for house style?
Potentially, but fine-tuning is coming soon. Start with RAG plus templates and examples in the prompt, which is easier to update when house style evolves.
Can it summarize long contracts?
Long-context models help with whole-document reasoning. Combine them with clause-level retrieval so the summary remains traceable to specific text.
How do we audit what the model saw?
Log the model name, the retrieved passage identifiers and the response. That trail lets a reviewer reconstruct how a draft was produced.
What is live today?
Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, assistants, responses and fine-tuning are coming soon.