Industry Solutions

How are AI agents used in legal teams?

In-house legal teams use AI agents for contract lifecycle support, policy Q&A, obligation tracking and regulatory change summaries. The architecture pairs an OpenAI-compatible agents API with retrieval over your clause library and policies, tool calls into CLM and ticketing systems, and counsel review on anything that creates or changes an obligation. Plugsky offers 30+ models and controlled deployment.

Key facts

API compatibilityOpenAI-compatible /v1/chat/completions (change the base URL)
Models30+ models from free to frontier tiers behind one API
Agent primitivesFunction calling, JSON mode and streaming are live
RetrievalEmbeddings and RAG over your own corpus
DeploymentPlugsky cloud, VPC, on-prem or air-gapped
PricingFlat monthly self-serve plans with fair-use usage; see the live pricing page
Playbook supportDeviation checks against your standard positions
Access controlRole-scoped retrieval for legal and privileged content

TL;DR

  • Keep your OpenAI SDK — change the base URL and model name.
  • 30+ models behind one API, from free chat models to frontier reasoning.
  • Deployment options from hosted cloud to VPC, on-prem and air-gapped.
  • Keep privileged material in its own collection and keys.
  • Agents triage and summarise; counsel approves.

How it works, step by step

  1. Define the job, the permitted data sources and where a human must approve.
  2. Start with policy Q&A, then playbook-based contract triage.
  3. Create a Plugsky account and generate an API key (free plan, no card required).
  4. Point your OpenAI SDK at the Plugsky base URL and map your model names.
  5. Index the approved corpus with embeddings and keep retrieval role-scoped.
  6. Define which outputs require counsel approval before launch.
  7. Measure quality on your own samples, then scale with usage monitoring.
1Define the job, thepermitted datasources and where a2Start with policyQ&A, thenplaybook-based3Create a Plugskyaccount andgenerate an API key4Point your OpenAISDK at the Plugskybase URL and map5Index the approvedcorpus withembeddings and keep6Define whichoutputs requirecounsel approval

Try it yourself

Open the AI data residency checklist →

Legal teams do not lack ideas for agents; they lack a safe path from demo to production. The pattern below targets repetitive, document-heavy work where a human can check the output, which is where agents earn their place first. Treat the agent as a new team member with a narrow brief, explicit permissions and a probation period, and rollout becomes an operations exercise rather than a leap of faith.

  • Contract review — compare incoming paper against your playbook and flag deviations
  • Policy Q&A — answer business questions from approved policies
  • Obligations — extract dates and duties from agreements into a tracker
  • Regulatory summaries — digest updates you supply and map them to internal owners

A review agent compares contract text to your standard positions and lists exceptions, while a Q&A agent retrieves policy answers with citations. Counsel approves anything that binds the company or changes a position.

  1. Business-unit scoping for retrieval
  2. Tools into CLM, ticketing and document stores
  3. Playbook configuration and deviation reports
  4. Review gates for obligations and external communications

Data governance and human oversight

Privilege and confidentiality drive the design. Keep legal content in a restricted collection, limit access by role, log queries, and make clear internally that agent output is not legal advice until counsel adopts it.

  • Role-scoped access to legal collections
  • Audit logging of queries and tool calls
  • Review before obligations are recorded
  • Retention and deletion per matter type

From pilot to production

Start with policy Q&A for the business and playbook-based contract triage, then extend to obligation extraction once precision is measured against counsel review.

Keep the rollout reversible: run the agent in shadow mode alongside the current process, compare outputs on your own samples, and move it into the workflow only when the evidence holds. Document what you measured so expanding to the next team is a decision, not a hope.

Honest comparison

CapabilityPlugskyTypical cloud AI APIBuilding in-house
API compatibilityDrop-in base URL changeUsually compatibleFull rewrite
Model access30+ models behind one APIVendor's own catalogueYou host each model
PricingFlat monthly self-serve plans; see live pricingOften per-tokenGPU + ops cost
DeploymentCloud, VPC, on-prem or air-gappedUsually vendor cloud regionsYou own the stack
Privileged workSeparated collections and keysVariesYou configure
ApprovalsCounsel-owned; agents prepareVariesYou enforce it

Frequently asked questions

Do we have to rewrite our application?

No. The chat completions API is OpenAI-compatible, so you change the base URL and model name and keep your existing SDK.

Is there a free plan?

Yes — the free plan includes two free AI models, plugsky-micro and plugsky-lite, with no credit card required.

How is pricing structured?

Self-serve plans are flat monthly with fair-use usage and no per-token charges; see the live pricing page for current plans.

Which endpoints are live today?

Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, fine-tuning, assistants and responses endpoints are coming soon — check the docs before planning around them.

Can the agent approve contracts?

No. It can review against your playbook and flag deviations; only authorised counsel should approve or execute.

Can business teams use it directly?

Yes, for policy questions and first-pass triage, with clear guardrails on what the agent may say and an escalation path to counsel.

How do we keep privileged material separate?

Use dedicated collections and keys for privileged work, and keep them out of broader business-facing retrieval.