What changes with governed AI in legal
In legal, an answer with no source doesn't save time: it creates risk. The line between a useful agent and a liability is simple. It answers only from material the team approved, in the version that's currently in force, and shows where each clause or position came from.
Contextfy is the layer that makes this possible. Instead of dumping the whole library into a model and hoping, you define which template contracts, clauses, opinions and policies are approved, who can query each collection, and which version is official. The agent operates inside those limits.
The business gain shows up early: fewer lawyer-hours spent hunting for the right draft, less rework on review, faster onboarding of associates, and confidence that nothing confidential leaks outside the scope of whoever asked. And when someone questions an answer, there's a trail to show.
The pain of running legal knowledge
Legal knowledge lives scattered: drafts in network folders, clauses copied from old contracts, opinions buried in the inbox of whoever signed them, policies that changed but were never updated in the repository. When a question arrives, the right answer depends on knowing who to ask.
Dropping a generic chatbot on top of that library usually makes it worse. It answers confidently using a revoked clause, mixes one client's template with another's, or cites a position the firm has already abandoned. Worse: there's no way to know afterward where the answer came from.
Add confidentiality. One client's material can't surface for another's team. Personal data in a contract falls under data-protection law. An agent with too much access turns every query into potential exposure, and legal is precisely the function that can least afford that.
Risks of AI without governed context in legal
Without a governance layer between the sources and the agent, the sector's typical risks pile up:
- Outdated clause. The agent cites a revoked wording or an old version of the draft, and the recommendation goes into a live contract.
- Confidentiality breach. Material from one client or a restricted practice surfaces for someone who shouldn't reach it, with no scope barrier.
- Conflicting sources. Templates from different clients blend in the answer because there's no separation by collection.
- Answer with no origin. When the partner or an auditor asks where it came from, there's no way to point to the source, version and approver.
- Personal data exposure. Sensitive contract information circulates without control, creating exposure under data-protection law.
- Abandoned position. The agent argues a view the firm has already revised, because the old material was never removed from the base.
Where Contextfy fits
Contextfy sits between the legal library and whatever agent the team chooses to use. Sources come in as drafts and only become official after an owner approves them, separating working drafts from material cleared for consultation.
Each collection has scope: one client's contracts are visible only to those working on it, internal policies to the in-house team, public templates to everyone. Permissions apply when the answer is served, not just in the interface. When there's no approved source to support the question, the agent declines instead of inventing.
Delivery happens over REST, MCP or connectors, so legal uses the assistant it prefers without rebuilding the base on every switch. And every query leaves a record in the Evidence Log with a traceId: the question, the sources used, the active version and the answer stay recoverable for internal review or audit.
Fontes
Drive, SharePoint, ERP, CRM, PDFs, APIs
Contextfy · Context Engine
Organiza · versiona · governa · observa o contexto
Runtimes
via MCP · API · conectores · pipelines
Typical sources in a legal library
These are the sources that usually feed agents in legal, each with its own approval state and scope:
Template contracts and drafts
Approved templates by deal type, with the in-force version clearly marked.
Clause library
Standard clauses and their variants, with current wording separated from revoked ones.
Opinions and memos
Legal positions already validated internally, with author and date, without drafts still under discussion.
Internal policies and rules
Codes of conduct, privacy policies and procedures, restricted to the right audience.
Negotiation playbooks
Limits, fallback positions and non-negotiable clauses by contract type.
Regulatory and reference base
Reference material on data protection, sector regulation and rules the team needs to consult.
How to start in legal
The path isn't to connect the whole library at once. It starts with one concrete, painful process: querying template contracts, supporting clause analysis, or answering on internal policies. You pick one practice area, two to four sources and an owner for curation.
The Diagnostic maps where the knowledge lives, which sources are ready, where scope is ambiguous, and which gaps need to be covered before any agent reaches production. From it comes a pilot plan with clear scope and success criteria.
Because the context layer is independent of the assistant, the pilot doesn't lock the firm to one vendor: the governed base built there serves any agent later. You measure refusals, most-used sources and gaps before expanding to more areas.
Frequently asked questions
How does Contextfy keep confidentiality between different clients?
Each library goes into collections with their own scope, and the control applies when the answer is served, not only on screen. One client's material is visible only to those working on it; an agent queried by another team simply can't reach that source. There's no blending of origins in the answer.
Can the agent cite a revoked clause by mistake?
The versioning layer exists to prevent exactly that. Each source has an official version, and revoked wordings are separated from what's in force. The agent answers from the active version, and the Evidence Log records which version supported each answer for later review.
Does this help with data-protection compliance?
Contextfy provides the controls that support personal-data governance in legal: scope by collection, permissions applied at serving time, separation of approved material from drafts, and a trail of who queried what. We don't sell compliance certification; we deliver the governed-context infrastructure that makes such control demonstrable.
What happens when there's no approved source for the question?
The agent declines honestly instead of improvising. Without approved material to support the answer, it flags the missing context. For legal that's an advantage: no answer beats a confident, wrong one about a clause or a position.
Do we have to replace the AI assistant we already use?
No. Contextfy governs the context and delivers it over REST, MCP or connectors; the agent that executes is the team's choice. You can use your current assistant and switch later without rebuilding the governed-context base.
How do we prove where an answer came from in an internal audit?
Every query leaves a record in the Evidence Log with a traceId: the question, the sources used, the active version and the answer. When a partner, compliance or an audit asks why the agent answered that way, there's a trail to show source, version, scope and permission.
Keep exploring
Free diagnostic: we map the legal library, the confidentiality risks and a pilot plan with controlled scope.
Assess AI with governance for legal