Port of Context for Law Firms

Every matter your firm has ever handled, legible to agents.

Port of Context builds agent execution infrastructure for law firms. Matters, precedents, and decades of the firm’s judgment, made legible to AI. That takes engineering, not improvisation.

your agent

Your ontology[ meaning ]

How your firm’s knowledge interconnects, so agents read a matter the way your partners do.

Your taxonomy[ structure ]

How your firm organizes its work: practice groups, matter types, and document classes, in its own terms.

Your deep data[ source ]

Decades of your firm’s most sensitive records, down to every brief, memo, and closing set.

Live at Prudential Financial and Block.

A $7B investment firm runs 50+ custom agents on Port of Context.

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Diligence and review

Triage every data room and document set without drowning in files.

A diligence request arrives as thousands of contracts, disclosures, and correspondence. Port of Context engineers agents that read all of it before your team opens a folder.

Diligence & Review

Precedent and work product archive

Every document your firm has ever produced, cleaned up, sorted, and queryable.

Briefs, agreements, memos, and closing sets, scattered across decades of matters and lateral hires. Port of Context reads every file and sorts it into one structured index your team can query.

Put stress-tested agents on top of the tools you already use.

Don’t force new workflows and UI onto your team. Run agents on the tools your team is already used to.

ExcelOutlookSharePointTeams
iManageNetDocumentsRelativitySalesforce
SnowflakeBoxDocuSign
Port of Context
Your agent, on the model you choose
ClaudeChatGPTGeminiLlamaMistral

How it works

Engineers build the foundation. Your team builds on top.

Resilient agents come from disciplined foundations.

  1. 1

    Clean the data at its source

    We index all of your deep data so that it can be accessed by agents with proper controls and tooling.

  2. 2

    Build a custom MCP server over it

    We tune the server the exact way agents need to read and query it.

  3. 3

    Eval rigorously

    We build evaluation cases on your actual workflows and keep refining them, so each agent proves it executes properly before it ever reaches production.

  4. 4

    Your firm builds agents in one prompt

    With the MCP server in place, your own team can now reliably build production-grade agents on top.

Map Port of Context to your environment.

We'll look at your infrastructure, models, and compliance constraints, then tell you honestly whether Port of Context fits. A real engineer follows up fast.

Book a demo

We'll reply from a real person, not an automated sequence.

One line is plenty. It helps us map Port of Context to your environment before we talk.

A real person from our team replies within one business day, usually faster.
No sales sequence. Just a technical conversation.

Your details are used only to prepare for and respond to this conversation. See our privacy policy.

FAQs

What is the difference between an ontology and a taxonomy?
A taxonomy is how a firm categorizes its data in its own terms. It tells an agent where things belong. An ontology defines how those categories relate to each other and encodes the specific logic the firm uses to put its data to work. An agent needs both. The taxonomy tells it where to look, and the ontology tells it what the data means once it gets there.
What is agent execution infrastructure?
Agent execution infrastructure is the layer that lets AI agents run on a firm’s systems and data in production. It covers how agents read and query the data, which tools they can call, how their access is controlled, and how every run is traced back to its sources. Port of Context builds this layer for regulated firms on top of the tools they already use.
How do AI agents handle matter files and privileged documents?
Badly, unless the data is prepared first. A matter file arrives as pleadings, correspondence, drafts, and exhibits with no shared structure, and an agent pointed at that raw pile will miss things. The reliable approach is to index the material at its source, build a custom MCP server over it, and give agents controlled, queryable access that respects ethical walls and privilege boundaries. Every answer then traces back to the underlying document, so your team can verify what the agent found before it reaches work product.
Are we locked into one AI model or vendor?
No. The infrastructure is model-agnostic. Agents run on Claude, GPT, Gemini, or open-weight models, and the firm can switch when a provider changes pricing, degrades, or gets ruled out by policy. The data layer, the MCP server, and the ontology work stay yours, so a model change never means rebuilding the foundation.
Do we need to replace the tools our team already uses?
No. We build on top of the tools your firm already runs, including Excel, Outlook, SharePoint, and document systems such as iManage, NetDocuments, and Relativity. Agents reach those systems through the MCP server, with access controls in place. Your team keeps working where it already works, and the agents operate on the same data.
What does it take for our own team to build an agent?
A prompt, once the foundation is in place. The engineering happens up front, in cleaning the data at its source, building the MCP server, and writing evaluation cases against your actual workflows. After that, someone on your team describes the agent they need and it runs on infrastructure that is already controlled and traceable.