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Your Associates Are Drowning in Document Search While Your Margins Evaporate

If you're a managing partner watching billable hours vanish into clause searches, you're funding the problem AI already solved.

Your associates spend 4 hours searching for precedent clauses across thousands of contracts. Your intake team qualifies leads over 3 email exchanges. Your billing descriptions are written at 11pm from memory. We build a RAG system over your document library -- 10,000 contracts ingested into pgvector. A lawyer asks show me all non-compete clauses from our pharma client contracts in 2026 and gets results with citations in seconds.

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Legal AI Integration

Legal AI integration is the process of embedding large language models and semantic search directly into a firm's existing document workflows, practice management systems, and client intake pipelines. Rather than replacing attorneys, it eliminates the low-skill retrieval and drafting tasks that consume associate time and inflate write-offs. The result is a system trained on your own precedents, operating inside your security perimeter, and connected to the tools your team already uses.

What is holding your current website back?

Common gaps we find in nearly every audit.

Associates spend three to four hours per matter searching across thousands of contracts for a single relevant clause, often duplicating work a colleague completed last quarter.
Risk: That unrecoverable time is either written off to keep clients happy or billed and challenged, compressing margins on every transactional matter the firm takes on.
Intake coordinators run three to five email exchanges before determining whether a prospect meets the firm's matter criteria, leaving partner time consumed by leads that never convert.
Risk: High-value prospects disengage during the delay and retain a competitor who responded with a qualified answer the same day.
Billing descriptions are reconstructed from memory at the end of the day or week, producing vague entries that clients dispute and auditors flag during rate reviews.
Risk: Write-downs on billing disputes compound across a full associate roster into a material annual revenue gap that never appears on a single invoice but accumulates across hundreds.

How We Build This Right

Every safeguard, built in from Day 1.

Attorney-Client Privilege Protected

All document ingestion and query processing runs within your own cloud tenancy or on-premises infrastructure. No contract data transits third-party model APIs in a form that could constitute a waiver of privilege or breach client confidentiality obligations.

Role-Based Access Controls

Document access within the RAG system mirrors the permission structure already defined in your practice management platform. Associates retrieve only what their matter assignments entitle them to see, and audit logs capture every query for conflict and ethics review.

Bar Ethics Rule Alignment

The system is scoped and documented to satisfy competence and supervision obligations under Model Rules 1.1 and 5.3. Attorneys review and approve all AI-assisted drafts before transmission, and the workflow produces a clear record of human oversight at each step.

What We Build

Purpose-built features for your industry.

Semantic Contract Search over Your Entire Library

Your document corpus is chunked, embedded, and stored in pgvector. Associates query in plain language — 'indemnification carve-outs in SaaS agreements governed by Delaware law' — and receive ranked clause results with source document citations in under three seconds, regardless of how the original drafters worded the provision.

Automated First-Draft Generation from Precedent

When an associate opens a new matter, the system retrieves the three most relevant precedent documents from your library and generates a first draft pre-populated with your firm's standard language. Deviations from house style are flagged inline before any attorney reviews the document.

Intake Qualification on First Contact

A structured AI intake flow collects matter type, jurisdiction, opposing parties, and conflict information in a single client-facing session. The system scores the lead against your defined acceptance criteria and routes qualified matters directly to the responsible partner with a one-page summary, eliminating the multi-email qualification sequence.

Clio and Smokeball Native Integration

Matter data, contact records, and time entries sync bidirectionally with Clio and Smokeball through their published APIs. Billing descriptions are drafted automatically from time-entry notes and matter context, surfaced for attorney approval inside the practice management interface your team already operates in.

Built on a Modern, Secure Stack

Claude APIpgvectorSupabaseVercelClio APIResend

Our Development Process

From discovery to launch. Quality at every step.

01

Document Audit and System Scoping

1 week

We inventory your existing document library, practice management configuration, and intake workflow to define the ingestion scope, access control requirements, and integration touchpoints before any code is written. You receive a written technical specification and a fixed project scope.

02

Corpus Ingestion and Embedding Pipeline

2 weeks

Your contracts, templates, and precedents are parsed, chunked by clause structure, embedded using a privately hosted model, and loaded into a pgvector instance running inside your designated cloud environment. Ingestion of up to 10,000 documents completes within this phase.

03

Application Build and Practice Management Integration

2 weeks

We build the search interface, draft generation workflow, and intake qualification flow, then connect each to Clio or Smokeball via API. Role-based permissions are mapped from your existing user directory. Internal testing runs against real matter types drawn from your practice areas.

04

Supervised Rollout and Performance Baseline

2 weeks

The system goes live with a defined group of associates and intake staff. We track retrieval accuracy, draft acceptance rates, and intake conversion over the first four weeks, then tune retrieval parameters and prompt templates based on observed usage before handing over to your team.

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Frequently Asked Questions

Yes. We ingest your documents into pgvector embeddings so the system understands meaning, not just words. Search for "non-compete clauses" and it surfaces documents that say "restrictive covenant" or "post-employment restriction" too -- because semantically, they mean the same thing. That's what makes this genuinely useful instead of just faster keyword search.
Yes -- and this comes up with every firm we talk to, for obvious reasons. All data stays in your infrastructure. The Claude API processes your queries in memory and doesn't retain your documents. Nothing gets used for model training. Audit logs are available for privilege review, and we can implement document-level access controls so not everyone sees everything.
We've built RAG systems with 10,000+ documents, and honestly the scale question comes up constantly. Here's the thing: 50,000 documents perform the same as 500. Search speed stays under 2 seconds regardless of library size. The pgvector architecture handles it without degradation.
A contract RAG system with semantic search starts at $15,000-$25,000 depending on document volume. The full suite -- intake automation, billing description generation, document drafting -- runs $35,000-$60,000. Most firms recover that in billable hours within 2 months. That's not a sales line, that's what the math actually shows.
Clio, Smokeball, PracticePanther, MyCase, custom-built systems. On the document management side: SharePoint, NetDocuments, iManage. If your system has an API, we connect to it. And if it doesn't have a clean API, we've usually found a way anyway.
Every AI response cites the specific document and passage it pulled from, so attorneys can verify before relying on anything. And we deliberately tune retrieval parameters for precision over recall -- 5 highly relevant results beat 50 vaguely related ones every time. Accuracy keeps improving as attorneys flag what's useful and what isn't.
Law firms utilize various AI tools to enhance their operations, including platforms like ROSS Intelligence for legal research, Kira Systems for contract analysis, and Lex Machina for litigation analytics. Additionally, tools such as Luminance and eBrevia assist with document review and due diligence. These technologies help law firms improve efficiency, reduce errors, and make data-driven decisions. As AI continues to evolve, its integration into legal practices is becoming increasingly prevalent, reshaping how legal services are delivered.
The "30% rule" for AI in legal contexts refers to the guideline suggesting AI can automate up to 30% of tasks within a particular job or industry without significant disruption. In legal practice, this means AI can efficiently manage tasks like document review, legal research, and contract analysis, enhancing productivity and allowing human lawyers to focus on more complex, strategic work. This rule underscores the balance between automation and human expertise, ensuring AI supports rather than replaces legal professionals.
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