Legal Document Automation Solutions Development Services

Buy the product when your need is general. Harvey, CoCounsel, Spellbook, and LegalOn are built for broad legal work across thousands of firms; they are cheaper to start, somebody else maintains them, and for general research and first-pass drafting they will serve you better than anything custom. If that is your situation, we would rather you reach it before paying us.

They stop fitting at a predictable point: when the answer has to come from your own matters and clause libraries, when the integration with your DMS and practice management stack has to be deep rather than nominal, when a client or regulator asks where a document went, or when your confidentiality obligations will not permit a third party to hold the corpus at all. Custom AI for law firms is built around your data, your workflows, and your compliance requirements — your firm owns the system and controls the roadmap.

The other difference is data control. With a SaaS product, your documents pass through a third-party platform. With a custom system using RAG for legal, the AI retrieves answers from your own infrastructure and nothing leaves your perimeter.

Retrieval-Augmented Generation changes how the AI generates responses. Instead of relying on what the model "remembers" from training data, RAG forces the system to first retrieve relevant documents from your own database — contracts, case files, internal memos — and then generate a response grounded strictly in that retrieved content. Every answer includes source citations so lawyers can verify the basis before relying on it.

This is why generative AI for legal built on RAG architecture is fundamentally different from prompting ChatGPT. The model can't invent precedents or fabricate clauses because it's constrained to your verified source material.

Cost depends on the scope, the number of document types you need to process, the integration complexity, and whether you need an on-premises deployment.

As a general guide:

• A focused MVP covering one workflow (such as AI contract review or document summarization) typically runs $30,000-$70,000.

• A mid-range system with multiple AI agents, DMS integrations, and compliance features runs $70,000-$180,000.

• An enterprise platform with on-premises deployment, multi-jurisdiction support, and full practice management integration ranges from $180,000 to $400,000+.

We provide a detailed cost estimate after a scoping call. No obligation.

An MVP targeting a single legal workflow is typically live in 3-5 months. A mid-range system with RAG, DMS integrations, and compliance infrastructure takes 5-9 months. A full enterprise platform with on-premises deployment, multiple AI agents, and multi-jurisdiction regulatory monitoring runs 9-14 months. A working proof of concept lands inside the first sprint, on a slice of your own documents rather than a demo corpus. We built our own production agent in four weeks with one developer.

Yes. We integrate with document management systems, including iManage, NetDocuments, and SharePoint; practice management platforms, including Clio, PracticePanther, and Smokeball; e-signature platforms, including DocuSign and Adobe Sign; billing systems; and CRM platforms. For LegalFly, we built a unified API connecting to official government portals across multiple countries. If your tool has an API, we build the integration.

We build to the compliance requirements your firm and clients demand. That includes SOC 2-aligned security architecture, GDPR-compliant data models with audit logs and right-to-erasure support, HIPAA alignment for healthcare-adjacent legal work (we conducted compliance discovery for MedTech client Biolux to prepare for HIPAA certification), CCPA, and ABA Model Rules on technology competence and confidentiality. Every system includes role-based access control, encryption at rest and in transit, and full audit trails.

Yes. We build fully air-gapped, on-premises AI legal systems with zero external API dependencies. This means your documents never leave your servers, no data is sent to third-party model providers, and the entire inference pipeline runs within your network. We support locally hosted open-source models (Llama, Mistral) and commercial models deployed in your private cloud. For firms that don't require full air-gapping, we also deploy on AWS, Azure, or GCP with encrypted data handling.

Attorney-client privilege protection is built into the architecture, not added as an afterthought. The system enforces strict data segregation between matters and clients so no AI response can cross-reference privileged information from one engagement into another. Role-based access ensures only authorized users see specific documents. All queries and responses are logged in tamper-proof audit trails. No client data is used for model training or fine-tuning - the RAG system retrieves and cites, it doesn't learn from your documents.

No, and the distinction matters more than it sounds. Retrieval and training are different operations. A RAG system reads your documents at query time to ground an answer, then forgets them; it does not adjust model weights, and nothing about your matters becomes part of the model. Where we use a commercial model, we deploy it in your own tenant with training and retention disabled at the contract level, and we name every external service in scope before it is connected. Where that is not acceptable, the whole pipeline runs on locally hosted open models and there is no external call to disable.

Every retrieval is logged with the query, the documents returned, the passages used, the model that generated the response and the user who asked. That log is append-only, so it can be produced months later for a client audit, a regulator, or opposing counsel. Answers carry inline citations to the source document and passage rather than a general reference, which is what lets a fee earner verify in seconds instead of re-reading the file.

This is the part most legal AI projects leave until last. We build it in the first milestone, because a system that cannot show its working is one your risk committee will not approve.

After deployment, we provide ongoing monitoring, security patches, model updates, and feature development. 80% of our clients stay for 7+ years - our LegalFly engagement is an ongoing partnership where we continue to add jurisdictions and data sources. We offer 1-hour incident response for critical issues and structured quarterly reviews to plan new capabilities as your firm's needs evolve.