Legal AI uses large language models and retrieval to research case law, review contracts, draft documents, and deliver legal guidance, with citations on every answer. We built LAWEP, an AI legislative drafting system, and Inpro, an AI consultation platform for consumer legal guidance. This page covers how the legal industry uses AI, with examples from systems we delivered.
Four patterns we have shipped for legal organizations:
Four patterns we have shipped for legal organizations:
Live dashboards and pipelines turn scattered operational data into one picture your team can act on, exportable for leadership.
Every system we deliver runs on infrastructure your team can own and extend, no black boxes, no per-seat licensing.
GPT-4 & GPT-4 Turbo · Claude 3.5 Sonnet · Llama 3.1 & Mistral · Azure OpenAI · AWS Bedrock
Pinecone · Weaviate · pgvector · Westlaw / LexisNexis APIs · Citation engines
LangChain · LangGraph · LlamaIndex · Custom Python · Evaluation test suites
Docker/Kubernetes · AWS/GCP/Azure · SSO (Okta, Azure AD) · End-to-end encryption · Designed with SOC 2 guidance, hosted on AWS or Google Cloud
CI/CD pipelines · Logging & metrics · Error tracking · Uptime monitoring · Automated backups
The models, vector stores, data tooling and infra behind every build.
Four patterns dominate in production: legal research with citations over a firm's own matters plus external case law, contract review against a playbook, document and legislative drafting from templates, and AI-powered consultation for routine legal guidance. We have shipped all four, including LAWEP for legislative drafting and Inpro for consumer consultation.
Only if it is built for that. Our legal AI systems retrieve from indexed sources and cite the exact matter, clause, or case behind every answer. If the answer is not in the available data, the system says so rather than inventing a citation. We validate against an evaluation set with ground-truth answers before launch.
It can be, with the right architecture. We deploy in your own cloud tenant, on-premise, or air-gapped, with end-to-end encryption, role-based access, and full audit logging. Your documents never train anyone else's model.
From our delivered systems, as reported by the clients: LAWEP drafted 15 bills in 6 months, and Inpro handled 50,000+ consultations in its first year. For your firm, we measure research and review time against your own baseline before launch and do not promise a figure up front.
Products like Harvey or Spellbook are fast to adopt but generic: they do not know your matters, playbooks, or jurisdiction, and you pay per seat forever. A custom build costs $75,000 to $175,000 once, ships in 4 to 5 months, and you own the code. The right answer depends on how distinctive your workflows and data are.
“EdgeFirm brought Sokrateque.ai to life: advanced multi-agent systems, Power BI wired to LLMs, and a seamless data pipeline that exceeded our expectations.”
“EdgeFirm was key to building LAWEP.AI's legal AI platform: powerful data pipelines, accurate OCR, and custom LLM fine-tuning, delivered with speed and precision.”
“These guys were fantastic. Above and beyond, affordable, and fast. I'm 100% working with them again for updates and new projects. Highly recommended!”
“There's not enough words to describe Zain's team. They take pride in their work and always over-deliver. A trustworthy, product-minded agency.”
“Loved working with Faisal, Usman, and the team. An interesting API integration project, and they figured everything out. I'll definitely work with them again.”
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