Lawrence AI · Legal technologySince 2024
Lawrence AI: from a tested AI core to a platform ready for lawyers
Lawrence AI had a legal AI core. We built the SaaS product around it: interface, backend, multi-agent retrieval and infrastructure, with the client's team.
Project facts
Every engagement rests on a few checkable facts.
- Our role
- Co-development with the internal team
- Industry
- Legal technology
- Technology
- Node.js · React · Next.js · Multi-agent LLM orchestration with RAG · Infrastructure as code (GitOps)
- Services
- AI agents & automation · Custom software development
A legal assistant that existed as a core, not yet as a product
Lawrence AI is a virtual legal assistant for lawyers, law firms and corporate legal teams. A user selects the statutes that apply, uploads their own documents, such as contract templates, legal opinions or court decisions, and asks questions. The answer has to stay grounded in those sources.
When we joined, the AI core was written and tested. What did not exist was everything a paying user touches: the interface, the backend, the infrastructure and the routine of running it every day. Our task was to review and stabilize the core, then build the production SaaS platform around it.
What a lawyer expects from the answer
- It cites the statutes and documents the user chose, not the open internet
- One workflow serves a sole practitioner, a law firm and an in-house team
- The product runs every day, not as a demo
What the platform had to hold together
- Agents for documents, databases, internet sources and external functions
- An interface, backend services and infrastructure that grow with the user base
- Delivery alongside the internal developers, so the knowledge stays with the client
The core stayed, the product grew around it
We started with analysis and the UI/UX design, then the application architecture. The backend runs on Node.js; the interface is built in React and Next.js. Around the core we built a multi-agent system in which each agent type handles one kind of source. A question about a statute, a query against a database and a search on the internet each follow their own path.
Before the model answers, retrieval-augmented generation (RAG) collects the relevant passages and a preprocessing step prepares them. The model works from what the user selected. That is what keeps the answer grounded.
The infrastructure is described as code and deployed through GitOps: version-controlled configuration drives every deployment. The platform runs on servers in the Czech Republic, with security standards applied from the first release.
Co-development, not a handover at the end
Lawrence AI’s developers worked in the same codebase with us throughout. Decisions about the architecture, the agents and the deployment were made together, so the team can operate and extend the platform without waiting for us.
Security and growth were decided before launch
Infrastructure as code, automated deployments and security standards were part of the build, not a later phase. The platform was prepared to take on more users and more source types without a redesign.
A platform ready for its first users, and a team that owns it
The engagement produced a working SaaS platform: the interface, the backend, the multi-agent retrieval architecture and the automated infrastructure underneath. Lawrence AI’s team holds the implementation and maintenance knowledge and keeps developing the product. The collaboration continues.
Similar work, a different starting point
A description in your own words is enough to start.
Describe what is holding you back. Within two business days we get back to you with a first practical step.