A Secure Generative AI Assistant for Legal Research, Drafting and Contract Review

Industry

Legal

Services

Generative AI DevelopmentRAG DevelopmentWeb Application Development

Technologies

PythonLangChainAnthropic ClaudeOpenAI GPT-4oAzure OpenAIPineconeReactAzure

Challenge

A legal services group with multiple practice areas wanted to give its lawyers the benefits of generative AI without exposing confidential client data to public tools.

Lawyers were already experimenting with consumer chatbots, creating risk, and the firm's knowledge was spread across document management systems, precedents and emails:

Legal research and first drafts consumed a large share of fee-earner time.
Consumer AI tools gave answers without reliable citations and risked confidentiality.
Precedents and know-how were hard to find across systems and teams.
Contract review against the firm's playbooks was slow and inconsistent.

The firm needed a secure, firm-wide AI platform designed specifically for legal work.

Solution

Winklix designed and delivered a private legal AI platform running in the firm's own cloud tenancy, built around retrieval-augmented generation and strict access controls.

Secure foundation

The platform runs on Azure with private model endpoints, single sign-on, matter-level permissions and full audit logs, so no client data is used to train public models.

Research & Q&A with citations

A RAG pipeline indexes precedents, internal know-how and approved legal sources. Every answer links back to the exact passage it came from, so lawyers can verify it quickly.

Drafting workspace

Lawyers generate first drafts of letters, clauses and memos from the firm's templates and house style, then edit them in a familiar document-style editor.

Contract review

Uploaded contracts are compared against the firm's playbooks. Deviations are flagged with suggested redlines and plain-English explanations.

Legal Services Group solution delivered by Winklix

The Approach

Workflow first — We mapped the real workflow with the people doing the work before choosing models or tools.
Human in the loop — Every AI output had a clear review point, confidence thresholds and an escalation path to a person.
Prototype, then scale — A working pilot on real data proved the value before the solution was rolled out more widely.
Measured from day one — Baseline metrics were captured up front so improvements could be tracked objectively.