SoluLab is a legal tech AI MVP development company for founders building contract review, legal research, e-discovery, and compliance AI. We engineer for citation accuracy and hallucination control from day one — backed by a team that has shipped generative AI into regulated banking and clinical healthcare environments, where a wrong answer has real consequences.
SoluLab was co-founded by a former Goldman Sachs Vice President and a former Citrix Principal Software Architect. That combination — capital-markets risk discipline paired with enterprise software architecture — is why "the model said so" is treated as an incomplete answer on every project.
Managed front-office interest rate derivatives, where a modeling error carries immediate, measurable financial consequences. M.S. Computer Science, Georgia Tech.
Led enterprise mobile platforms used by millions of people daily — the enterprise-grade software architecture discipline SoluLab still builds on.
Law firms didn't reject early AI tools out of caution alone — they rejected them because hallucination rates were genuinely too high for professional use. That history is exactly why "unauthorized practice of law" and "AI hallucination" are live search terms among legal AI buyers today, not hypothetical concerns.
Grounding techniques — retrieval-augmented generation over verified source documents, citation-level verification, and human-in-the-loop review gates — matured enough to bring hallucination rates down to production-viable levels for legal-grade use cases. Our entire build philosophy for legal AI is built around that shift.
We haven't shipped a named legal AI platform yet — and we're not going to pretend otherwise here. What we have done is build generative AI inside two of the least forgiving environments for AI accuracy: regulated consumer banking and clinical healthcare decision support. The engineering discipline is the same one legal AI demands.
AI-powered chatbots and voice assistants automating onboarding and customer support inside a compliance-bound financial environment.
Multimodal reasoning (text + imaging) with graph-based logic to improve decision accuracy in symptom triage and patient education, under strict healthcare safety rules.
Published as an academic case study — Springer NatureSame core discipline legal AI requires: constrained, grounded outputs; explicit accuracy/safety measurement; human oversight built into the workflow, not bolted on after a demo.
Clause extraction and risk flagging, traceable to the exact source clause.
Retrieval-augmented case law search that returns citations, not summaries.
Drafting assistants grounded in firm precedent and playbooks.
Large-scale review and classification built for defensibility.
Regulatory monitoring grounded in the specific regulatory text.
Case outcome analytics with confidence bands, not false certainty.
Prior-art analysis and portfolio workflows with source-linked outputs.
AI-assisted intake that flags conflicts before a human has to.
[Dev note: render live Clutch & GoodFirms embeddable badge widgets here, not static images, so scores stay current.]
We'll talk through your use case, your accuracy and compliance requirements, and what a scoped MVP timeline and estimate actually look like — no generic AI-agency pitch.
Book a Technical Discovery Call
