Industry · Legal
A confident fabrication ends a career.
Legal is the vertical where a confident fabrication ends careers. Every answer we build returns the passage it came from, and refuses when the corpus doesn't cover the question.
- Generative AI & RAG
- AI Agents
- Strategy & Governance
Why now
In-house teams are asked to review more contracts with the same headcount, and firms are under fee pressure on exactly the work that is most repetitive. Review, extraction and first-pass drafting against a known playbook are where AI is genuinely useful — and where hallucination is most expensive, which is why grounding is non-negotiable.
The problem
Where legal teams lose time today.
- 01
First-pass review is a bottleneck
NDAs and vendor agreements queue behind higher-value work, and the business routes around legal to keep moving.
- 02
Precedent is trapped in the DMS
The clause you need was drafted two years ago on a matter nobody remembers the name of.
- 03
Obligations aren't tracked after signature
Renewal dates, notice periods and reporting obligations live in executed PDFs and surface only when missed.
Constraints
What makes this harder than a generic chatbot.
- 01
Every claim carries its source
Answers return the clause and the document, and refuse when retrieval is weak. An unsourced legal answer is worse than no answer, and the eval suite scores it as a failure.
- 02
Privilege and confidentiality are architectural
Matter-level access control in retrieval, deployment inside your tenancy, and no training on your documents — stated in the contract, enforced in the build.
- 03
The lawyer signs, always
The system drafts, redlines and flags. Advice and filing stay with an admitted practitioner, and no pathway lets generated text reach a counterparty unreviewed.
Integrations
Systems we wire into.
- iManage & NetDocuments
- SharePoint
- DocuSign & Adobe Sign
- Contract lifecycle management tools
- Microsoft 365 & Word add-ins
- Postgres with matter-level access control
Services
What we build for legal.
- RAG for legal documents and precedentRetrieval across matter documents, executed contracts and internal know-how — every answer returning the clause, the document, and the matter it came from.Read
- AI agents for contract reviewAgents that triage incoming contracts, redline against your standard positions, and route to the right reviewer with the deviations already summarised.Read
- AI governance for legal teamsAcceptable-use policy, confidentiality controls, and an approval process for AI tools that lawyers are already using with or without permission.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in legal: the questions we get.
Do you train models on our documents?
No. Documents are indexed for retrieval in your own infrastructure, and we use providers whose terms exclude training on API inputs. It's in the engagement contract, not just the sales deck.
How do you handle privilege?
Matter-level access control in retrieval, mirroring your DMS permissions, plus an access log for anything our engineers can see during the engagement. Where the risk appetite requires it, we work only against a de-identified sample.
Can it draft, or only review?
It drafts first passes from your own precedent — which is the useful kind of drafting, because it produces documents that look like your firm's rather than a model's average of the internet.
Contact
Talk to us about AI in legal.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read