Contract review assistant
Find and compare clauses across large contract sets.
- 1Extracts key clauses
- 2Flags deviations from your playbook
- 3Links each finding to its source
New York, NY · Generative AI Engineering
LLM applications for New York teams that answer from your own documents, cite their sources and are tested before anyone relies on them.
Generative AI Engineering in New York
New York runs on regulated, document-heavy work: onboarding clients, pricing risk, handling claims and managing leases. It is where AI has the most work to do, and the highest bar for control.
Where it fits
Find and compare clauses across large contract sets.
Cited answers from research, policies and filings.
First drafts in your house style, reviewed by people.
Built for New York rules
Access controls, MFA and audit trails on every system AI touches.
Reasonable safeguards for the private data AI systems read and write.
Bias audit and candidate notice for tools that score or rank candidates.
Safeguards and a business associate agreement for anything touching patient data.
Working together · Eastern Time
Your morning is our evening, so we meet live at the start of your day and build while you sleep.
How we work
We agree what a good answer looks like and build a test set.
We connect trusted sources and design retrieval around them.
We measure accuracy, safety and cost, and iterate until it holds up.
We launch with monitoring and feedback loops that keep it improving.
Yes, with safeguards. We keep private data inside systems you control, limit what the model can see by role, and log access, which are the kinds of reasonable safeguards the SHIELD Act expects.
It can be, with review built in. Drafts go through your compliance or legal reviewers before anything is published, and the tool keeps a record of who approved what.
Retrieval-augmented generation (RAG) finds the most relevant passages in your own content and gives them to the language model with the question, so answers are grounded in your data and can cite their sources.
Often not. Good retrieval and prompt design solve most enterprise use cases. We recommend fine-tuning only when evaluations show it clearly improves quality, cost or speed.
We design around your security requirements: private deployments where needed, access controls applied to retrieval, no training on your data without consent, and full logging.
Let's talk
We help you navigate Enterprise AI, from first use case to production.
Let's connect