ooligo

Kira Systems

contract-ai contract-analysis · due-diligence · clause-extraction · m-and-a
AI-NATIVE API
Legal Ops
8.0 /10

What it is

Kira Systems is the contract-analysis platform that pioneered ML-based clause extraction for M&A due diligence and large-scale contract review. Acquired by Litera in 2021, it now lives inside the Litera platform alongside Litera Transact (deal management) and Litera Compare (document comparison). Deployed across most of the AmLaw 100 for transactional and diligence work.

  • Best-in-class clause extraction. Hundreds of pre-trained clause models (change of control, assignment, indemnification, governing law, exclusivity) plus the ability to train custom models on a firm’s own contract corpus. The accuracy bar that newer LLM-based tools are still chasing.
  • Built for diligence at volume. Pull 5,000 contracts into a data room, run Kira, get a structured matrix of every key clause across every contract. Replaces weeks of associate time on red-flag review.
  • Litera ecosystem fit. Pairs naturally with Litera Transact for deal workspaces and Litera Compare for redline workflows. One vendor relationship for the transactional stack.

Pricing

  • Custom only. Sold per-attorney/year, typically packaged with other Litera products. Mid-market firms see effective rates from $40K/year for small teams up to seven figures for global firm rollouts.
  • Volume model also available. Some deals priced on document throughput rather than seats.
  • Implementation usually 60-90 days with Litera professional services for custom-model training.

Best for

  • M&A and transactional practices doing high-volume diligence
  • Litigation teams handling document-heavy second requests and HSR responses
  • In-house legal teams reviewing inherited contract portfolios after acquisitions

Watch-outs

  • Older UI relative to newer LLM-based tools; the value is in the trained models, not the interface
  • Less fit for pure drafting or negotiation workflows — Spellbook or Harvey serve those better
  • Custom-model training requires enough labeled examples to be worthwhile; small teams may not have the corpus to justify it
  • LLM-based contract analysis (Luminance, Harvey, native Anthropic + custom Skills) is closing the accuracy gap fast on common clauses