Technology has been quietly transforming various aspects of practice and business of law, for last few decades. Up until late 2010's, most prevalent legaltech use cases were enabling faster search of right documents in a large repository (using advanced retrieval techniques) or extracting key terms in a legal document (using entity extraction), etc. The launch of chatGPT / gpt3.5 in late '22 changed everything. Larger, more powerful LLMs (Large Language Models) and reasoning / research capabilities opened up tremendous opportunities for legal teams to use AI, and do more with less.
One of the most valuable applications of Generative AI in business of law is in Transactional / Corporate legal work, especially contract drafting, review and negotiations. Many top law firms and enterprise legal teams have now deployed AI for tasks like spotting risky terms or clauses, refining clause language, generating custom diligence reports, etc. More importantly, leading AI tools are available right where lawyers work, i.e. inside Microsoft Office suite (often as a Word add-in within Microsoft Word) – making adoption seamless.
ContractKen's philosophy is to use AI to simplify and amplify. Its designed to help lawyers amplify their expertise while simplifying the use of AI in contracts.
Our journey began with applying traditional Natural Language Processing (NLP) techniques tailored to contracts. Our initial approach combined pattern recognition algorithms and deep learning (of 2020 vintage!) models like:
Over the last 36 months, as LLM capabilities exploded, we continuously enhanced our solutions (almost every 3-6 months!) to cover use cases which were previously too error prone or impossible altogether. As of 2025 Q1, we are leveraging AI to drive following key benefits:
ContractKen has evolved from a specialized NLP driven application into a full-fledged generative AI copilot, inside Word for contract drafting and review.
We believe that legaltech is about leveraging the immense analysis and generation capabilities of best-in-class AI models already out there. Our products make API calls to best-in-class publicly hosted or on-prem LLMs. Our orchestration layer employs a routing capability behind the scenes, selecting the optimal model for each task. The system orchestrates the LLM calls in the background, functioning almost like an AI project manager delegating subtasks to the best specialists.
By directly integrating LLMs, ContractKen ensures it stays on the cutting edge of AI capability. As new models with reasoning and tool use capabilities emerge and mature (esp for legal usecases), they can be plugged into the platform. This flexibility is crucial in the rapidly advancing AI landscape.
Moreover, ContractKen’s approach includes transparency and control: the AI’s suggestions in Word are editable by the user, and the lawyer always remains the final arbiter. In practice, the AI might draft a section or propose an edit, and the attorney can accept, reject, or modify that suggestion. This keeps the legal expert in the loop, using AI as a helpful assistant rather than a black-box oracle – a distinction that eases adoption among professionals wary of ceding too much control to algorithms
While general-purpose LLMs are powerful, they can sometimes “hallucinate” – producing plausible-sounding but incorrect statements. In legal work, accuracy is paramount, and AI's output need to be grounded in reliable, context-specific information. That’s why Retrieval-Augmented Generation (RAG) has become a standard solution for deploying LLMs in legal application.
How RAG works in ContractKen: whenever the AI needs to draft or answer something about a contract, it first retrieves relevant context from a curated knowledge base (the Knowledge Layer). This knowledge base can include the organization’s own clause library, templates, playbooks, or even industry-standard clause collections. For example, if you’re reviewing an NDA (Non-Disclosure Agreement), the system will fetch common NDA provisions and your company’s past NDAs as reference. The LLM then uses those references to inform its output. So if you ask, “Is there a non-compete in this contract?”, the AI will search the text (and perhaps a definitions database) to ensure its answer is grounded in actual content, not a guess. Or, if you request, “Draft a liability clause based on our standard,” the AI will look up your company’s standard liability clause from the clause library and either excerpt it or adapt it with the LLM, rather than inventing new language from whole cloth.
Using this RAG based approach dramatically improves accuracy and trustworthiness in contract drafting and review tasks.
ContractKen’s AI effectively has a live checklist and knowledge repository to consult, which keeps it honest. It avoids the pitfall of a pure generative model that might otherwise fabricate a clause that sounds good but isn’t actually in the document or in line with your policies.
A key criterion for legal AI is drawing from content you trust and enabling verification of AI outputs. RAG provides exactly that: every AI-generated answer or clause can be linked back to source materials (e.g., “flagged missing Arbitration clause based on playbook X”). This gives the lawyer confidence in the suggestions, knowing they’re backed by either the contract text itself or vetted knowledge sources.
Another advantage of ContractKen’s RAG pipeline is customization by contract type and industry. The system can apply different knowledge layers depending on the context. For a privacy policy, it might pull in relevant regulations and company policies; for a merger agreement, it might reference past deals and M&A checklists. Each practice area or client can have a tailored repository of clauses and guidance.
Essentially, ContractKen’s AI is learning the playbook of the organization and the transaction at hand before it speaks. This leads to outputs that are not only correct, but also contextually appropriate. As a result, users see highly relevant clause suggestions or issue flags that align with their specific needs – a level of bespoke service that generic AI tools can’t match.
For specific clients, ContractKen has invested in fine-tuning models to further specialize them for contract review tasks. Fine-tuning means taking a general model and training it on a narrower dataset so it learns patterns specific to that domain. ContractKen has experimented with fine-tuning OpenAI’s GPT-4o Turbo on contract review and analysis data. The team employed an ingenious approach: use a more powerful model (the “teacher”) to generate high-quality labeled examples, and then use those to fine-tune the faster GPT-4o model. In effect, GPT-4o’s knowledge is distilled into a leaner model that can run more efficiently.
What does this achieve? It produces an AI model that is fluent in “legalese” and contract structure, tuned to identify and classify clauses, spot anomalies, and follow legal reasoning more closely. Fine-tuning boosts accuracy for these domain-specific interpretations, improving both precision and recall in clause detection (meaning it finds more of the relevant clauses and makes fewer incorrect identifications).
ContractKen can offer custom fine-tuning for specific law firm needs. Imagine a firm has a unique style of drafting or a proprietary set of fallback clauses. By training the AI on the firm’s past contracts and playbook, the model can internalize those preferences. This goes beyond one-size-fits-all AI. It means Firm A’s version of ContractKen might learn to be stricter on IP indemnity clauses because that firm’s policy is very risk-averse, whereas Firm B’s AI might be tuned to focus on data protection clauses due to their tech clientele.
In ContractKen’s case, this fine-tuning manifests in the AI’s recommendations: the tone and content of suggestions will better reflect the user’s own standards. Over time, as the firm uses the tool and corrects it, ContractKen can further learn (via supervised updates or feedback loops) – much like “learns as you negotiate” from each redline you apply.
The outcome is an AI assistant that doesn’t just know contracts in general, but knows your contracts. For the end-user, this feels like working with a junior lawyer who has been thoroughly trained in the firm’s way of doing things – except this associate works at machine speed and is available 24/7.
Technology is only as good as its usability. ContractKen recognizes that lawyers live in Microsoft Word documents when it comes to contracts, so it delivers its AI capabilities through a seamless Word add-in. This design choice – to meet users where they already work – aligns with the broader trend of AI legal tools integrating into Word or Office 365.
What does the ContractKen Word add-in help you with?
Because lawyers can see the AI’s output directly in the document (highlighted text, margin comments, inserted suggestions), it feels like a very natural extension of the review process. Early adopters of ContractKen Word add-in have reported significant productivity gains, noted saving at least an hour of work each day.
ContractKen’s design keeps the human in the loop at all critical junctures. The AI suggests, but the human decides. This collaborative model means the lawyer is always in charge of the final document, easing concerns about AI making unauthorized changes or errors.
In fact, one of our client's CFO noted that lawyers “love that they’re natively connected” in Word and can edit AI outputs in real time the AI becomes a partner, not a replacement.
ContractKen also prioritizes data security and confidentiality. We have developed a proprietary Moderation Layer in collaboration with industry experts to ensuree sensitive data is anonymized locally before being processed. While basic anonymization is standard with many out-of-the-box contract solutions, ContractKen goes a step further and allows customers to specify the data they want anonymized. This commitment to data privacy has earned the trust of law firms handling highly confidential information.
Lastly, a word on ethical AI usage: ContractKen is built with guardrails to avoid problematic outputs and deploys a continuous monitoring capability for the AI’s performance.
ContractKen’s latest advancements position it as a leading solution in the Legal AI landscape for contracts. By combining direct LLM firepower, knowledge-driven accuracy, and user-centric design, it addresses the triad of speed, quality, and usability that law firms and legal departments demand.
They can negotiate from a position of information advantage (with the AI surfacing insights) and maintain consistency across thousands of documents. Mundane tasks are minimized, freeing attorneys to focus on strategy, complex negotiations, and client interaction – the aspects of legal work that truly require human creativity and expertise. As one early user insightfully put it about AI in contract work: it doesn’t replace lawyers, but “simply means that their roles will transform to be less mundane.”
To conclude, lets recap ContractKen's real-world impact: Faster, smarter contracts with AI assistance.
By combining direct LLM power, knowledge grounding, fine-tuned accuracy, and seamless Word integration – ContractKen’s AI copilot delivers concrete benefits for contract drafting and review:
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