Company: Law Firm
Industry: Intellectual Property
Client Background
Our client is a leading New York City intellectual property law firm providing innovative legal solutions to protect inventions, brands, and other intellectual property assets. The firm handles a broad range of patent and trademark matters, including prosecution, litigation, post-grant proceedings, freedom-to-operate, licensing, and IP due diligence across multiple technology sectors.
As part of its efforts to modernize operations, the firm sought to automate its reporting letter preparation process using an AI-driven technology solution.
Client Challenges
The firm’s objective was straightforward: automate a traditionally manual reporting process, reduce dependence on internal resources, and improve the speed and efficiency of client communications. However, the AI implementation created several unexpected challenges.
- Inaccurate AI-generated information: The system did not consistently populate critical matter information correctly, including filing dates, official fees, firm-specific fees, patent/application numbers, claims, and other matter details.
- Loss of internal expertise: The firm’s internal reporting letter resource had resigned based on the technology provider’s expected implementation timeline, leaving the firm without dedicated operational support when the AI solution went live.
- Growing reporting backlog: AI-generated letters required significant review and correction, while new reporting requests continued to arrive, resulting in a growing backlog.
- Limited IP-specific understanding within the technology team: The AI development team required practical guidance from IP professionals to understand which information needed to be extracted, validated, or manually entered.
The firm therefore needed more than an outsourcing resource. It needed immediate operational support alongside subject-matter expertise to help make the AI solution work effectively.
Scope of Work
OBS was initially engaged to:
- Prepare and process daily reporting letters.
- Clear the existing reporting letter backlog.
- Manually prepare and validate letters where required.
- Work directly with the AI implementation team to identify data and workflow issues.
As the engagement progressed, the scope expanded to include:
- Reviewing AI-generated reporting letters before client delivery.
- Validating filing dates, patent/application numbers, official fees, firm fees, claims, and matter-specific information.
- Identifying inaccurate or missing information.
- Providing IP-specific guidance to the AI development team.
- Establishing a quality-control layer between AI-generated output and final client communication.
Our Solution: A 3-Phase AI Stabilization Approach
Phase 1: Stabilizing the Reporting Operation
The immediate priority was to stop the backlog from growing and ensure that daily reporting requirements continued without interruption.
OBS deployed an experienced IP resource who began processing reporting letters manually while simultaneously handling both the existing backlog and newly generated reporting requirements.
This gave the firm immediate operational stability while preventing further delays to client communications.
Phase 2: Improving the AI Workflow
Rather than simply working around the technology, OBS worked alongside the AI implementation team to identify the underlying causes of inaccurate reporting letters.
Each incorrect or incomplete AI-generated letter was reviewed to determine what information was missing, what data was inaccurate, and what logic needed to be corrected.
OBS provided practical IP-specific feedback covering:
- Filing dates
- Patent and application numbers
- Official fees
- Firm-specific fees
- Claims and other manually required information
- Matter-specific details
- Information that could not be reliably extracted through automation
This allowed the technology team to refine the AI solution using real-world IP reporting requirements and actual workflow scenarios.
Phase 3: Building a Human-in-the-Loop Quality Layer
As the AI solution became more reliable, OBS transitioned from preparing every reporting letter manually to providing expert quality control over AI-generated output.
The resulting workflow became:
Source Information → AI-Generated Letter → OBS IP Review → Validation & Corrections → Final Client Reporting
This approach allowed the firm to retain the efficiency benefits of automation while ensuring that critical information was reviewed by an experienced IP professional before reaching the client.
Results Achieved
Within just 30 days, OBS helped the firm stabilize its reporting operation, eliminate the immediate backlog, and establish a more reliable AI-assisted workflow.
- 100% of the existing reporting letter backlog cleared within 30 days, while newly generated letters continued to be processed.
- Daily reporting operations restored without requiring immediate internal recruitment or training.
- AI-generated reporting accuracy improved through continuous IP-specific feedback on critical data fields, fees, dates, and matter information.
- A dedicated human quality-control checkpoint established to validate AI-generated letters before client delivery.
- Reporting operations transitioned from fully manual processing to a scalable hybrid AI + human workflow.
- Critical matter information continued to receive expert validation, including claims and other details that could not be reliably automated.
- The firm retained OBS after the AI implementation, recognizing the continued value of specialized IP expertise alongside automation.
Business Impact
The engagement evolved from an urgent operational recovery project into a long-term technology-enabled reporting model.
The firm was able to continue using its AI investment without treating automation as an all-or-nothing replacement for human expertise. Instead, AI handles the initial generation while OBS provides the IP knowledge, validation, and quality control needed to make the process dependable.
This hybrid model helped the firm eliminate its reporting backlog, maintain uninterrupted client communication, reduce dependency on a single internal resource, and improve the practical effectiveness of its AI investment.
The key takeaway was clear: successful legal automation is not simply about implementing AI. It is about combining the right technology with the right process and the right subject-matter expertise.