Our Approach
Legal intake AI deployments are governed by a qualification-accuracy threshold that must be established before any deployment conversation begins: the attorney's time is worth too much to route unqualified leads, and the firm's reputation depends on declining ineligible cases professionally. For Sterling, we analyzed 2,000 historical intake records to reverse-engineer the qualification patterns their best paralegals applied intuitively, then built those patterns into a branching conversation model trained on case-type-specific signals. The urgency scoring and attorney notification system was designed as a competitive mechanism — personal injury firms win cases by being first to call — not just as a workflow convenience. Clio integration and data security architecture were scoped and cleared by ethics counsel before any code was written, because attorney-client privilege protections must be structurally embedded, not retrofitted after deployment.
Challenge
Sterling & Associates drove 600+ monthly visitors to their landing pages through $42,000 in Google Ads, but the website was hemorrhaging potential clients. Their contact form had a 73% abandonment rate—prospects started filling it out and left before submitting. Of the forms that were completed, the average response time was 4.2 hours because the three intake paralegals triaged submissions manually between phone calls. The firm's analytics revealed a deeper conversion gap: 61% of website visitors who viewed a practice area page never initiated contact at all. Many had questions about whether their situation warranted a consultation but had no way to get an immediate answer. Meanwhile, 34% of booked consultations were no-shows or practice area mismatches—someone seeking workers' comp advice when the firm only handled personal injury, or duplicate inquiries from the same prospect submitting forms on multiple pages. The real cost wasn't just ad waste—it was invisible lost revenue. Sterling estimated they were converting just 5% of qualified website traffic into consultations. Their intake paralegals spent more time sorting through irrelevant submissions and chasing unresponsive form leads than actually onboarding viable cases. The firm needed a way to engage visitors the moment interest peaked, filter out mismatches before they consumed attorney time, and reduce the friction between "I might have a case" and a signed retainer.
Solution
24/7 Case Qualification Engine
The AI intake chatbot qualifies personal injury cases around the clock across the firm's website, Google Ads landing pages, and Facebook lead ads. The qualification flow was built around Sterling's specific case criteria: it determines practice area (auto accident, slip and fall, medical malpractice, product liability, workplace injury), assesses liability indicators through guided questioning, captures injury details and medical treatment history, checks statute of limitations based on state and incident date, and estimates case value based on injury severity and treatment costs. Trained on 2,000 past intake records, the AI identifies viable cases with 94% accuracy compared to paralegal assessments. The chatbot uses empathetic language patterns designed with Sterling's most experienced paralegal, acknowledging the caller's situation before proceeding with qualification questions.
Learn more about our AI chatbot services →Intelligent Lead Routing & Attorney Notification
High-value leads with clear liability, significant injuries, and valid statute of limitations trigger an immediate SMS notification via Twilio to the assigned attorney with a case summary and urgency score. The attorney can call the prospect within minutes while details are fresh, a critical advantage in personal injury where the first firm to make contact often wins the case. The urgency scoring algorithm weighs injury severity, estimated case value, and time sensitivity to ensure the highest-priority leads reach attorneys first. Standard qualified leads are routed to the intake paralegal queue with pre-populated case files. Unqualified leads receive a professional declination with referral suggestions, maintaining the firm's reputation even with prospects they cannot serve.
Learn more about our AI chatbot services →Clio Case Management Integration
Integration with Clio creates a new matter automatically for every qualified lead, attaches all intake data including injury details, medical provider information, insurance coverage, and incident timeline, and sets follow-up tasks for the assigned attorney with deadlines based on the urgency score. This eliminated the incomplete intake notes that caused Sterling to lose three viable cases in the prior year. Data completeness improved from 71% to 98% because the AI never forgets to ask about prior injuries, insurance coverage, or medical providers, following the same thorough protocol on every interaction regardless of time or volume.
Learn more about our AI integration services →Compliance & Data Security Framework
The chatbot was deployed with a comprehensive data security architecture reviewed by Sterling's ethics counsel and IT consultant. All client communications are encrypted in transit and at rest, with intake data stored in a Supabase database that enforces row-level security and automatic deletion policies aligned with the firm's document retention schedule. The system maintains attorney-client privilege protections by clearly identifying itself as a legal intake tool rather than providing legal advice, a distinction validated through 300 test conversations before launch. Conversation logs are retained with full audit trails for bar compliance requirements, and the Google Ads API integration ensures HIPAA-adjacent handling of medical information disclosed during intake. This security-first approach enabled Sterling to pass their annual compliance review without additional remediation for the first time in three years.
Learn more about our AI integration services →Measurable Outcomes
+233%
Monthly Qualified Cases
- Before
- 84
- After
- 280
-65%
Cost Per Qualified Case
- Before
- $890
- After
- $310
45% of all qualified leads
After-Hours Case Capture
- Before
- 0 (voicemail only)
- After
- 126/month
-82%
Average Qualification Time
- Before
- 22 minutes
- After
- 4 minutes
+38%
Intake Data Completeness
- Before
- 71%
- After
- 98%
Key Takeaways
- 24/7 intake captured 126 qualified cases per month during after-hours periods that previously produced zero leads, accounting for 45% of all qualified cases.
- Cost per qualified case dropped from $890 to $310 by eliminating the manual qualification time for the 68% of inquiries that did not meet case criteria.
- Training the AI on 2,000 historical intake records achieved 94% agreement with paralegal case assessments while reducing qualification time from 22 minutes to 4 minutes.
- Immediate attorney notification for high-value leads lets the firm make contact within minutes, winning cases that would have gone to competitors during the voicemail-callback delay.
- Consistent data capture at 98% completeness eliminated the incomplete intake notes that previously caused the firm to miss viable claims.
Why It Worked
Sterling's intake transformation succeeded because it addressed every failure point in their previous process simultaneously. The 24/7 availability captured the 45% of inquiries arriving outside business hours. The AI's consistent qualification protocol eliminated the variability between paralegals who asked different questions in different orders. The immediate attorney notification for high-value cases exploited the competitive advantage of being the first firm to call back. And the Clio integration ensured no qualified lead lost momentum in the handoff from intake to attorney. The $1.8M trucking case signed at 11 PM on a Saturday exemplifies the compound value: it combined after-hours availability, accurate qualification, and instant attorney notification into a single seamless interaction that the old system could not have produced.
Implementation Timeline
Week 1
Case Criteria Training & Flow Design
Analyzed 2,000 past intake records to identify qualification patterns, built the branching conversation flow for 5 practice areas, and defined the scoring model for case value estimation.
Week 2
Multi-Channel Deployment & Clio Integration
Deployed the chatbot on the website, Google Ads landing pages, and Facebook lead ads. Built the Clio API integration for automatic matter creation and task assignment.
Week 2-3
Attorney Notification & Routing Logic
Implemented the urgency scoring system, built SMS notification triggers for high-value leads, and configured the paralegal queue routing with pre-populated case summaries.
Week 3
Testing & Launch
Ran 300 test conversations against historical intake data to validate qualification accuracy, confirmed 94% agreement with paralegal assessments, then launched with a 48-hour monitoring window.
Tools & Platforms
“The AI catches things our paralegals sometimes missed—it never forgets to ask about prior injuries, insurance coverage, or medical providers. We signed a $1.8M trucking case last month that came through the chatbot at 11 PM on a Saturday. Under our old system, that lead would have gone to voicemail and called another firm Monday morning.”
Marcus Sterling
Managing Partner, Sterling & Associates
Frequently Asked Questions
- How does the AI determine if a personal injury case is viable?
- The chatbot evaluates cases against Sterling's specific criteria: practice area match, liability indicators based on incident description, injury severity and medical treatment received, statute of limitations based on state and incident date, and estimated case value. It was trained on 2,000 historical intake records to recognize patterns that predict case viability with 94% accuracy.
- Can the intake chatbot handle cases across multiple practice areas?
- Yes. The system qualifies cases across five personal injury practice areas: auto accidents, slip and fall, medical malpractice, product liability, and workplace injuries. Each practice area has a dedicated conversation branch with questions specific to that case type, liability assessment criteria, and statute of limitations rules.
- What happens to leads that do not qualify for representation?
- Unqualified leads receive a professional declination message with referral suggestions to other practice areas or legal aid resources. This maintains the firm's reputation and occasionally generates referral relationships. The chatbot explains why the case does not meet criteria without providing legal advice, a distinction reviewed by Sterling's ethics counsel.
- How does the AI handle sensitive personal injury details with empathy?
- The conversation flow was designed with input from Sterling's most experienced paralegal, incorporating empathetic language patterns for discussing injuries, accidents, and medical history. The AI acknowledges the difficulty of the caller's situation before proceeding with qualification questions, and adjusts pacing based on response patterns that indicate distress.
