The Urgency Problem in Personal Injury Law: Why Every Hour Counts
Personal injury accidents do not respect business hours. Every weekend on the Highway 401 corridor through Mississauga and Brampton, dozens of motor vehicle accidents send people to emergency departments across the GTA. The Gardiner Expressway sees rear-end collisions on Friday evenings. The 407 ETR through York Region records multi-car pile-ups in winter storms at 3am. When those accidents happen, the injured parties — or their family members — will almost immediately begin searching online for legal representation. The question is not whether they will search. The question is whether your firm will be there when they do, or whether your competitors will be.
Ontario's personal injury legal market is structured around urgency in a way that most other practice areas are not. The Statutory Accident Benefits Schedule — universally known as SABS — creates immediate obligations for accident victims. SABS benefits must be applied for within seven days of the accident in order to access income replacement benefits, medical and rehabilitation funding, and attendant care support. Victims who do not understand this deadline often lose access to significant compensation they are legally entitled to receive. When an injured person searches online for a personal injury lawyer and finds your chatbot ready to guide them through their rights under SABS, you are not just capturing a business lead — you are providing a genuinely valuable service at the moment of maximum need.
Most Ontario personal injury law firms close their offices at 5pm. Some have answering services. Very few have a system that can engage a new prospect, collect their information, and begin the intake process at 10:30pm on a Thursday night. This is not a minor operational gap — it is a fundamental competitive vulnerability. Law firm marketing data consistently shows that between 35% and 45% of all website contact attempts happen outside of regular business hours. For personal injury specifically, where accidents cluster in the evening commute hours and weekend leisure periods, that after-hours window is likely even larger. Every hour a potential client spends without a response is an hour in which they are reconsidering their options and potentially reaching out to a competitor.
The Greater Toronto Area alone has hundreds of registered personal injury law firms competing for the same pool of accident victims. From Bay Street boutiques to large volume referral operations in Scarborough and North York, the competition for PI leads in Ontario is intense. Many of these firms spend tens of thousands of dollars per month on Google Ads, outdoor advertising, and radio campaigns — all designed to drive traffic to a website that then loses leads overnight. An AI chatbot does not fix your advertising spend, but it does ensure that every dollar you invest in driving traffic to your website results in a captured lead rather than a missed opportunity.
The pattern that personal injury law firms consistently observe is this: accident occurs at 9pm, victim or family member begins searching for a lawyer by 10pm, visits three or four websites, contacts the first one that makes contact or appears the most responsive, and retains that firm. The entire decision cycle can compress into a matter of two to four hours. A firm with an AI chatbot that engages the visitor instantly, collects their information, and confirms that a lawyer will call first thing in the morning is almost always the firm that gets the retainer. A firm with a phone number and a contact form — but no active engagement after 5pm — is almost always the firm that loses the case to a competitor.
What Happens When a Personal Injury Lead Arrives at 2am
When a person who was injured in a slip and fall at a Toronto grocery store visits a law firm website at 2am without a chatbot in place, one of two things happens. They see a phone number, call it, reach a voicemail, and hang up. Research across legal service contact behaviour consistently shows that approximately 63% of callers who reach voicemail do not leave a message and do not call back. They move on. The second option is slightly better: the visitor finds a contact form, fills it out, submits it, and then waits until morning. By the time morning arrives and the firm's intake coordinator begins calling through the overnight contact forms, the prospect has already called three other firms and spoken with whichever one called them back fastest.
With an AI chatbot deployed on the firm's website, the visitor experience is completely transformed. Instead of being met with silence or a static contact form, the visitor is greeted immediately by a conversational interface that acknowledges their situation. The opening message might read: "Hello, I'm here to help 24 hours a day. It sounds like you may have been injured — I can help connect you with our personal injury team. First, can you tell me a little about what happened?" This single message changes the dynamic entirely. The visitor is no longer making a one-way contact attempt into a void. They are in a conversation with an entity that is responsive, empathetic, and actively working to help them.
The chatbot then begins a structured intake flow designed specifically for personal injury cases. Rather than asking generic questions like "how can we help you?", the chatbot is scripted to collect the exact information a personal injury lawyer needs to evaluate the case and prioritize the follow-up call. Within the first few exchanges, the chatbot has established the type of accident, when it occurred, where it happened, whether the person was injured and to what degree, whether they received emergency medical treatment, and whether they have already spoken with any other lawyers or insurance adjusters. This information is not just collected — it is organized and logged in real time.
The specific information a PI chatbot collects during an after-hours intake flow is comprehensive: the visitor's full name; their preferred phone number and email address; the date, time, and location of the accident; the type of accident (motor vehicle, slip and fall, dog bite, occupiers' liability, long-term disability); the nature and severity of their injuries; whether they attended a hospital or clinic; whether a police report was made; whether their own insurance company has already contacted them; and whether they have consulted with any other lawyers. Each of these data points has a direct bearing on how quickly the follow-up call should be made, which lawyer should be assigned to the intake call, and how the initial consultation should be framed.
What happens to that information after the chatbot conversation ends is equally important. In a well-integrated system, the chatbot logs the intake data directly into the firm's Clio practice management account, creating a new contact and a draft matter with all of the collected fields pre-populated. Simultaneously, an email alert is sent to the intake coordinator and the on-call lawyer summarizing the lead, flagging any urgency indicators such as a recent accident or a mention of catastrophic injuries. When the team arrives at 9am, the lead is not buried in a contact form inbox — it is a fully organized matter in Clio with a task assigned for immediate follow-up. The firm calls the prospect while the memory of finding the firm's helpful chatbot at 2am is still fresh. That combination of empathetic responsiveness and professional follow-up is extremely difficult for a competitor to beat.
Scripting Your PI Chatbot: MVA and Slip-and-Fall Intake Flows
The opening message of your personal injury chatbot establishes the entire tone of the interaction. It needs to accomplish three things simultaneously: communicate empathy and care for the visitor's situation, establish that the chatbot can genuinely help them, and invite them into a conversation. A poorly written opening message — one that feels generic, robotic, or overly legalistic — will cause the visitor to disengage. The best opening messages for PI chatbots are warm, specific, and framed around the visitor's need rather than the firm's services. Something like: "Hi there — if you or a loved one has been injured in an accident, you've come to the right place. I'm available right now to help connect you with our personal injury team. What type of accident were you involved in?" This approach signals availability, competence, and genuine care within the first two sentences.
For motor vehicle accident cases — which represent the majority of personal injury files in Ontario — the chatbot intake flow needs to capture a specific set of facts that determine the viability and value of the claim. After establishing the type of accident, the key questions are: Was this a single-vehicle accident or did it involve another driver? When exactly did the accident occur? Where on the road network did it happen — the specific intersection, highway name, or city? Were you the driver, a passenger, a cyclist, or a pedestrian? Were you wearing a seatbelt? Were other occupants injured? Did you call 911 or was police called to the scene? Did emergency responders attend? Were you transported to hospital by ambulance, or did you go to the emergency department independently? What injuries are you experiencing? Are those injuries preventing you from working or performing daily activities? Has your own insurance company been in contact with you since the accident? These questions move through the SABS and tort claim considerations systematically without the visitor ever feeling like they are being processed through a bureaucratic form.
Slip and fall cases require a different intake flow that addresses the elements of a successful occupiers' liability claim under the Ontario Occupiers' Liability Act. The chatbot must establish: what type of property the accident occurred on (retail store, restaurant, apartment building, municipal sidewalk, private residence); who owns or manages that property; what caused the fall (wet floor, uneven pavement, ice and snow, inadequate lighting, unmarked hazard); whether the hazard was reported to property management or staff at the time; whether there are any witnesses who observed the fall; whether the incident was documented in any way such as an incident report filed with the property manager; and whether there is any video surveillance footage that may have captured the incident. For municipal sidewalk falls, the chatbot should flag the 10-day notice requirement under the Municipal Act — a critical procedural deadline that can extinguish a claim if missed.
Ontario's personal injury legal landscape contains a layer of regulatory and procedural complexity that a well-built PI chatbot must understand and reflect. The Financial Services Regulatory Authority of Ontario (FSRA, formerly FSCO) oversees Ontario's auto insurance system. The Statutory Accident Benefits Schedule sets out the framework for accident benefits including income replacement, medical and rehabilitation, attendant care, and housekeeping. The Minor Injury Guideline (MIG) caps medical and rehabilitation benefits at $3,500 for claimants whose injuries are classified as minor. The tort threshold in Ontario's Insurance Act means that claimants must meet a statutory threshold of non-pecuniary damages before they can successfully advance a pain and suffering claim. A chatbot that asks the right questions — particularly around whether injuries are preventing work, whether surgeries are anticipated, and whether the person has been diagnosed with a psychological impairment — can flag potential catastrophic impairment cases for immediate escalation.
Perhaps the most powerful feature of a well-scripted PI chatbot is its ability to branch conditionally based on the visitor's responses. A visitor who mentions they were hospitalized and required surgery should trigger a high-priority alert to the on-call lawyer immediately — not an email that waits until morning. A visitor who mentions that their injuries include a traumatic brain injury, spinal cord damage, or severe burns should be flagged as a potential catastrophic impairment case, which under SABS entitles the claimant to significantly enhanced benefits including up to $1 million in medical and rehabilitation funding. A visitor who mentions that their insurance company has already offered them a settlement should trigger a specific response path that educates them on the importance of not accepting any settlement without legal advice. These conditional branches transform the chatbot from a simple intake form into an intelligent triage system that helps the firm allocate its most urgent attention to its most valuable leads.
Ontario's Accident Benefits System and What Your Chatbot Needs to Know
The Statutory Accident Benefits Schedule is the foundation of Ontario's auto insurance system and the source of immediate, fault-independent compensation for anyone involved in a motor vehicle accident in the province. Under SABS, every person injured in an accident — driver, passenger, cyclist, or pedestrian — is entitled to claim accident benefits through their own auto insurance policy or, if they do not own a vehicle, through the policy of the vehicle they were travelling in. SABS benefits include income replacement benefits for those unable to work, medical and rehabilitation funding for treatment, attendant care benefits for those requiring personal assistance, housekeeping and home maintenance benefits, visitor expenses, and death and funeral benefits. Understanding SABS is not optional for a PI chatbot — it is foundational. When a visitor mentions they were in a car accident, the chatbot should immediately acknowledge that accident benefits are available regardless of fault and ask whether they have already notified their insurance company.
The distinction between a Minor Injury Guideline designation and a catastrophic impairment designation is one of the most consequential determinations in any personal injury file. Under the MIG, injuries classified as minor — including sprains, strains, and whiplash-associated disorders — are subject to a $3,500 cap on medical and rehabilitation benefits. However, if the claimant can demonstrate that their injuries fall outside the MIG — for example, because they have a pre-existing condition that was aggravated by the accident, or because they have developed a psychological impairment as a result of the collision — they may access substantially higher benefits under the non-catastrophic benefit limits. Catastrophic impairment designation triggers the highest tier of SABS benefits, including up to $1 million in medical and rehabilitation funding and up to $1 million in attendant care. A PI chatbot that asks visitors about the nature of their injuries, including questions about psychological symptoms and pre-existing conditions, can flag potential MIG disputes and catastrophic claims for immediate legal attention.
The two-year limitation period for tort claims in Ontario runs from the date the claimant knew or ought to have known that they had a claim — the discoverability principle established in the Limitations Act, 2002. For most motor vehicle accident tort claims, the two-year clock begins running on the date of the accident itself. This means that a person injured in a car accident who contacts a law firm 23 months later is in an urgent situation requiring immediate action. A well-designed PI chatbot should ask the date of the accident during the intake flow and, for accidents that occurred more than 18 months ago, should display a prominent notice flagging the approaching limitation period and escalating the lead to immediate review. This feature alone can save a firm from inadvertently missing a limitations issue on a valuable file and protect the prospective client from losing their right to compensation through inadvertence.
The License Appeal Tribunal — commonly known as the LAT — is the adjudicative body in Ontario that hears disputes between accident victims and their insurers regarding denied or reduced SABS benefits. Since the LAT assumed jurisdiction over accident benefits disputes in 2016, it has become a significant part of the personal injury landscape. When an accident victim contacts a law firm's website and mentions in the chatbot conversation that their insurance company has denied a specific benefit — for example, denied their attendant care claim or disputed a treatment plan — the chatbot should flag this as a LAT matter requiring urgent review, since LAT applications must generally be filed within two years of the date the insurer denies the benefit. Chatbot scripting that specifically asks "Have any of your insurance benefits been denied or reduced?" captures this category of client efficiently and ensures they are connected with a lawyer who handles SABS disputes before their LAT filing window closes.
Beyond the immediate SABS and tort claim considerations, the Ontario accident benefits system interacts with a range of other legal frameworks that a sophisticated PI chatbot can navigate. Long-term disability claims, WSIB claims for accidents that occurred in the course of employment, and criminal injuries compensation through the Ontario Victim Services program are all potential avenues of recovery that a PI chatbot can introduce to the appropriate visitor through conditional branching. A visitor who was injured in an accident while driving for their employer may have both a tort claim and a WSIB claim to pursue — and the chatbot can flag this complexity for the intake lawyer. A visitor who was injured as a result of someone else's criminal act may be eligible for compensation through Ontario's Victim Quick Response Program. Educating prospects about these options during the chatbot intake conversation builds trust, demonstrates the firm's expertise, and increases the likelihood that the visitor will ultimately retain the firm for what may prove to be a multi-avenue recovery case.
Ontario personal injury law firms that deploy AI chatbots with SABS-aware intake flows report capturing leads that traditional phone intake systems routinely miss — particularly from accident victims who are hesitant to speak with a live person but are comfortable sharing information through a text-based conversation at their own pace.
Conversion Rates: What Ontario PI Firms Report After Installing a Chatbot
The headline statistic from Ontario personal injury law firms that have deployed AI chatbots with structured PI intake flows is a 34% increase in after-hours leads captured. Before chatbot deployment, the average mid-size PI firm in the GTA captures approximately 12 after-hours leads per month through voicemail and contact forms combined. After chatbot deployment, that number rises to approximately 16 per month — with the additional leads being of significantly higher quality because they arrive fully qualified with all intake information pre-collected. The improvement comes entirely from converting website visitors who previously left without making any contact because they did not want to leave a voicemail or did not believe a contact form would receive a timely response.
Beyond raw lead capture volume, personal injury firms report a 28% reduction in no-show consultation rates after implementing chatbot-driven intake. The reason is straightforward: leads that arrive through a chatbot conversation are pre-qualified and have already invested meaningful time in explaining their situation. They are engaged with the firm. They have received a confirmation message and, in many cases, an automated email confirming that their inquiry has been received and that a lawyer will call them at a specific time. Compare this to a contact form submission that receives no immediate acknowledgment — the prospect has no assurance that anyone has even read their message, and by the time the firm calls the next morning, the prospect may have already moved on or simply forgotten about the inquiry. The chatbot creates a sense of relationship and commitment that dramatically increases the likelihood of the consultation actually happening.
The time-to-first-contact metric is perhaps the most stark illustration of the chatbot's impact. Without a chatbot, the average time between a prospect's website visit outside business hours and the firm's first outbound contact attempt is 4.2 hours — and that assumes the firm has an efficient morning intake process. With an AI chatbot, the first contact happens in under one second: the chatbot greets the visitor the moment they arrive on the website. The entire intake conversation typically takes seven to twelve minutes, after which the prospect has received a detailed confirmation and the firm has a fully qualified lead in its system. When the intake coordinator calls at 9am, they are not introducing the firm for the first time — they are following up on a conversation the prospect already had with the firm's digital presence. This difference in the relationship dynamic is reflected in dramatically higher contact-to-consultation conversion rates.
The financial impact of these conversion improvements is significant when expressed in terms of case value. The average personal injury file in Ontario — across the range of MVA cases, slip and fall claims, and long-term disability disputes — generates between $8,000 and $25,000 in legal fees over the life of the file, with higher-value catastrophic impairment cases generating substantially more. A conservative average of $15,000 per file means that capturing just two additional files per month through improved after-hours conversion generates $30,000 per month in incremental revenue — or $360,000 per year. Against a monthly chatbot cost of $400 to $800, the return on investment is extraordinary. This is the calculation that drives adoption among personal injury firms, and it is the reason that firms which were initially skeptical about chatbot technology become its most enthusiastic advocates within three to six months of deployment.
Key finding: personal injury firms using AI chatbots report a 34% increase in after-hours leads captured, a 28% reduction in no-show consultation rates, and a contact-to-consultation conversion rate of 38% — compared to 22% for firms relying on phone and contact form intake alone. At an average file value of $15,000, capturing two additional files per month generates $360,000 per year in incremental revenue against a monthly chatbot cost of $400–$800.
| Metric | Before Chatbot | After Chatbot | Improvement |
|---|---|---|---|
| After-hours leads captured | 12/month | 16/month | +34% |
| No-show consultation rate | 35% | 25% | -28% |
| Average time to first contact | 4.2 hours | <1 second | -99% |
| Contact-to-consultation rate | 22% | 38% | +73% |
| Monthly files opened from web | 3 | 5 | +67% |
Clio Integration for Personal Injury: From Chatbot to File in 60 Seconds
The value of a chatbot intake conversation is fully realized only when the data it collects flows seamlessly into the firm's practice management system without requiring manual re-entry by staff. For Ontario personal injury law firms using Clio — which is by far the most widely adopted practice management platform in the Canadian legal market — this integration is both technically achievable and operationally transformative. A chatbot that is connected to Clio via Zapier, or through a native API webhook if the chatbot platform supports it, can create a new Clio contact and draft matter within seconds of the intake conversation concluding. By the time the chatbot has sent the visitor their confirmation message, the firm already has a fully populated lead record in Clio ready for review.
The Clio contact record created from a chatbot conversation should be pre-populated with every piece of information collected during the intake flow: the prospect's full name, phone number, email address, preferred contact time, and any additional notes they provided about their situation. The contact record should also be tagged with the lead source — "Website Chatbot" — so the firm can track its chatbot's contribution to file acquisition in its marketing analytics. Clio's contact management system allows for custom fields, which means the chatbot can populate fields specific to personal injury practice such as "Accident Date," "Accident Type," "Injury Description," and "SABS Benefits Applied" directly from the intake conversation data.
Beyond the contact record, a well-integrated system will automatically create a draft Clio matter associated with the new contact. The matter should be pre-configured with the appropriate practice area — Personal Injury — and should include custom fields for the key PI-specific data points: accident type, accident date and time, accident location, injuries sustained, hospital attended, police report number, at-fault party information, insurance company name, and policy number if provided. Creating this draft matter automatically means that when the intake coordinator reviews the lead in the morning, they are not starting from a blank file — they are reviewing a fully structured matter draft that needs only to be confirmed and assigned to the appropriate handling lawyer before the initial consultation call is made.
Task assignment is another critical component of the Clio integration. When a new chatbot lead is logged, the system should automatically create a Clio task assigned to the intake coordinator with a due date of the same business day, a priority rating based on the urgency indicators flagged during the chatbot conversation, and a brief summary of the lead. For high-priority leads — those involving catastrophic injuries, approaching limitation periods, or denied SABS benefits — the task should be flagged as urgent and an immediate text or push notification should be sent to the on-call lawyer's mobile device, regardless of the time of night. This ensures that truly urgent matters are not lost in the overnight queue waiting for morning review.
The prospect-facing component of the Clio integration is equally important. Immediately after the chatbot intake conversation concludes, the system should send the prospect an automated email from the firm's own email address — not from the chatbot platform's domain — confirming that their inquiry has been received, summarizing the key information they provided, confirming the expected callback time, and providing the firm's direct phone number in case they need to reach someone urgently before then. This confirmation email serves multiple purposes: it reassures the prospect that their inquiry was genuinely received by a real firm and not lost in a digital void; it demonstrates professional responsiveness that builds confidence in the firm's competence; and it creates a record of the firm's first communication with the prospect, which is relevant for conflict-checking and retainer letter purposes. Firms that send confirmation emails report significantly higher next-morning answer rates when the intake coordinator calls.
Choosing the Right AI Chatbot Platform for Your PI Practice
The decision between a DIY chatbot configuration and a fully managed PI-specific chatbot solution is more consequential than most law firm principals realize at the outset. DIY chatbot platforms like Tidio, Intercom, or Drift offer accessible interfaces and moderate pricing, but they require significant internal investment to configure effectively for personal injury intake. A proper PI intake chatbot for an Ontario firm is not a simple "leave us a message" form dressed up with conversational UI. It requires a branching decision tree that covers MVA scenarios, slip and fall scenarios, occupiers' liability cases, long-term disability claims, and dog bite cases — with conditional branches for SABS questions, MIG flagging, LAT dispute identification, and limitation period alerts. Building this from scratch in a DIY platform, even by an experienced operations manager, typically takes 40 to 80 hours of configuration and testing time. At $100 per hour of skilled administrative time, that is a $4,000 to $8,000 investment before the chatbot has handled a single visitor.
Smith.ai occupies a valuable middle ground in the chatbot market for personal injury law firms. Rather than a purely automated AI conversation, Smith.ai deploys a hybrid model in which AI handles the initial engagement and structured intake questions, but live human agents — trained receptionists available around the clock — can take over the conversation when the visitor's situation becomes complex, highly emotional, or requires judgment that an AI flow cannot provide. For PI law firms dealing with callers who are in acute distress following a serious accident, the empathy and adaptability of a live human agent can be the difference between a converted lead and a lost one. Smith.ai's agents are also trained in PIPEDA-compliant intake scripting and can maintain consistent quality across a high volume of overnight contacts in a way that a purely automated system may struggle with during edge cases.
Custom natural language processing capabilities become important for personal injury firms operating at the more sophisticated end of the intake spectrum. A basic chatbot handles structured intake flows well, but it can struggle when visitors use their own language to describe their situation in ways that do not map neatly onto the prescribed question flow. A visitor who says "I slipped on black ice in a parking lot behind a No Frills in Barrie last February and I'm still having trouble walking" is providing rich, usable information — but a poorly configured chatbot may fail to extract the key facts cleanly. Advanced NLP, whether provided by the chatbot platform itself or through a custom integration with an AI language model, allows the chatbot to understand natural language descriptions and map them to the appropriate intake fields. For catastrophic impairment screening — where the chatbot needs to detect references to brain injuries, spinal cord damage, severe burns, or blindness within free-form descriptions — NLP capabilities are essentially mandatory for reliable triage.
Bilingual service is a practical requirement for Ontario personal injury law firms operating outside the GTA. Ottawa serves a large French-speaking population under the French Language Services Act. Northern Ontario communities including Sudbury, Sault Ste. Marie, and Timmins have significant francophone populations who may prefer to communicate about a stressful legal matter in their first language. A chatbot that can seamlessly detect the visitor's language preference — by asking at the outset or by detecting the language used in the visitor's first message — and shift to fluent, legally accurate French for the remainder of the intake conversation represents a meaningful competitive advantage in these markets. Not all chatbot platforms offer genuine bilingual capability; some offer machine translation that produces awkward or technically incorrect French, which can damage the firm's credibility with francophone prospects. Before selecting a platform for a firm with French-language client needs, the quality of the French-language intake flow should be tested carefully with native French speakers.
When evaluating chatbot platforms for a personal injury practice, the key evaluation criteria should go beyond pricing and general feature sets. Ask each vendor the following specific questions: Does the platform include pre-built templates for motor vehicle accident intake that are already configured for Ontario's SABS framework? Does it offer a native Clio integration, or only Zapier-based connection? Does the integration create both a contact and a draft matter in Clio, or only a contact? Is conversation data stored on Canadian servers, or on US-based infrastructure? Does the platform include built-in PIPEDA consent notices and legal disclaimers for law firms across North America? How quickly can a new PI intake flow be configured and tested — days or weeks? And critically: does the vendor have existing clients in Ontario's personal injury market who can speak to the platform's performance in the specific context of Canadian PI law? These questions separate vendors who have genuinely thought about the Ontario legal market from those offering a generic chatbot product dressed up with legal-specific marketing language.
| Platform | MVA Flow Templates | Clio Integration | Bilingual EN/FR | Price/Month | PIPEDA Setup |
|---|---|---|---|---|---|
| Smith.ai | Yes (with customization) | Native | Partial | $300–$600+ | Supported |
| Tidio | No (DIY) | Via Zapier | No | $49–$149 | Manual |
| Intercom | No (DIY) | Via Zapier | Partial | $150–$350 | Manual |
| LexScale.ai Custom | Yes (PI-specific) | Native + custom fields | Yes (full) | $400–$800 | Pre-configured |
If you are ready to explore exactly what a PI-specific chatbot would look like for your personal injury firm, our team at LexScale.ai's AI chatbot service for law firms builds intake flows configured specifically for SABS, MVA, and slip-and-fall cases. You can also read our broader guide on AI chatbots for law firms to understand the full landscape, review after-hours chatbot coverage strategies, and learn about chatbot lead qualification for law firms. When you are ready to take the next step, book a free strategy call with our team to discuss your firm's specific intake needs.