Litify AI: what ships native, and what firms build on top.

Salesforce Litify sits at the center of the operational stack for many law firms. The workflows that route through it are the workflows where AI investment shows up first on the P&L: intake to matter routing, conflict checks, document automation, matter-to-template matching, timesheet reconciliation. A commissioned AI layer that integrates cleanly with Salesforce Litify addresses those workflows without forcing the operator to migrate off the system of record (the one system that holds the official copy of a record).

The most common integration pattern is a read-and-suggest pattern. The AI layer reads structured records out of Salesforce Litify, runs the workflow it was commissioned to run, and writes back a suggested action that a human reviewer approves inside Salesforce Litify's native UI. The system of record stays Salesforce Litify. The AI layer never bypasses the human-in-the-loop step for production-data writes.

For lighter-touch workflows we have shipped read-only layers that extract structured data out of Salesforce Litify, hand it to a reasoning step, and emit a report. No writes back. The operator uses the report as input to their existing decision process. Time to ship is faster, integration risk is lower.

For heavier workflows where the audit trail is structured and the failure cost is bounded we have shipped fully bidirectional integrations that close the loop end-to-end with structured logging. These engagements take longer (six to seven weeks rather than four to five), require more diligence on the read/write permissions inside Salesforce Litify, and ship with a runbook for human review of edge cases.

Integration with Salesforce Litify happens at one of three levels: the API layer, the webhook (an automatic notification one system sends another when something changes) layer, or the database layer. The right level depends on what permissions the operator's Salesforce Litify instance grants, what data the workflow needs to see, and what data the workflow needs to write.

API layer. Read and write through Salesforce Litify's documented REST (a standard way for software to exchange data over the web) or SOAP endpoints. Cleanest, most maintainable, vendor-supported. Works when the data the workflow needs is exposed through the API.

Webhook layer. Subscribe to Salesforce Litify events, react to them in real time, write back through the API. Good for workflows that need to fire when a specific record changes.

The way to know whether this playbook applies to your own setup is the $499 AI-Ready Audit, which checks it against your actual stack rather than the general case.

Database layer. Direct read against the underlying database, where the API does not expose what is needed. Brittle, requires direct hosting access, used only as a last resort and always with the operator's explicit approval.

Treating the integration as an afterthought. The AI work is the easy part. The integration is the hard part. Operators that under-invest in the integration boundary spend the entire build cycle fighting authentication, rate limits, and edge-case schema. The commission scopes the integration boundary in the first week.

Skipping the human-in-the-loop step too early. Closing the loop end-to-end on day one is a recipe for hidden errors. Every engagement starts with human review of every AI output. Only after the operator has seen the output quality hold for sixty to ninety days does the human-in-the-loop step relax to spot-check.

Underestimating the data-cleanup work. Salesforce Litify contains data the operator has entered over years. Some of it is clean. Some of it is not. The AI layer's quality is bounded by the data it reads. Cleaning happens as part of the build, not as a prerequisite for it. If the data is unworkable we flag it in the audit call.

Building bespoke when a product would suffice. If Salesforce Litify already has a productized AI feature that covers the workflow, the operator should evaluate it before commissioning a custom build. We will tell the operator honestly when that is the right answer.

Specific numbers are bound by NDA (a signed non-disclosure agreement) but the pattern is consistent across the engagement set: the operator runs the workflow faster, with fewer hands, and with a structured record of every AI-generated suggestion alongside the human approval.

A typical Salesforce Litify commission scope: one or two specific workflows, read-and-suggest pattern, four-to-seven-week build cycle, fixed fee from $10K depending on integration depth and workflow complexity. The audit call identifies the workflow. The prototype demonstrates feasibility against the operator's real data inside seven to ten days. The production build ships inside the operator's own cloud tenant (a private cloud account) under NDA.

The operator owns the Salesforce Litify integration code, the AI prompts, the model selection, and the data pipeline at handoff. We do not retain a license, a recurring fee, or a vendor relationship that the operator depends on.

Litify has been adding AI into the platform feature by feature. Publicly announced pieces include AI-powered demand drafting and Billables AI for automatic time capture, alongside summarization of records already held in Litify objects. Ask which features are generally available on your edition, because an announcement date and an availability date are rarely the same date.

Litify prices per user and quotes AI capability inside the package rather than publishing a separate AI price. Get the fully loaded per-user annual number with the AI features enabled, then multiply by your three-year headcount plan. That figure decides build versus buy, not the first-year discount.

This is the question practitioners actually ask on forums, and the answer is that running on Salesforce is Litify's biggest advantage. Your matters, intakes, and financials sit in a documented data model your own build can read and write. Firms needing intake scoring, medical-record summarization, or case-value modeling beyond the native features build against those same objects.

Litify is built for high-volume practice, so the native features are aimed at that shape of work. The ceiling appears in the parts specific to your firm: how you score a lead, how you value a case, what makes a file settlement-ready. A vendor cannot ship your underwriting. That is the piece worth commissioning.

Two real ones. Salesforce governor limits and managed-package boundaries shape what a build can do inside the org, so architecture decisions have to be made up front. And an upgrade can move objects underneath you. Both are managed the same way: keep Litify as the system of record and the AI layer as read and suggest, never as a shadow database.

Both, in that order. Turn on the native features, run them for a quarter, and write down the work they did not touch. One anonymized law firm ended up with a commissioned platform running matters, invoices, and IOLTA (the client trust account a law firm must keep separate) trust: 13,296 matters, 4,396 clients, 5,684 invoices, trust reconciled byte-identical. A build should target only what the platform genuinely leaves behind.

On a commissioned build, yes. The code, the prompts, the model selection, and the data pipeline transfer at handoff, and the system runs inside the firm's own tenant (a private cloud account). That is the structural difference from a subscription: there is no license to renew and no vendor to wait on for a change request.

A prototype against the firm's real data in 7 to 10 days, then a production build in 5 to 7 weeks for one or two scoped workflows. The fee is fixed and set before the build starts, from $10,000 depending on integration depth.

If nobody in the firm can name the workflow that is leaking, that is the answer. Buy nothing, commission nothing, and spend a month counting. Firms that skip that step buy a platform, use a fraction of it, and still have the original bottleneck a year later.

No, and the pitch to be suspicious of is the one that says it does. What AI removes from intake is the typing, the record lookup, the duplicate check, and the after-hours gap where a lead goes cold. The decision on whether a case is worth signing stays with a person, and any system that buries that decision inside a score is a liability.

The narrow, high-frequency work first: summarizing a new matter file, drafting the first pass of a demand, capturing time nobody logged, and flagging files that have gone quiet. Wide systems fail because nobody trusts them. A narrow one that is right every day earns the room to expand.

The firm owns the code, so nothing is locked to us. Fixing it yourself carries no fee; firms that want us to diagnose and update it instead can add the optional $997 monthly stewardship plan, cancel on 30 days notice.

Fixed fee from $10,000, set after the $499 AI-Ready Audit and after the workflow is scoped against your own Litify org. During the build we need a Salesforce admin who can grant sandbox access, a named point of contact to approve scope, and time from one or two intake or billing staff to review the prototype against real matters. Every engagement starts with human review of every AI output, so a case-value or drafting suggestion the model gets wrong is caught before it reaches a client rather than after.