12-person mid-size law firm
A managed AI employee for a 12-person mid-size law firm took over first-pass intake, document triage, and conflict checks. Intake processing time dropped by 70%, the team redeployed to client work, and the engagement paid for itself within 11 weeks.
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At a glance
01
Engagement
Ongoing AI Employee subscription: free strategy call, scoped build, first workflow live in week one, full system in 4 to 8 weeks, weekly tuning and async Loom updates from there.
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Sector
Legal services
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Stage
Managed AI Employee engagement
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Result focus
Intake processing time dropped by 70%. The team redeployed to client work, hired a new associate within two quarters, and the engagement paid for itself inside 11 weeks of kickoff.
01
Client need
A managed AI employee for a 12-person mid-size law firm took over first-pass intake, document triage, and conflict checks. Intake processing time dropped by 70%, the team redeployed to client work, and the engagement paid for itself within 11 weeks.
02
Kavora role
Kavora designed, built, and now operates a managed AI employee that runs the firm's first pass inside the case-management and email tools the team already uses.
03
Outcome
Intake processing time dropped by 70%. The team redeployed to client work, hired a new associate within two quarters, and the engagement paid for itself inside 11 weeks of kickoff.
The challenge
What needed to change
A 12-person mid-size law firm was losing associate and paralegal hours to first-pass intake: opening every new enquiry, reading the attached documents, running conflict checks, and triaging urgency. The work was repetitive, time-sensitive, and not where the firm's senior judgment added the most value. Hiring another intake coordinator would have been a band-aid. The firm needed the work to stop requiring a person at all, without losing the institutional knowledge of which matters matter.
Our solution
How we approached it
Kavora ran a free strategy call, audited the firm's intake flow, scoped a single AI employee, and built the first workflow in week one. The AI employee reads every new enquiry, extracts the relevant facts and dates, runs conflict checks against the existing matter database, flags urgent matters, and queues the rest for human review. We monitor it continuously, ship a tuning improvement every week, and send the team a Loom update each time we change something so they always know what the system is doing. By month two the full intake pipeline is in production. By month three the engagement is paying for itself. After that, the work compounds: every week the system gets a little better, and the firm gets a little more capacity back.
What we delivered
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Managed AI employee built around the firm's intake and document-triage workflows
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Integration with the firm's existing case management, email, and document tools
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Continuous monitoring with a watchdog that catches breakage before the team notices
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Weekly tuning, async Loom updates, and a customer-facing kanban for requests
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Monthly outcome review with the managing partner and a rolling improvement roadmap
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Runbook for the firm's IT contact covering integrations, credentials, and escalation paths
Stack and systems
AI Employee · Document extraction · Workflow automation · Conflict-check integration · Watchdog monitoring · Async Loom updates · Customer-facing kanban
The impact
Numbers that moved
01
-70%. Intake processing time
Time from new enquiry to triaged matter file dropped by 70% on the firm's highest-volume practice area. Measured against the firm's own baseline, not an industry average.
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1 week. Time to first workflow
First intake workflow was live inside the first week of the engagement, on a private workspace integrated with the case management and email tools the team already uses.
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6 weeks. Time to full system
Full intake pipeline in production within 6 weeks of kickoff, with the watchdog running and the customer-facing kanban live.
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11 weeks. Payback
Engagement paid for itself inside 11 weeks, measured against the team's prior intake cost. The number is conservative: the firm has not yet counted the cost of the next hire it was able to make.
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1 new associate. Capacity added
Firm hired a new associate within two quarters of go-live, funded by the freed paralegal and associate hours. The AI employee did not replace anyone. It made the next hire possible.
Proof and handoff
Evidence of work
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Intake processing time reduced by 70% on the firm's highest-volume practice area
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First workflow live within the first week of the engagement
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Full intake pipeline in production within 6 weeks of kickoff
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Engagement paid for itself inside 11 weeks, measured against the team's prior intake cost
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Firm hired a new associate within two quarters of go-live, funded by the freed capacity
Handoff artifacts
01
Async Loom update after every change the AI employee ships
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Customer-facing kanban (Trello-style) for intake requests and visibility
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Monthly outcome review with the firm's managing partner and a rolling improvement roadmap
04
Watchdog that catches breakage before the team notices
05
Runbook for the firm's IT contact covering integrations, credentials, and escalation paths
Case study FAQ
Project questions
What did the AI employee actually do?
It handled the first pass on every new intake: pulled key details from incoming emails and PDFs, ran conflict checks against the firm's existing matters, routed urgent items to the right associate, and queued the rest for review. The team stopped doing the clerical opening of every file. We measure 'first pass' as the time from a new enquiry landing in the inbox to a structured, triaged matter file ready for the associate.
Did it replace the paralegals?
No. The paralegals moved to higher-judgment work: client communication, drafting, and case prep. The firm hired for a new associate role within two quarters of the AI employee going live because the team had bandwidth for more billable work. That is the pattern we look for: automation creates the headroom for the next hire to be a more senior, higher-return one.
What does the engagement look like from the inside?
Free strategy call. We audit the workflow, scope the first AI employee, and quote a fixed monthly fee. Week one: the first workflow is live on a private workspace, integrated with the tools you already use, with a watchdog running. From there, we ship one improvement per week and send a Loom update with every change so the team always knows what the system is doing. A monthly review covers outcomes, roadmap, and any pricing questions. Month-to-month, no lock-in.
What tools does the AI employee run on?
The same providers your team already uses, configured for your business. We do not bring a proprietary stack. Your workflows, your documents, and your data stay in your environment. We use best-in-class models and the same integration primitives that serious AI agencies use: meeting notes piped into the kanban, a Trello-style customer board for visibility, async Loom updates, and a watchdog that pages us when something changes shape upstream.
How long did it take to see results?
The first workflow was live inside the first week. The full intake pipeline was in production within 6 weeks, and the engagement paid for itself within 11 weeks of kickoff. We over-deliver on timeline, not the other way around. The first workflow is intentionally small so the team sees real value before we add the next one.
Related services
More ways we help
01
An AI employee that knows your business and improves every week. We build it, run it, and fix it before you notice. You don't touch tokens, models, or infrastructure.
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Founder-level engineers who embed with your team when you need extra capacity. Without a staffing layer in between.