An illustrated path beneath cherry blossoms with Mount Fuji in the distance.

AI implementation,carefully handled.

Selected implementation work

Evidence before promises.

These projects show how Meru studies an operation, connects the right systems, preserves human review, and measures what changed.

01Sales Intake Intelligence implementation artifact

Sales operations

Sales Intake Intelligence

A law firm sales and intake team used better lead context to prepare conversations, shorten discovery, and improve conversion from paid demand.

  • Discovery calls moved from 40 minutes to 15 minutes
  • Consult conversion increased from 9% to 14%
  • Managers gained clearer visibility into objections and missed follow-up
Read the case study
02Enterprise Legal Systems implementation artifact

Legal operations

Enterprise Legal Systems

Marketing attribution, telecom, intake, operations, finance, and AI-supported workflows were connected across a high-volume legal organization.

  • Reduced more than 1,000 automations to seven governed workflows
  • Improved answer rate from 60% to 98%
  • Implementation reached a 350+ employee operating environment
Read the case study
03BloomChat implementation artifact

Mid-market implementation

BloomChat

An AI-on-enterprise platform built with a managed services provider brought client records, conversations, reports, and proposal workflows into usable account context.

  • Deployed across varied client environments
  • Connected tickets, calls, email, documents, and reporting
  • Supported executive reporting and proposal preparation
Read the case study
04The Crash Data Project implementation artifact

Public data product

The Crash Data Project

A public research product created and operated by Meru AI that makes changing California crash records easier to examine and trace.

  • Preserves source fields and revision history
  • Standardizes geography, severity, vehicle, and road context
  • Delivers a live public map and research workflow
Read the case study

Services, clearly defined.

From a difficult question to a dependable system.

Explore services and deliverables
01

AI strategy and assessment

The opportunity is visible, but the right starting point is not.

A prioritized implementation plan grounded in value, readiness, and risk.

02

Implementation and integration

Useful context is divided across systems, documents, calls, and teams.

A production-ready AI workflow connected to the systems already carrying the business.

03

Ongoing improvement

The system needs ownership, review, training, and measurement after launch.

Clear operating procedures, human review, monitoring, and continued refinement.

How Meru works

Clear decisions. Careful implementation.

The process reduces uncertainty for leadership and technical burden for the team carrying the work.

  1. 01

    Understand the operation

    We study the people, workflows, systems, information, and decisions involved before recommending technology.

  2. 02

    Define the priority

    We identify the opportunity with the clearest business value, practical data access, and responsible path to adoption.

  3. 03

    Design the solution

    We establish the implementation plan, technical design, human review, ownership, and measures of success.

  4. 04

    Implement the system

    We build, integrate, test, and prepare the solution for use inside the systems your team already relies on.

  5. 05

    Support and improve

    We train the team, monitor performance, resolve exceptions, and refine the system as the business changes.

Built inside operations where the system had to work every day.

Jose Okabe has led implementation across attribution, telecom, intake, reporting, automation, and AI-supported workflows in a 350+ employee environment. That operating experience shapes how Meru evaluates risk, adoption, ownership, and measurable change.

About Jose and Meru AI

Begin with a conversation.

Bring the complexity. We will help establish the right next step.

Share the workflow, system, or decision you want to improve. You do not need a finished AI roadmap or a polished technical brief.