Keenethics Case studies Shilo — A B2B AI SaaS Platform for Sales and Real Estate Teams

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About Shilo Challenge & Solution Results and Value Cooperation Process Related Success Stories Contact Us

Shilo — A B2B AI SaaS Platform for Sales and Real Estate Teams

Real Estate

Vendor/Integration Clients (Bonzo, CINC, External API, Sierra, and more)

million calls processed

10000+

total users

Optimize Data Processing in Sales and Real Estate Teams

Shilo is an AI-based platform that helps sales, real estate, and brokerage teams centralize fragmented data generated during communication with leads and customers, making it searchable, coachable, and action-ready for managers and agents.

Technologies:	React React Native Node.js GraphQL PostgreSQL Hasura Show 2 more
Team:	1 Team Lead, 5 Full-Stack Developers, 2 React Native Developers, 1 Automation QA, 1 QA
Timeline:	2 years of active development
Location:	USA

Challenge and Solution

Challenge:

Real estate and sales teams constantly communicate with customers and leads, generating a wealth of valuable information. Regrettably, this valuable customer data is fragmented. Important insights are hidden among CRMs, call recordings, webhooks, notes, appointments, and manager feedback loops. Shilo offers a product that helps organize those insights. While planning the product’s development, the Shilo team encountered two challenges that we helped address: creating a streamlined framework for data processing and ensuring its integration with existing technologies.

Solution:

From the product planning perspective, the client needed a highly detailed and streamlined structure for data analysis and processing. Our team assisted with detailing the specific steps for data processing and aligning them with existing technologies. From the technical perspective, we assisted with integrating different CRM formats, ensuring reliable async audio transcription and AI processing, and creating supporting systems for the product (tools for reprocessing, role play, mobile/web recording, permissions, billing/usage, and feature gating).

Result:

Shilo is a multi-surface SaaS platform rather than a single-purpose call tool. It can transcribe available call recordings, collect key datapoints in CRMs, such as manager notes, and then analyze them, offering users AI-based summaries, analysis, coaching insights, and action items. The product includes a web dashboard, mobile recording capabilities, backend workflows, integration frameworks, external API, reporting tools, AI insights, agentic CRM automation, training instruments, such as role play and voice coaching, and achievements/leaderboards integration. More than 390 organizations rely on it to decrease manual CRM work and make coaching faster.

Vendor/Integration Clients (Bonzo, CINC, External API, Sierra, and more)

million calls processed

total users

Solution 1. Building a Multi-CRM Conversation Intelligence Layer

Our team has created a normalized integration layer that connects Shilo with multiple CRM systems and vendors, imports call and lead data, and maps vendor-specific entities into a shared domain model. This model includes organizations, integrations, agents, people/leads, calls, appointments, teams, stages, sources, tags, and recordings.

Solution 2: Automating Call Processing with Transcriptions and AI Analysis

We’ve implemented a system that helps streamline call processing, saving time for management teams. Its data review pipeline automatically moves calls through audio processing, waveform generation, transcription, AI analysis, and terminal states. Temporal orchestration keeps the flow reliable while reusing existing backend business logic, following KISS/DRY principles.

Solution 3: Creating AI Coaching, Insights, and Performance Surfaces

The Keenethics team has delivered tools for automatic processing and analytics based on AI. The platform provides the following features: call, appointment, contact, insight, and agent pages, dashboards, report generation tools, and training instruments for role play and voice coaching. Shilo uses AI to summarize data, provide ratings, analyze objections, generate action items, detect speakers, create coaching notes, and deliver performance insights.

Solution 4: Adding Agentic CRM Automation

We’ve also implemented tools that help turn analytical insights into automation routines. Shilo goes beyond analysis by creating AI-driven workflows for CRM notes, tasks, appointment detection, and lead tagging. This turns conversation intelligence into operational action and reduces manual CRM administration.

Solution 5. Recording and Processing Tools for Conversations and Appointments

Shilo offers instruments for capturing in-person conversations and appointments. The app can process this information and transform it into transcripts, summaries, and reports that outline the full context of all sales conversations happening in a particular organization/business.

Solution 6. Leveraging Accumulated Contextual Knowledge

The product also leverages the accumulated contextual knowledge to facilitate autonomous sales coaching and script training. As a result, Shilo minimizes the need to rely on human-in-the-loop procedures that were essential for improving worker skills in the past.

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What Does Cooperation with Us Look Like?

Step 1. Assessment

Analyzing real-estate sales workflows, CRM data structures, call flows, manager coaching needs, and agent follow-up routines.

Step 2. Product planning and structuring

Designing the core domain model and platform architecture: organizations, integrations, agents, people/leads, calls, recordings, appointments, usage, permissions, and feature gates.

Step 3. Active product development

Implementing the recording/transcription/AI pipeline, building CRM integrations, and delivering product surfaces, such as dashboards, call review tools, and training instruments.

Step 4. Feedback and testing

Gathering feedback from stakeholders to ensure smooth integration of key features, optimizing interaction between various systems within the product (UI/UX, AI integration, and automation tools), and adding new features based on gathered information.

Step 5. Post-launch support

Launching the product, providing dedicated support to ensure smooth functioning of the product, and gathering stakeholder feedback to further perfect the product.