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Salesmsg

Salesmsg: SMS Marketing Solution

NEKLO transformed Salesmsg into a high-performance MarTech leader. By migrating to Soketi and PostgreSQL, we delivered 18 months of features in 10 weeks, including an integrated generative AI text assistant.

Salesmsg:

About the client

SalesMessage provides text messaging solutions to businesses across various industries. Their text marketing solution is accessible worldwide, allowing organizations to send and receive two-way texting messages and calls and automate communication workflows.
Country:
USA
Industry:
Telecom
Duration
2020 – ongoing
Model:
B2B, SaaS
Team size:
3 product managers, 7 front-end developers, 12 back-end developers, 3 DevOps, 2 mobile developers, 14 QA engineers, 2 designers
Key technologies:
PHP Laravel, Node.js, PostgreSQL, Amazon ElastiCache, Amazon S3, Amazon SQS, Amazon MSK (Kafka), Amazon MQ (RabbitMQ), Amazon EKS, AWS Lambda
See all

Client’s challenge

The client aims to help businesses of all sizes improve customer engagement by providing a secure sms solution for business. Their initial product gained traction from small businesses; however, scaling it turned out to be challenging. Because of their inefficient communication between several distributed development and QA teams, bugs remained unresolved for weeks.
  • SMS and push limits
  • Insufficient functionality
  • Poor platform performance
  • Bugs and errors lingering for weeks
  • Delayed message delivery
  • Slow database queries
  • No automation
  • No AI support

Solution

NEKLO helped the client turn their initially malfunctioning texting solution for business into a secure, all-in-one texting and calling platform. We developed new features, fixed existing errors, and optimized application performance. Our scope of involvement included:

Pusher WebSockets to Soketi migration

We transferred the server from PusherWebSockets to Soketi due to limitations with Pusher that prevented messages from reaching users in real time. This allowed us to get rid of subscription costs and overcome push limitations.

MySQL to PostgreSQL migration

Despite attempts to optimize using Aurora Serverless and capacity scaling, slow query performance persisted. NEKLO managed a seamless migration to PostgreSQL, implementing a more robust schema capable of handling complex relational queries. This resulted in significantly faster data retrieval and improved overall application stability under heavy loads.

New feature development

Within the first 10 weeks, we deployed more fixes and features than the client's onshore development team did in 18 months. We overhauled the contact management system — introducing bulk uploads, sophisticated tagging, and shared inboxes

AI text assistant development

Beyond simple "canned responses," we implemented a generative AI layer that allows users to instantly draft, refine, and personalize outgoing messages based on specific campaign goals. This integration significantly reduces the time-to-send for marketing teams and ensures higher engagement through more human-centric, AI-optimized copy.

Automation and triggers

We adopted an API-first philosophy: every action available in the UI is fully executable via the API. We built out an extensive automation engine featuring custom triggers, webhooks, and public API access. To minimize user error in complex campaigns, we introduced a "Test Trigger" preview mode, allowing users to simulate and validate automated workflows before they go live to their customer base.

Integrations

We implemented integrations with various CRM and tools like HubSpot, Salesforce, Intercom, Infusionsoft, ActiveCampaign, Pipedrive, Make, Keap, Slack, Aircall, Google Workspace, Webhook, and Zapier. Moreover, we added a Chrome extension to make calls and send messages directly from a browser.

Process

Using our expertise in MarTech and high-load communication platforms, we implemented a structured recovery and scaling strategy to turn the client's vision into a high-performance reality.

1. Infrastructure audit and architectural roadmap

We began with a comprehensive technical audit to diagnose the root causes of message latency and database bottlenecks. Our team identified that the existing Pusher and MySQL setup could no longer support the platform's scaling requirements. We mapped out a migration path to Soketi and PostgreSQL, ensuring that the transition would resolve real-time delivery issues while providing a cost-effective, enterprise-grade foundation for future growth.

2. Core infrastructure and database migration

With the roadmap defined, we executed a seamless migration of the communication layer and the primary database. By shifting to Soketi, we bypassed previous push limitations and eliminated third-party subscription costs. Simultaneously, we migrated the data environment to PostgreSQL to optimize query processing. This phase was critical in stabilizing the platform, ensuring zero data loss, and preparing the architecture to handle complex, high-volume automated workflows.

3. Product development and AI integration

Once the core was stabilized, we shifted into a high-velocity delivery cycle to clear the feature backlog. Utilizing an API-first approach and agile sprints, our team deployed a comprehensive suite of CRM integrations and automation triggers. In parallel, we developed and integrated the generative AI text assistant. Through continuous CI/CD and rigorous QA, we achieved a delivery pace that allowed us to launch more critical features in 10 weeks than the previous team had in 18 months.

1. Requirements alignment

A cross-functional team of AI engineers, backend developers, DevOps specialists, and a business analyst worked with the client’s stakeholders to review existing workflows, map data sources, and define integration points within the EMR platform.

2. Prototyping and validation

Once the scope and requirements were agreed upon, we built a proof of concept to test several machine learning approaches. The goal was to ensure the model could handle the type, quality, and volume of the client’s data and reach the level of accuracy needed for real use, for example, detecting unexpected patterns without producing false positives. The prototype helped verify data consistency, tune preprocessing logic, and validate model outputs before production rollout.

3. Project management routine

Throughout the engagement, the teams worked in shared Jira and Confluence spaces, held daily check-ins via Slack, and tracked progress against delivery milestones. We ran regular demo sessions for the client to review intermediate results and clarify expectations around data handling and report outputs. This helped refine the solution as the model was tested on real clinical data and keep the project on schedule.
go
go

Technologies

Front-end

React
Vue 2

Back-end

Apache Kafka
Elasticsearch
Express.js
Laravel
MySQL
Node.js
PHP
PostgreSQL
Python
RabbitMQ
Redis
Swoole
TypeScript

DevOps

AWS Aurora
AWS DynamoDB
AWS EKS
AWS ElastiCache
AWS Lambda
AWS MQ (RabbitMQ)
AWS MSK
AWS S3
AWS SQS
GitHub Actions
Jenkins
Terraform

Mobile

Embrace
Firebase
MobX
React Native
Sentry
Twilio
Wix React Native Navigation

Deliverables

01
Pusher Websockets to Soketi migration
02
Vue to React migration
03
Database optimization
04
Built-in real-time rich analytics
05
Geo-targeting segmentation
06
Integration with third-party systems
07
Achieved STIR/SHAKEN compliance
08
ISO/IEC 27001 certification

Results

We’ve completely redesigned the solution and helped the client to present Salesmsg 2.0 on desktop and mobile apps. The enterprise sms solution was recognized on Inc. 5000 America’s Fastest Growing Private Companies and Top 5 for the Easiest To Use SMS Marketing Solution on G2.
Salesmsg mobile apps are now available on iOS and Android and have thousands of downloads. The app rating improved to 4.7 stars based on over 1K reviews.
349
%
growth in three years
6
#
for the top-rated SMS software
4
x
team growth as the platform evolves

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