Lamatic.ai Accelerates Traq.ai's AI Innovation by 4x, Boosting User Satisfaction by 35%

Lamatic.ai

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AI & Startups #AI #Automation #Chatbots #RAG #Serverless #Low-code #AI Development #Productivity

About

Lamatic.ai is an AI middleware platform offering a visual, no-code/low-code environment for building, deploying, and optimizing agentic applications. Key features include a drag-and-drop workflow builder for RAG chatbots, semantic search, and document processing; seamless integrations with over 100 data sources like Slack and Google Drive; and serverless deployment with real-time monitoring for low latency and high reliability. The platform also provides Developer SDKs (Python, JavaScript) and GraphQL APIs for flexible integration into existing tech stacks, prioritizing data ownership and security.

The Challenge

What went wrong

Traq.ai, a leading provider of conversational AI, faced significant challenges in rapidly scaling their product roadmap to meet growing demand. Their engineering team struggled with the complexities of integrating advanced features like Retrieval-Augmented Generation (RAG) workflows, semantic search across diverse data sources, and ensuring real-time monitoring for production-grade AI agents.

Traditional development approaches were time-intensive, requiring custom coding for data ingestion from sources like Google Drive and Slack, and manual scaling for high-traffic deployments. This led to an estimated 6-9 months and over $50,000 in resources per project for building these capabilities in-house, diverting focus from Traq.ai's core mission of innovating user-friendly AI solutions.

Solution Approach

How it was solved

Lamatic.ai emerged as the ideal AI middleware platform, providing Traq.ai with a comprehensive solution to their development bottlenecks. The core of the approach involved:

Visual Workflow Builder: Traq.ai leveraged Lamatic's intuitive drag-and-drop interface to rapidly prototype and build RAG chatbots, semantic search engines, and document processing pipelines, empowering non-developers to contribute.
Seamless Integrations: One-click connections to over 100 data sources, including Slack, Google Drive, and S3, allowed Traq.ai to effortlessly vectorize and query their internal knowledge bases.
Serverless Deployment and Monitoring: Lamatic.ai's serverless infrastructure ensured instant scaling, real-time tracing, and performance dashboards, maintaining low latency and high reliability even during peak usage.
Developer SDKs: Python and JavaScript libraries facilitated the embedding of Lamatic flows into Traq.ai's existing stack, utilizing GraphQL APIs for streamlined data flow.

Traq.ai began by customizing Lamatic's pre-built templates, such as the RAG Chatbot and Slack Bot, to handle domain-specific queries and enhance customer service scenarios, while ensuring compliance with privacy standards through Lamatic's data ownership and encryption features.

Results Achieved

Success Metrics

The partnership with Lamatic.ai delivered significant, measurable improvements for Traq.ai across multiple key areas:

4x Acceleration in Development: Development cycles were dramatically shortened from months to as little as 2-4 weeks, enabling a 4x acceleration in roadmap delivery. A complex generative AI workflow for personalized lead nurturing, for instance, was deployed in under 10 days.
Enhanced Performance: Agent response times improved by 3x, with RAG-based queries achieving up to 95% accuracy, thanks to Lamatic's optimized vector search and low-latency infrastructure.
Significant Cost Efficiency: Traq.ai realized annual savings exceeding $40,000 by avoiding custom data extraction engines and reducing engineering hours by 70%, enabling non-developers to contribute directly to workflow iterations.
35% Uplift in User Satisfaction: Traq.ai's clients reported a 35% increase in satisfaction scores, with chatbots autonomously handling 50% more complex queries. Internal adoption also surged, with the Slack Bot template integrated for team-wide knowledge retrieval.

This transformation allowed Traq.ai to deliver innovative, scalable solutions that delight users and firmly established a competitive advantage in the conversational AI market.

Verified Testimonials

Real voices from people who witnessed this story firsthand

Alex Rivera

👔 CTO & Co-founder

🏢 Traq.ai

Before adopting Lamatic.ai, our engineering team was drowning in the complexity of building and maintaining production-grade RAG pipelines. What used to take us 6–9 months and tens of thousands of dollars per feature was suddenly reduced to just 2–4 weeks from idea to live deployment.

With Lamatic’s visual workflow builder and 100+ native integrations, we connected our Google Drive knowledge bases, Slack archives, and customer transcripts in hours — not weeks. The serverless edge deployment gave us instant scaling and sub-second latency even during traffic spikes, while the built-in tracing and evaluation dashboard caught hallucinations early and improved our query accuracy from ~75% to 95%.

The numbers speak for themselves:

4× faster feature delivery
70% reduction in engineering hours dedicated to AI infrastructure
$40,000+ saved annually on custom tooling and cloud costs
35% higher client NPS thanks to smarter, more reliable agents

Perhaps most importantly, product managers and domain experts can now prototype and iterate on new AI workflows themselves — no hand-holding from engineers required. Lamatic didn’t just accelerate our roadmap; it democratized AI development across our entire company.