Unico Connect boosts enterprise AI.
Unico Connect establishes new MLOps practice to support enterprises managing AI.
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Unico Connect, an AI-native software agency, has launched a dedicated MLOps practice to help enterprises keep AI systems running reliably after they go live. The move targets a stubborn industry problem: approximately 85% of machine learning models fail to launch or collapse shortly after deployment, according to Fortune Business Insights.
CEO Malay Parekh put it plainly: "Deployment is only the beginning. Models drift, data changes, business requirements evolve." The new practice covers the full AI lifecycle — monitoring, retraining, and optimization — building on Unico's existing work in AI agents, large language models (LLMs), and retrieval-augmented generation (RAG), a technique that grounds AI answers in real company data.
The Deployment Gap Driving the MLOps Market
MLOps — short for Machine Learning Operations — works the same way DevOps once did for regular software. It bridges the gap between building an AI model and keeping it accurate in the real world. Unlike traditional code, AI models degrade over time as data shifts and business conditions change, a problem known as "data drift," according to Gartner.
The market opportunity is enormous. The global MLOps market is projected to grow from $1.7 billion in 2024 to as much as $52.2 billion by 2034 — a compound annual growth rate of up to 40%, per IMARC Group. Yet only 25% of AI initiatives delivered expected ROI over three years, an IBM study found, mostly due to poor operations after launch.
Unico's Path From No-Code Shop to AI Production Partner
Unico did not arrive at MLOps overnight. The company moved into no-code tools like Bubble and FlutterFlow in 2021, then added LLMs and early AI agents in 2023. By January 2024, it had expanded into the U.S. market after winning "Best NoCode Agency of 2023," per Business Wire.
In May 2026, Unico formalized a dedicated AI Services vertical. Three weeks before the MLOps launch, it published a guide on running AI agents in production, flagging orchestration failures as the top risk, according to The Kingston Whig Standard. The MLOps practice is the direct response to those gaps. DesignRush recently ranked Unico among the top 10 AI development agencies for its ability to show how models behave on "messy, real-world data," per DesignRush.
WhatsApp Voice Orders: MLOps Working in the Field
One of Unico's clearest production examples involves Ashokraj Transport & Logistics. Buyers sent purchase orders as WhatsApp voice notes — often mixing Hindi and English. Manual transcription was slow and error-prone. Unico built a system using LangChain and LangGraph to convert those voice messages into clean, structured purchase orders automatically.
The result: the company scaled operations without adding back-office staff. Unico says it now delivers a full AI voice-to-order system in 13 to 14 weeks. The firm also reports that 80% of its production code is AI-generated and human-reviewed, cutting documentation time by 35%.
On-Premise Servers and the Privacy vs. Scale Trade-Off
Cloud-based MLOps tools from AWS, Google, and Microsoft currently control 54.9% of the market, per Precedence Research. But enterprises in fintech and healthcare are pushing back. They want AI systems kept on their own servers for data privacy compliance. Unico says it supports hybrid deployments — cloud and on-premise — to meet both needs.
The MLOps community has flagged a hiring mismatch as well. Many companies bring in PhD researchers for these roles, but the job actually demands production engineering skills — infrastructure, monitoring, and pipelines. Agencies like Unico are positioning themselves to fill that gap for mid-market companies that cannot afford a dedicated ML team costing $500,000 a year or more.
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