Engineered. Purpose-built. Production-ready. Business-first. Outcome-driven.
Practical AI

Honestly, both - and we think that's the point. A lot of "AI companies" are a few years old and light on engineering discipline. On the contrary, we are a 25-year-old software company that has been able to develop our artificial intelligence/machine learning capability over a pre-existing engineering culture and hence our development process, architecture, and testing capabilities have been established. That is how we have managed to go from concept to reality with our AI.

It is deliberate that we are not tied down to any particular model. On a day-to-day basis, we work with GPT, Claude, Gemini, Llama, Grok, DeepSeek, Mistral, Qwen, and Command for reasoning and language tasks, and also with LangGraph, CrewAI, and AutoGen for agents, and Pinecone, Weaviate, Qdrant, and Milvus for retrieval. Sometimes we also work with voice and media-generation platforms such as Deepgram, ElevenLabs, Runway, Midjourney, and others depending on the nature of the project. The crux of the matter is, we select the model based on the requirements of the task at hand.

We hold direct partner status with Anthropic (Claude Partner) and OpenAI (Select Partner), we're an Expert Partner listed on the n8n Marketplace, and we're a Microsoft Solutions Partner. Not only is this just another kind of title; it also means that you are ahead of the game when it comes to the capability of the model, and you have direct access to the vendor if something needs to be escalated.

Each, based on the situation - but we're transparent about which applies. There are many cases where connecting to a pre-existing model API is an adequate solution, and it would be foolish to do more work than needed. On the other hand, there are instances when model training and tuning based on your data becomes inevitable, whether for categorizing documents, identifying sensitive information, or even improving routing efficiencies, and this becomes a skill that most organizations do not possess. We will be upfront regarding the requirement of the specific model for your project, rather than always defaulting to the expensive route.

Complete suite: SharePoint (intranet, migrations, apps for Teams, process automation), Dynamics 365 (Business Central, Finance and Supply Chain, Sales and Customer Service, Field Service), Power Platform (Power BI, Power Apps, Power Automate, Dataverse, AI Builder), and Azure and Cloud Services - migrations, automation, big data analysis, and enterprise mobility. This is an actual business, not something tacked onto the side of AI.

Our headquarters is in Vadodara, with engineering teams in Indore and Ahmedabad. From there we deliver globally - we have client-facing presence in Coventry (UK/EMEA), a US desk covering Microsoft 365 work, and Perth for our Australia/APAC clients. So, you get an India-based engineering cost base with people in your own time zone when you need to talk to someone directly.

Most people start with our two-week AI Discovery Sprint, a fixed-scope engagement that ends with a clear brief on where AI is actually worth your money and where to begin. Depending on your needs, it could either be a 90-Day AI Sprint, where we develop one feature from pilot to production; an Acceleration Partnership, where we run many workstreams at once over many quarters; or Embedded AI Talent, where we embed our engineers into your team as needed. You don't have to know which one you need before you talk to us - that's what the first call is for.

Private Azure tenant deployment, Private Link and use of customer-managed keys is included by default in our builds and is not a paid upgrade, as your information will only reside in your environment unless you choose otherwise. With respect to the process aspect, we follow the standards of CMMI 3 level and are certified for ISO 27001:2022 and ISO 9001, meaning that security and quality control are already included in our processes.

Some real numbers from the production systems we have created: MatScan AI system automated 85% of our customer’s invoice processing in just minutes, whereas it previously took many hours. AI video analysis helped to reduce the defect detection time on a manufacturing line by 60%. And AI assistants processing these inquiries reduced the customer response time to a quarter of what it used to be. On average, for all our AI initiatives, our clients experience 70% process automation and 3.2x return on investment.

Our expertise spans from finance and insurance to retail, automobiles, health care, manufacturing, logistics, airlines, real estate, SaaS, education, life sciences, aerospace, EVs and batteries, and energy. These industries may be different, but the only common factor here is that these are all real industries with operational data that can be automated by AI.