deepsense.ai sells delivery rather than a self-serve product. The team positions itself as applied AI specialists who combine AI engineering, product and workflow design, and production infrastructure so that organizations end up with systems they can deploy, operate and scale themselves. The numbers it publishes are substantive: 200 commercial AI projects completed, around 120 AI experts, and a decade of AI work, with long-term clients including global brands, tech enterprises and scaleups across pharma, healthcare, financial services, manufacturing, telecoms and software.
Capability areas include LLM and VLM evaluation, agentic RAG pipelines (the company open-sources its ragbits framework), custom MCP servers as enterprise AI infrastructure, AI voice bots for operations, MLOps, computer vision, edge solutions and predictive analytics, plus AI advisory and adoption programs. A recurring theme in its case studies is measurement: one engagement reported an 84% response-quality rate for a self-improving deep research agent, another achieved up to 96.3% average GPU utilization for distributed training and inference workloads, and others focus on automating accounting workflows or building conversational analytics over Microsoft Fabric data.
Engagements typically start from uncertainty — deciding which workflow to automate and building the business case — then move into architecture, implementation, evaluation harnesses and handover. The company states official partnership status with major model and cloud providers including Anthropic, OpenAI and Gemini Enterprise, which matters when regulated enterprises need vendor-supported deployment paths. It also runs an applied AI talent program and publishes open-source resources and technical webinars for its engineering audience.
