Turn Gemini Enterprise Into Production AI Systems
We help organizations design, build, and operationalize high-value solutions on Gemini Enterprise – from enterprise knowledge and custom agents to complex, regulated AI workflows.
AI-Native Engineering for Gemini Enterprise
deepsense.ai works with Google to help organizations move beyond Gemini Enterprise usage and build the custom AI systems their highest-value workflows require.
Our role is engineering: identifying where advanced AI can create meaningful operational value, connecting it to enterprise data and systems, building custom agents, and taking those solutions into production.
Google AI Native Partner, Google-certified engineering expertise, Gemini Enterprise & ADK delivery capabilities, Production AI from architecture to deployment
From Gemini Enterprise Investment to Production Value
Gemini Enterprise gives organizations a powerful foundation for enterprise AI. Real value comes when that foundation is connected to company-specific workflows, proprietary knowledge, operational systems, and the controls required to use AI at scale.
deepsense.ai closes that implementation gap.
Custom Agents Beyond the Standard Use Cases
Build organization-specific agents for workflows that generic assistants or ready-made agents can’t solve.
High-Value Business Workflows
Identify where Gemini Enterprise can materially improve throughput, decision quality, operating cost, customer experience, research velocity, or product capabilities.
Enterprise Data and Knowledge
Connect AI to the information employees actually need: internal repositories, databases, documents, APIs, business applications, and specialist data sources.
Evaluation, Security and Production Operations
Build reliability into the architecture from the beginning. Let’s implement evaluation, traceability, access controls, observability, cost management, and production monitoring.
Custom Agentic Systems With ADK
Build the agents your business actually needs
deepsense.ai designs and engineers custom agents using Google’s Agent Development Kit and Gemini Enterprise Agent Platform for workflows requiring proprietary logic, enterprise tools, multiple data sources, controlled actions, or coordination between specialized agents.
We own the technical path from workflow design and architecture through implementation, evaluation, integration, and production deployment.
Multi-Agent Workflows
Break complex processes into specialized agents that research, reason, validate, execute, and coordinate across defined responsibilities.
Tool and System Integration
Give agents controlled access to enterprise APIs, databases, applications, knowledge repositories, and operational tools.
Human-in-the-Loop Operations
Introduce review, approval, exception handling, and escalation where organizational accountability must remain with people.
Voice and Real-Time Agents
Build agentic voice experiences for workflows such as service operations, appointment management, specialist support, and customer interaction.
Production Agent Operations
Deploy agents with the evaluation, tracing, observability, security, and operational controls required for sustained production use.
Gemini Enterprise Solutions for Business-Critical Use Cases
Turn fragmented enterprise information into a usable AI knowledge layer
Gemini Enterprise can become a powerful interface to organizational knowledge, but high-value enterprise information rarely lives in one clean repository.
We design and build knowledge systems that connect structured and unstructured information across business applications, internal repositories, databases, documents, and external sources.
Common applications
- Enterprise knowledge assistants
- Research and intelligence systems
- Technical and product knowledge retrieval
- Internal policy and process assistants
- Customer and service support knowledge
- Scientific and regulated information retrieval
Impact
Faster access to institutional knowledge, less manual research, higher-quality decisions, and a scalable foundation for advanced Gemini Enterprise workflows.
Already Have Gemini Enterprise but Haven’t Reached the Highest-Value Use Cases?
Organizations often move quickly through initial search, productivity, and generic assistant use cases. The harder step is identifying where AI can materially change a department or business process and then engineering the data access, custom agents, controls, and integrations required to make it work.
We help you move from:
Client Success Stories
AI Agents, MCPs, MLOps
project
Specialist AI Engineering for High-Stakes Enterprise Programs
The firm gains AI engineering depth exactly where the program carries the most technical risk, while retaining the ownerships of the…
case-study
Accelerating Distributed AI Workloads with Up to 96.3% Average GPU Utilization
The optimized setup delivered measurable improvements across several workloads. A 32×A10 training job achieved 96.3% average GPU utilization, selected inference and…
project
MCP Server for Conversational Analytics in Beverage Manufacturing
The client gained a secure interface for accessing Microsoft Fabric data through ChatGPT, enabling business users to ask ad hoc analytical questions and…
project
Agentic AI for Investment Workflows. Securely Scaling Claude Across Deal, Legal, and Finance Teams
We assessed the Claude Enterprise and Claude Cowork setup, reviewed security settings, access boundaries, connector risks, and configuration gaps, then created…
project
Accelerating AI-Driven Workforce Automation with OpenAI Technology
The PoC showed potential to reduce manual effort, improve user experience, and shorten processing time for expense reporting impacting ~3,000 employees…
case-study
From AI Agent Prototypes to a Scalable Enterprise Architecture for Supply Chain Operations
The client gained a clear architectural roadmap for moving from early AI agent prototypes to a cohesive enterprise-scale platform strategy. The engagement reduced…
project
Investment Research Agent for an AI-Native Portfolio Management Platform
The scalable foundation can be extended with additional capabilities such as scenario stress testing, portfolio strategy optimization, or portfolio rebalancing.
project
Competitive Intelligence for Structured Tracking of Insurance Innovation
Instead of manually reviewing large volumes of data sources, stakeholders could quickly identify relevant innovation signals, compare competitors across selected dimensions, and…
project
Accelerating Product Launch Research from 1-2 Weeks to 3-4 Hours
Reducing analysis preparation time from 1-2 weeks down to 3-4 hours, freeing strategists for client-facing work and enabling the organization to scale…
Pharma and Life Sciences
case-study
From 3,000+ Medical Papers to Clinical Insight. Building an AI Assistant for Physicians
An AI-powered medical research assistant deployed across 13 countries helps 2 million physicians extract insights from 3,000+ high-quality medical sources spanning…
case-study
Automating Pharma-Compliant Content Creation with LLMs — From OCR to MLR-Ready Outputs
An end-to-end AI pipeline that transforms complex source documents into structured, high-fidelity, MLR-compliant promotional content, combining OCR, multimodal LLMs, and agent-based…
project
Healthcare & Life Sciences MCP Server Suite
We built six specialized MCP servers that unify access to key healthcare and life sciences data sources.
project
AI Copilot For Reimbursement Negotiations With Public Healthcare Authorities
Long-context processing enables the solution to synthesize extensive documentation into concise, relevant insights.
case-study
Guideline-Aware Protocol Generation: How LLMs Streamlined ENCePP-Aligned Study Design for Global R&D Teams
The solution enables faster, guideline-compliant protocol creation, boosting researcher productivity and accelerating time-to-market for new therapies.
project
Revolutionizing Arthritis Trials with AI-Driven Imaging Biomarkers
The client needed a more accurate and automated solution to enable smaller, cost-efficient trials while maintaining regulatory confidence.
case-study
Rethink Site Selection. Our AI Model Beats Traditional Choices 90% of the Time
90% of model-recommended sites outperformed legacy solutions in the US market
case-study
5x Boost in In-Silico Drug Discovery with a Multimodal LLM
The new LLM allows the client’s research team to explore molecular properties and relationships more effectively.
case-study
100% More Bookings: How AI Transformed Appointment Scheduling
The AI voicebot transformed an unstable product into a robust AI scheduling voicebot that responds 10x faster, uses 20x fewer tokens per…
project
From Days to Hours: Automating Information Updates for Compassionate Use of Drugs
The process of source verification was reduced to just 2 hours, with added benefits of detailed reports on document changes.
project
Optimizing Clinical Trial Planning with AI-Driven Site Selection
A retrospective analysis showed that 90% of trials using model-recommended sites outperformed those with lower scores, increasing the success rate and reducing…
project
91% Accurate Skin Analysis for Precision Beauty Solutions
The system achieved 91% accuracy, outperforming human evaluations, and enabled the automated selection of the most suitable cosmetics for various skin…
project
Accelerating AI Strategy and Product Development
In a 3-week project, we reviewed their machine learning practices, including MLOps, to boost efficiency.
project
AI-Enhanced Facial Analytics for Next-Gen Beauty Devices
Launched in March 2024, the product integrated our models, achieving key-point detection with an error margin of less than 20 pixels on ~2000×1200 pixel…
Financial & Insurance
project
Helping Private Equity Turn AI Opportunities Into Scalable Value-Creation Roadmaps
We proposed an AI Product Accelerator model combining AI advisory, product strategy, and implementation planning. The approach started with discovery workshops, stakeholder…
project
Agentic AI for Investment Workflows. Securely Scaling Claude Across Deal, Legal, and Finance Teams
We assessed the Claude Enterprise and Claude Cowork setup, reviewed security settings, access boundaries, connector risks, and configuration gaps, then created…
project
Investment Research Agent for an AI-Native Portfolio Management Platform
The scalable foundation can be extended with additional capabilities such as scenario stress testing, portfolio strategy optimization, or portfolio rebalancing.
project
Competitive Intelligence for Structured Tracking of Insurance Innovation
Instead of manually reviewing large volumes of data sources, stakeholders could quickly identify relevant innovation signals, compare competitors across selected dimensions, and…
case-study
Cutting Response Time by 95% in Customer Service
The Proof of Concept demonstrated that AI can process thousands of customer emails daily with high accuracy, reducing handling time from minutes…
project
Streamlining Data Processing with Ray Infrastructure Optimization
The client adopted our recommendation to transition to the Anyscale Platform, reducing internal tooling maintenance and improving Ray adoption.
project
Stock Market Fraud Prevention with AI-Powered Detection
We developed a web monitoring system that analyzes investment-related social media, forums, and message boards.
Gemini Enterprise for Regulated and Business-Critical Use Cases
Some of the most valuable AI opportunities are also the least tolerant of unreliable outputs.
deepsense.ai brings production AI engineering together with domain experience in regulated and data-intensive environments, helping organizations implement systems where evidence, source traceability, permissions, validation, and human accountability matter.
Pharma & Life Sciences
Build AI across the therapy lifecycle – from drug discovery and clinical development to evidence generation, market access, medical affairs, and commercialization.
Relevant use cases include scientific research agents, clinical-trial intelligence, evidence synthesis, protocol development, knowledge retrieval, and specialist agentic workflows.
Our experience includes AI systems that have accelerated evidence-generation workflows from weeks to days and protocol-development processes from months to weeks.
Healthcare
Build AI for providers, payers, digital-health platforms, and healthcare operations.
Use cases include clinical knowledge systems, patient access, appointment automation, clinician assistants, medical information retrieval, voice AI, and secure AI product capabilities.
Our production experience includes an AI medical research assistant supporting evidence-based knowledge access and a voice-based appointment solution scaled across thousands of physicians.
Financial Services
Engineer research, analysis, decision-support, and operational agents for environments where traceability, access control, and governance are fundamental requirements.
Use Gemini Enterprise and custom agents to connect complex information sources, automate research-intensive work, and support controlled multi-step workflows.
Why deepsense.ai for Gemini Enterprise Implementations
deepsense.ai combines deep AI engineering with enterprise implementation expertise to turn Gemini Enterprise capabilities into production-ready AI systems. Our team of 120 AI experts (80% senior) brings 10 years of applied AI experience and more than 200 commercial AI projects.
You get AI systems designed to work in real business environments.
We take each solution from use-case qualification and architecture through engineering, evaluation, integration, deployment, and production operation.
You get AI agents designed around the way your organization actually works.
We build sophisticated LLM and agentic systems that integrate with your data, tools, and business processes.
You get implementation expertise aligned with the Google ecosystem.
Our Gemini Enterprise practice works closely with Google teams across customer opportunities, technical implementation, and production use cases.
You get AI systems built for environments where trust, control, and accountability matter.
We have delivered AI solutions across Pharma, Healthcare, Financial Services, and other regulated industries, with reliability, governance, traceability, and human oversight.
From Business Requirement to Production System
01. Qualify the Use Case
Align business objective, executive ownership, operational KPI, required data, risk constraints, and technical feasibility.
02. Architect the Solution
Define agent architecture, retrieval, integrations, permissions, evaluation criteria, security, and production requirements.
03. Build and Integrate
Engineer the custom agents and workflows, connect enterprise systems and data, and implement the necessary controls.
04. Evaluate
Test factuality, retrieval quality, agent behavior, workflow completion, security boundaries, and business performance against agreed criteria.
05. Deploy and Scale
Move the system into production, establish observability and operating practices, and extend successful patterns into additional workflows.
An AI Implementation Partner Built for Complex Delivery
We bring together AI engineering, product and workflow design, enterprise integration, evaluation, and production architecture to build systems organizations can deploy, operate, and scale.
commercial AI projects
completed
world-class
AI experts
years
of AI expertise