Scale Production AI Without Building It All In-House
We work as an extension of your organization, bringing together senior AI engineering, architecture, governance, and operations to turn your AI roadmap into reliable production systems.
Move Beyond Project-by-Project AI Delivery
AI roadmaps stall when every initiative is treated as a separate project. We provide the senior expertise, delivery capacity, and operating structure needed to build AI as a scalable production capability.
One Partner: From Architecture to AI Operations
deepsense.ai embeds senior AI engineers, architects, and technical leads into your organization to build, deploy, and scale production AI systems. Instead of managing separate consultants, contractors, and implementation vendors, you get one accountable partner covering architecture, engineering, enterprise integration, reliability, governance, and cost control.
AI Capability and Operating Model
We help establish the structure required to make AI delivery repeatable. This can include:
- AI portfolio and use-case governance;
- decision rights and ownership;
- architecture and engineering standards;
- evaluation and quality frameworks;
- security and compliance requirements;
- delivery processes and success metrics;
- AI Center of Excellence support.
Embedded Senior AI Teams
Senior AI engineers, architects, and tech leads work directly with your product, data, engineering, security, and business teams.
The pod is configured around your roadmap and can expand, contract, or change its technical profile as priorities evolve.
You gain technical ownership and specialist capability—not simply additional CVs.
Production AI Delivery
We design and build production systems across:
- AI agents and agentic workflows;
- enterprise RAG and knowledge systems;
- LLM-powered products and copilots;
- document intelligence;
- voice AI;
- computer vision;
- predictive and classical machine learning;
- MLOps and LLMOps;
- AI evaluation and monitoring;
- edge AI.
AI Operations and Scale
Our involvement does not end when the first version is deployed.
We help operate, improve, and expand AI systems through:
- production monitoring and incident resolution;
- quality and regression evaluation;
- model, prompt, retrieval, and workflow optimization;
- infrastructure and inference cost control;
- security and access management;
- reliability and performance improvements;
- rollout to new users, workflows, and markets;
- continuous knowledge transfer to internal teams.
Showcasing Our State-of-the-Practice
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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…
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project
Technical Advisory and Roadmapping to Reduce QA Costs with OpenAI Codex Agents
We delivered a 6-week AI Advisory project with the goal of delivering a technical architecture design of the new AI-powered QA platform, and a clear roadmap…
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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…
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project
Enterprise-Grade ChatGPT Rollout with Security and Governance for 1,500+ Users
ChatGPT Enterprise launched securely and on schedule for more than 1,500 users, with no disruption to the rollout.
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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…
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project
Detecting 98.5% of Network Anomalies in Real Time for Telecom Providers
Together, we developed proprietary machine learning algorithms tailored to the specific requirements of the telecom industry.
What Our Clients Say About Us
“At Unstructured, we have been delighted to partner with deepsense.ai, a collaboration that has significantly accelerated the development across our Product Roadmap. Specializing in the complex domain of unstructured ETL for RAG, deepsense.ai has matched our technical intensity and contributed across various functional areas.”
Brian S. Raymond
Founder & CEO at Unstructured
“Sky partners with deepsense.ai across a wide range of topics, including an ML model development, predictive analytics, credit risk, price elasticity, and AI consulting. In price elasticity modeling, deepsense.ai strengthened pricing strategy and RevOps through robust model design and optimization, delivering accurate, adaptable solutions with clear business impact.”
Kostis Manolitzas
Group Head of Data Science Innovation and AI at Sky
” Our collaboration shows how to apply cutting-edge AI in niche markets and industries where we seek a competitive advantage. We share efforts in our innovative approach, which differentiates us from peers and startups, embodying our belief that it’s better to disrupt ourselves than to be disrupted by the competition.”
Burkhard Boeckem
CTO at Hexagon AB
“One particular example where deepsense.ai’s expertise really stood out was their involvement in the development of our GenAI-powered frontline worker digital assistant. The solution integrated a diverse set of data sources, providing assistance to frontline employees with relevant responses in their moment of need.”
Tom Bianculli
CTO at Zebra Technologies
“deepsense.ai has been a dependable and high-quality partner to Brainly’s AI research, development, and operations efforts over the past 3 years. Their team has integrated seamlessly with our in-house teams, bringing top-tier talent and a collaborative spirit that drives innovation.”
Bill Salak
CTO & SVP Operations at Brainly
“We engaged deepsense.ai for an AI Advisory engagement with the aim of reviewing and enhancing our AI capabilities and practices. deepsense.ai was adept at identifying practical quick-win improvements in our AI operations, providing guidance for our long-term investment priorities in the AI domain and ensuring a thorough transfer of knowledge to our internal AI team throughout the engagement.”
Mariusz Gralewski
CEO at DocPlanner
“We have successfully partnered with deepsense.ai on multiple R&D projects. The deepsense.ai team was able to effectively partner and work hand-in-hand with our development team, complementing our domain knowledge with deep expertise in AI/ML and predictive analytics.”
Ned Taleb
Co-Founder & CEO at B-Yond
“deepsense.ai quickly delivered a Proof of Concept for a code completion tool, using a state-of-the-art technological stack, including the newest available LLMs and libraries. They also led an excellent LLM discovery workshop that jump-started AdaCore’s integration of LLM solutions into our business processes and products.”
M. Anthony Aiello
Head of Product & Innovation at AdaCore
“The deepsense.ai solution for anomaly detection helped expedite the problem detection times and decreased the number of monitoring failures. We were also impressed by their team’s technical know-how and solution-based mindset.”
Nitin Navare
CTO at LogicMonitor
“Over a three-year period, deepsense.ai has augmented our internal capabilities with a dedicated team of consultants, helping us enhance our MLOps Platform. Their involvement from early ideation and design phases through to the entire software development cycle has ensured a high standard of engineering maturity and adherence to industry best practices.”
Carsten Ingerslev
Head of Advanced Analytics
at Danish Business Authority
“We engaged deepsense.ai to improve some of the functionalities of our AI-driven transportation management platform in terms of Estimated Time of Arrival (ETA) and On-time Probability (OTP). The ease of collaboration with deepsense.ai’s team and their adaptability to our feedback and ideas made the entire development process seamless and productive.”
Paul Beavers
CTO (2020-2023) at a transportation management platform provider
Built for Your AI Maturity
Whether you are building AI capability from the ground up or scaling an existing team, we adapt the model to your starting point.
Build Your AI Capability
For organizations with promising use cases but no clear roadmap, operating model, governance, or senior delivery capacity.
We define priorities, establish the architecture and operating model, and build the first production systems.
Outcome: A structured AI program instead of disconnected pilots.
Scale Your Existing AI Capability
For organizations with established teams but insufficient senior capacity or specialist expertise to deliver the full roadmap.
We embed senior AI teams, close capability gaps, and unblock complex production work without adding permanent headcount.
Outcome: Faster delivery while your teams retain ownership and control.
From AI Priorities to Production at Scale
We support the full AI lifecycle, from selecting the right opportunities to building, operating, and expanding production systems.
AI Advisory
Define the roadmap
Prioritize use cases, align stakeholders, and establish the architecture, governance, and delivery model required for implementation.
Engineering Acceleration
Build production systems
Embedded senior AI teams design, integrate, evaluate, and deploy AI solutions within your existing technology and workflows.
AI Operations & Scale
Operate and expand
Maintain reliability, control costs, improve performance, and scale proven AI systems across new teams and use cases.
Deep AI Engineering for Industry-Critical Workflows
We combine advanced AI capabilities with an understanding of the workflows, data environments, and operational requirements that shape each industry. From AI agents and enterprise RAG and LLM evaluation to MLOps, computer vision, and edge systems, we build production solutions around business processes.
Pharma
AI systems for regulatory, clinical, medical affairs, pharmacovigilance and quality workflows—supported by enterprise knowledge architecture, agents, evaluation and controlled human review.
Healthcare
AI for healthcare operations, payer workflows, clinical knowledge access and AI-enabled products, designed around privacy, traceability and real-world adoption.
Financial & Insurance
Controlled automation for claims, underwriting, compliance, investigations and knowledge-intensive operations where accuracy, auditability and integration matter.
Private Equity
Repeatable AI implementation programs focused on operational improvement, productivity and scalable capability across portfolio companies.
Manufacturing & Industrial
AI for engineering knowledge, quality, maintenance, field service and operational decision support across complex IT and edge environments.
Software & Technology
AI product engineering, code modernization and production infrastructure for companies building AI into customer-facing products and internal platforms.
Proven AI Delivery at Enterprise Scale
We combine AI engineering, product and workflow design, and production infrastructure to deliver systems organizations can deploy, operate and scale.
commercial AI projects
completed
world-class
AI experts
years
of AI expertise
Why deepsense.ai for Scaling AI
Senior technical depth, embedded delivery, and production accountability to help you build and scale AI without adding every capability in-house.
Official AI and Cloud Partners
We work closely with leading model, cloud and AI infrastructure providers to help clients move faster from technology selection to reliable production deployment. These partnerships strengthen our platform expertise, implementation capabilities and ability to support complex enterprise environments across the full AI delivery lifecycle.
FAQ
What does an enterprise AI implementation partner do?
An enterprise AI implementation partner helps an organization move from AI priorities and prototypes to systems that are integrated, evaluated, secure, and production-ready. This can include use-case planning, architecture, engineering, integration, deployment, governance, and ongoing operations.
How is this different from AI consulting?
Traditional AI consulting often focuses on assessments, strategy, or recommendations. deepsense.ai connects advisory directly to engineering and production delivery, enabling the same collaboration to progress from planning through implementation to operations.
Is the Extended AI Hub a form of AI team augmentation?
It includes flexible engineering capacity, but the model goes beyond traditional team augmentation. deepsense.ai provides senior technical leadership, delivery structure, architecture, specialist expertise, reusable frameworks, and support for production outcomes—not only individual team members.
Can deepsense.ai work with our existing AI and data teams?
Yes. Many engagements are designed specifically to extend existing teams. We can provide specialist expertise, additional senior capacity, independent architectural guidance, or a complete delivery pod for selected workstreams.
Can you help us build an AI Center of Excellence?
Yes. We can help define the AI operating model, governance, technical standards, portfolio processes, delivery practices, evaluation framework, and team structure required to establish or strengthen an AI Center of Excellence.
Do you take over the entire AI program?
Not necessarily. The model is designed to complement the organization’s existing capabilities. The client retains strategic ownership, while deepsense.ai supports selected areas such as architecture, technical leadership, engineering delivery, evaluation, or operations.
Can the team scale as our roadmap changes?
Yes. Delivery pods can expand, contract, or change their capability mix as priorities evolve. This provides access to specialist expertise without requiring permanent headcount for every stage of the roadmap.
Which AI technologies do you support?
Our work includes AI agents, LLM applications, RAG, voice AI, AI evaluation, MLOps and LLMOps, computer vision, edge AI, document intelligence, predictive analytics, and classical machine learning.
Do you support AI systems after deployment?
Yes. We support monitoring, reliability, evaluation, optimization, infrastructure, security, cost management, model and platform upgrades, and expansion to additional workflows.
How does an engagement typically begin?
Engagements usually begin with a discovery discussion around your roadmap, current capability, and most important delivery blockers. We then define the appropriate starting point: advisory and planning, a focused production implementation, an embedded engineering pod, or support for an existing AI system.