APPLIED AI FOR BUSINESS
Enterprise AI consulting and implementation, from use case to operation.
We help companies find real opportunities, prepare data, build AI solutions, and measure results with governance, security, and integration into existing processes.
DIRECT ANSWER
What is enterprise AI consulting?
Enterprise AI is the application of machine learning and generative AI to decisions, content, knowledge, and processes with defined goals, data, and controls.
Who it is forExecutives and leaders in innovation, technology, operations, customer service, data, and product who need to turn interest in AI into a dependable initiative.
CONTEXT
Enterprise AI consulting: what we do and why
AI adoption fails when it starts by picking a model instead of defining a problem. Our approach weighs impact, data feasibility, risk, and integration capacity before any build begins.
Projects can involve agents, enterprise search, document automation, forecasting, recommendation, computer vision, or AI components embedded in products. The architecture follows the outcome and the company's constraints.
From proof of value to production, we build evaluations, telemetry, safeguards, and improvement cycles. That gives you the evidence to decide when to expand, adjust, or stop an initiative.
IN DEPTH · 1/4
What artificial intelligence can do in your company
AI creates value when it solves a concrete problem in a process. These are the most common groups of application.
Knowledge and support
Answers based on the company's documents and systems, for internal teams and customers, with sources and access control.
Operations and quality
Computer vision for inspection, counting, workplace safety and flow analysis on cameras and images.
Documents and back office
Extraction and classification of information from contracts, invoices, forms and emails, with human validation where needed.
Data and forecasting
Models that anticipate demand, classify cases and flag anomalies from historical data.
Software development
AI as support for understanding existing systems, generating tests and speeding up delivery, always with engineer review.
IN DEPTH · 2/4
Our applied AI practice areas
Gemini Enterprise and AI agents
Rollout of Gemini Enterprise, Google Cloud's agent platform, connected to your data, with permissions and governance.
RAG and enterprise search
Systems that retrieve authorized knowledge and answer with sources, integrated with agents and applications.
Computer vision and AI video security
Solutions that interpret images and video to produce records, alerts and indicators tied to business decisions.
Process automation with AI
Flows that combine rules, integrations and models to cut manual work with proper supervision.
Strategy, data and governance
Use case prioritization, data preparation, evaluation, security and an adoption plan.
IN DEPTH · 3/4
What we deliver in each phase of an AI project
Outcome-driven discovery
We map processes, data and risks to choose a first use case with measurable impact and a business owner.
A pilot with success criteria
We build a usable version with real users and metrics agreed before the start, not after.
Integration into the workflow
The solution shows up in the systems and channels the team already uses, with identity, permissions and usage logs.
Evaluation and operation
We measure quality, cost, adoption and impact, adjust the solution and plan the scale-up from evidence.
IN DEPTH · 4/4
Responsible AI: security, privacy and human review
Data protected by design
Identity, segregation, encryption, retention and logging go into the architecture, not into a later patch.
Personal data in AI projects
Personal data follows a legal basis, minimization and retention rules defined with your legal team. This page is a general guide and is not legal advice.
Supervision proportional to risk
Higher-impact decisions keep human review, and the limits of automation are agreed from the start.
Transparency about limits
Models make mistakes. We make clear what the solution does well, where it fails and how errors are detected and corrected.
OUTCOMES
What artificial intelligence has to deliver to your company
Technical goals only matter when they improve security, speed, cost, experience or the ability to decide.
- 01Prioritized use case portfolio
- 02Prototypes validated with users
- 03Integration with data and systems
- 04Governance and evaluation
- 05Automation with the right supervision
- 06Scale and adoption plan
WHEN IT MAKES SENSE
When it makes sense to hire an AI consultancy
- There are plenty of ideas and no priorities
- Pilots never reach production
- Data is scattered or of poor quality
- The company has to choose a technology
- Security and privacy are a concern
- Return has to be demonstrated
HOW WE WORK
How we work, from AI strategy to operation
Short stages, visible criteria and knowledge transfer at every decision.
Strategy
We align objectives, use cases, risks, and metrics.
Preparation
We organize data, architecture, access, and evaluation.
Delivery
We build and integrate a solution people can use.
Operation
We monitor quality, cost, adoption, and impact.
DELIVERABLES
What you receive from an AI project
- Maturity assessment
- Opportunity map
- AI architecture
- Proof of value
- Solution in production
- Governance model
TRACK RECORD
Applied AI in real projects
More than 10 years in business and more than 170 projects delivered, including video analytics for events and mobility platforms.
- 10+
- years in business and Google partnership
- 170+
- projects delivered
- 97%
- NPS
Engagement intelligence at large-scale events
eSight.AI turned event footage into an objective read on audience attention, distraction and response across the talks.
See the project ↗Hard Rock Cafe CuritibaAnalytics and operational intelligence for events
Cameras that mostly served security now produce indicators for foot traffic, occupancy, dwell time and engagement across event operations.
See the project ↗SAGUIAn end-to-end platform for mobility surveys and intelligence
An ecosystem that brings together offline field collection, origin-destination surveys, video processing, vehicle classification, dashboards and reports.
See the project ↗FREQUENTLY ASKED QUESTIONS
Frequently asked questions about enterprise AI
What does an AI consultancy do?
It helps the company decide where AI creates value, prepare data and access, build and integrate the solution, evaluate results and operate securely. At EAGLE BS that ranges from use case prioritization to deploying agents, enterprise search and computer vision.
What is the difference between generative AI, machine learning and computer vision?
Generative AI creates text, images and code from instructions. Machine learning finds patterns in data to predict or classify. Computer vision interprets images and video. In practice the categories overlap, because generative AI and computer vision use machine learning techniques, and all three can coexist in one solution, depending on the problem.
How do you measure the return on an AI project?
By recording beforehand the time, cost, volume and errors of the current process and comparing them with the pilot result. You also measure answer quality, team adoption and operating cost. Without a baseline there is no way to prove the gain.
Will artificial intelligence run on its own, without supervision?
Not for higher-impact decisions. Models make mistakes, so we keep human review proportional to risk and monitor quality continuously.
Where should a company start with AI?
Start with a process that matters, repeats often, and can be measured, with accessible data and a business owner. Do not start with a broad technology purchase.
Do you only work with generative AI?
No. We combine generative AI, machine learning, computer vision, search, and automation according to the problem.
How do you protect corporate data?
The architecture has to account for identity, segregation, encryption, retention, logging, vendors, and usage policies from the start.
How long does a first project take?
It depends on data availability and integrations. A focused discovery can run in weeks; production requires validation, security, and operations proportional to how critical the system is.
Technical sources and references
FIELD EVIDENCE
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Artificial Intelligence
MLRC 2025 at Princeton: five principles for reproducible LLM evaluation
Five EAGLE BS lessons from MLRC 2025 at Princeton AI Lab on benchmarks, determinism, data, transparency, and robust LLM evaluation.Read article ↗
Google Cloud
Google Cloud Next 2025: AI, agents, and the new digital infrastructure
EAGLE BS at Google Cloud Next and Partner Summit 2025: Agentspace, Gemini 2.5, agents, cloud, data, and practical lessons for customers.Read article ↗TALK TO A SPECIALIST
Tell us the situation. We help you see the best path.
A focused conversation to understand context, risk, priority and the first workable step.