AI for Decision Intelligence: Go Beyond Data, Drive Actionable Strategy
Stop reacting to yesterday's reports.
Start shaping tomorrow's outcomes with AI-augmented decision-making.
In today's hyper-competitive landscape, the quality and speed of your decisions define your market position. Gut feelings and historical data are no longer enough. Decision Intelligence (DI) is the next frontier, combining data science, machine learning, and management science to create a framework for superior, data-driven choices. We build AI systems that don't just show you what happened; they recommend what to do next, simulate potential outcomes, and learn from every interaction to continuously refine your strategic edge.
Trusted by Industry Leaders and Fast-Growing Startups
Why Partner with Errna for Decision Intelligence?
We bridge the gap between complex AI capabilities and tangible business results. Our approach is built on a foundation of deep technical expertise, strategic business acumen, and a relentless focus on your ROI.
Explainable AI (XAI)
We build 'glass box' models, not black boxes. Our solutions provide clear, auditable reasoning behind every recommendation, ensuring your team trusts, understands, and maintains control over AI-driven decisions.
Measurable ROI Focus
We start with your business goals. Every project begins with a comprehensive analysis to model expected ROI, focusing on metrics like cost reduction, revenue uplift, and risk mitigation to ensure value is delivered.
Seamless Integration
Our DI solutions are designed to integrate with your existing data ecosystem (ERP, CRM, data lakes). We use a phased, agile approach to minimize disruption and deliver value from day one.
Enterprise-Grade Security
With certifications like CMMI Level 5, ISO 27001, and SOC 2, we build security and compliance into the core of our solutions, protecting your most valuable asset: your data.
Human-in-the-Loop Design
Our systems augment, not replace, your experts. We design collaborative DI frameworks where AI handles the computational heavy lifting, empowering your team to apply their domain knowledge and make the final strategic call.
Full Data Lifecycle Support
Data readiness is often the biggest hurdle. We provide end-to-end data engineering, from data cleansing and preparation to building robust pipelines, ensuring a solid foundation for powerful AI.
Rapid Prototyping
We believe in demonstrating value quickly. Our process includes rapid prototyping and pilot programs on focused use cases, allowing you to see the potential of DI in your organization with minimal upfront investment.
Continuous Learning Systems
The market is always changing, and so should your models. We build systems with feedback loops that learn from new data and outcomes, ensuring your decision-making capabilities become smarter over time.
Global Expertise, Local Support
With over 1000 in-house experts and a track record since 2003, we bring a wealth of cross-industry experience to solve your unique challenges, providing dedicated support tailored to your needs.
Our Decision Intelligence Services
We offer a comprehensive suite of services designed to embed intelligent decision-making into the core of your operations. From strategic forecasting to real-time operational optimization, we tailor our AI solutions to your most critical business challenges.
Predictive Analytics & Forecasting
Move beyond historical analysis to accurately predict future trends, customer behaviors, and market shifts. We build custom machine learning models that analyze vast datasets to provide you with a clear view of what's coming, enabling proactive strategy and resource allocation.
- Demand Forecasting: Optimize inventory and staffing by accurately predicting product demand and service needs.
- Customer Churn Prediction: Identify at-risk customers and intervene with targeted retention strategies before they leave.
- Revenue & Sales Forecasting: Build reliable financial models that account for seasonality, market trends, and internal factors.
Prescriptive Analytics & Optimization
Go beyond prediction to get clear recommendations on the best course of action. Our prescriptive models analyze constraints, variables, and goals to suggest optimal solutions, automating complex decisions and maximizing outcomes.
- Dynamic Pricing Engines: Automatically adjust pricing in real-time based on demand, competition, and inventory levels to maximize revenue.
- Next-Best-Action Recommendations: Empower your sales and marketing teams with AI-driven suggestions for customer interactions.
- Resource Allocation Modeling: Optimize budgets, staffing, and asset deployment for maximum efficiency and impact.
Intelligent Supply Chain Optimization
Transform your supply chain from a cost center into a strategic advantage. We apply AI to create resilient, efficient, and transparent supply networks that can adapt to disruptions and anticipate future needs.
- Inventory Management Automation: Use AI to maintain optimal stock levels, reducing carrying costs and preventing stockouts.
- Logistics & Route Optimization: Develop algorithms that find the most efficient delivery routes, factoring in traffic, fuel costs, and delivery windows.
- Supplier Risk Assessment: Proactively identify potential disruptions in your supply chain by analyzing supplier performance and geopolitical factors.
AI-Powered Risk & Anomaly Detection
Identify threats and opportunities that human analysts might miss. Our anomaly detection systems continuously monitor your data streams to flag unusual patterns in real-time, from fraudulent transactions to failing equipment.
- Fraud Detection: Build real-time systems that analyze transactions and user behavior to stop fraud before it impacts your bottom line.
- Predictive Maintenance: Monitor sensor data from industrial equipment to predict failures, enabling proactive maintenance and preventing costly downtime.
- Cybersecurity Threat Intelligence: Analyze network traffic and system logs to identify emerging cyber threats and vulnerabilities automatically.
Real-World Impact of Decision Intelligence
We deliver measurable outcomes. Explore how we've helped industry leaders turn data into a decisive competitive advantage.
Case Study: Dynamic Pricing for a Global E-commerce Retailer
Client Overview
A leading e-commerce platform with over 10 million SKUs faced intense market competition and shrinking margins. Their static, rule-based pricing strategy couldn't adapt to real-time market dynamics, leading to lost sales and excess inventory.
The Problem
The client needed a way to optimize prices across their entire catalog automatically, responding to competitor pricing, demand fluctuations, inventory levels, and promotional events without manual intervention.
Key Challenges
- Scaling price adjustments across millions of products.
- Avoiding price wars that erode brand value.
- Factoring in complex variables like shipping costs and regional demand.
- Ensuring pricing decisions aligned with overall business strategy (e.g., market share growth vs. profit maximization).
Our Solution
Errna developed and deployed a custom AI-powered dynamic pricing engine. The solution integrated directly with their inventory and sales platforms, using machine learning to analyze real-time data streams.
- Developed a reinforcement learning model to test and learn optimal price points.
- Integrated competitor price scraping and market trend analysis via APIs.
- Created a "what-if" simulation dashboard for managers to test pricing strategies.
- Implemented guardrails and business rules to maintain brand integrity and prevent extreme price volatility.
Case Study: Predictive Maintenance for an Automotive Manufacturer
Client Overview
A major automotive parts manufacturer was experiencing significant losses due to unplanned downtime on their assembly lines. Their preventative maintenance schedule was inefficient, leading to both unnecessary servicing and unexpected critical failures.
The Problem
The client needed to shift from a calendar-based maintenance schedule to a predictive model that could identify potential equipment failures before they occurred, allowing for targeted, just-in-time servicing.
Key Challenges
- Integrating data from thousands of disparate IoT sensors.
- Identifying subtle patterns in sensor data that precede a failure.
- Building a system that provides actionable alerts with sufficient lead time.
- Avoiding "alert fatigue" by ensuring a low false-positive rate.
Our Solution
We implemented an end-to-end predictive maintenance solution powered by anomaly detection and survival analysis models. The system ingested real-time data from PLCs and IoT sensors across the factory floor.
- Built a centralized data pipeline to clean and aggregate sensor data (vibration, temperature, pressure).
- Trained an LSTM neural network to recognize patterns indicative of imminent failure.
- Developed a dashboard that visualized equipment health scores and remaining useful life (RUL) estimates.
- Integrated the alert system with their existing CMMS to automatically generate work orders for at-risk machinery.
Case Study: AI-Powered Credit Risk Scoring for a FinTech Lender
Client Overview
A fast-growing FinTech company specializing in small business loans was struggling to scale its underwriting process. Their traditional credit scoring model was slow, heavily reliant on manual review, and not accurately pricing risk for non-traditional applicants.
The Problem
They needed a more sophisticated, automated credit risk model that could analyze a wider range of alternative data, provide instant decisions for most applicants, and more accurately predict the likelihood of default.
Key Challenges
- Incorporating unstructured data (e.g., business reviews, bank transaction data) into the model.
- Ensuring the model was fair, unbiased, and compliant with financial regulations.
- Building an explainable AI (XAI) component to justify decisions to regulators and applicants.
- Integrating the model into their existing loan origination system via API.
Our Solution
Errna designed and built a machine learning-based credit risk engine that significantly outperformed their legacy system. The solution used a gradient boosting model trained on both traditional and alternative data sources.
- Utilized Natural Language Processing (NLP) to extract features from bank statements and online business data.
- Implemented fairness and bias detection toolkits to ensure equitable lending decisions.
- Built a SHAP (SHapley Additive exPlanations) dashboard to provide clear, human-readable explanations for each credit decision.
- Deployed the model as a scalable, low-latency microservice for seamless integration.
Our Technology Stack & Tools
We leverage a best-in-class ecosystem of technologies to build robust, scalable, and effective Decision Intelligence solutions.
Our Agile & Collaborative Process
We follow a structured, transparent, and iterative process to ensure your Decision Intelligence solution is delivered on time, on budget, and perfectly aligned with your business objectives.
1. Discovery & Strategy
We begin by immersing ourselves in your business. We conduct stakeholder interviews and workshops to deeply understand your challenges, goals, and existing data landscape. The output is a clear business case and a strategic roadmap.
2. Data Preparation & Engineering
We assess your data readiness and build the necessary pipelines to collect, clean, and structure your data. This foundational step ensures the quality and reliability of any AI model we build.
3. Prototyping & Model Development
In this phase, our data scientists explore various algorithms and build initial models. We focus on rapid prototyping to quickly demonstrate potential value and refine our approach based on your feedback.
4. Integration & Deployment
Once a model is validated, we productionize it. Our engineers deploy the solution into your existing tech stack as a scalable and reliable service, ensuring seamless integration with your operational workflows.
5. Monitoring & Continuous Improvement
Our work doesn't end at deployment. We continuously monitor model performance, track business KPIs, and implement feedback loops to retrain and improve the system over time, ensuring it adapts to your evolving business needs.
What Our Clients Say
Our success is measured by the success of our clients. Here’s what they have to say about our partnership.
"Errna didn't just deliver an AI model; they delivered a strategic capability. Their team's ability to understand our complex business logic and translate it into a functional, explainable AI system was remarkable. They are true partners in innovation."
"The supply chain optimization engine Errna built for us has been transformative. We've reduced shipping costs by over 15% and can now react to disruptions in hours, not weeks. Their expertise in both AI and logistics is unparalleled."
"We were struggling with data overload. Errna helped us cut through the noise and build a decision intelligence framework that provides our leadership with clear, actionable insights. The clarity it has brought to our strategic planning is invaluable."
"The professionalism and technical depth of the Errna team exceeded our expectations. They handled our sensitive financial data with the utmost security and delivered a fraud detection system that is both incredibly accurate and easy for our analysts to understand."
"As a startup, we needed to move fast without compromising on quality. Errna's agile approach and rapid prototyping allowed us to get our AI-powered recommendation engine to market in record time. They felt like an extension of our own team."
"What impressed me most was Errna's focus on the 'last mile'—making sure the AI insights were actually adopted and used by our business teams. Their commitment to user training and building intuitive dashboards was key to the project's success."
Frequently Asked Questions
Have questions? We have answers. Here are some of the most common inquiries about our Decision Intelligence services.
Business Intelligence (BI) primarily focuses on descriptive analytics, showing you what has happened in the past through dashboards and reports. Decision Intelligence (DI) is the next evolution. It incorporates predictive and prescriptive analytics to not only show you what happened, but also predict what will happen next and recommend the best course of action to achieve a desired outcome. It's about moving from passive reporting to active, augmented decision-making.
Absolutely. In fact, most of our engagements begin with a data engineering and strategy phase. We understand that perfect data is rare. Our experts specialize in building robust data pipelines, cleaning and integrating disparate data sources (from ERPs and CRMs to IoT sensors and third-party APIs), and establishing a solid data foundation upon which powerful AI models can be built.
This is a critical concern, which is why we are committed to Explainable AI (XAI). We use techniques like SHAP and LIME to interpret model behavior and build intuitive dashboards that show the key drivers behind each AI recommendation. This transparency ensures your team can understand, trust, and confidently act on the insights provided, maintaining full human oversight.
The ROI varies by use case but is always our primary focus. We work with you upfront to build a detailed business case, targeting specific KPIs like cost reduction, revenue growth, or efficiency gains. Through our agile, pilot-based approach, we aim to demonstrate tangible value within the first 3-6 months, allowing you to validate the ROI and scale the solution with confidence.
Not necessarily. We offer flexible engagement models. We can build and hand over a fully automated system to your existing technical team, or we can provide ongoing "AI-as-a-Service" support, where we manage the model monitoring, retraining, and maintenance for you. Our goal is to make Decision Intelligence accessible, regardless of your in-house data science capabilities.
Ready to Make Smarter, Faster Decisions?
Let's talk about how AI-powered Decision Intelligence can transform your operations and give you an unbeatable competitive edge. Schedule a free, no-obligation consultation with our experts today.