Redesign Critical Workflows with AI-Driven Process Reengineering
GIGA IT analyzes, redesigns, and automates end-to-end processes with Artificial Intelligence, so cycle times shrink, errors decrease, and operations scale across mission-critical environments.
Trusted in mission-critical environments
Diagnose what slows your operation down
Process Mining
We analyze system logs and event data to reconstruct the real workflow, identifying delays, loops, and rework that slow execution.
Rebuild the process for speed, clarity, and scalability
Implement intelligent automation that delivers results in weeks
Assess
Diagnose what slows your operation down
Process Mining
We analyze system logs and event data to reconstruct the real workflow, identifying delays, loops, and rework that slow execution.
Redesign
Rebuild the process for speed, clarity, and scalability
Automate
Implement intelligent automation that delivers results in weeks
Technologies we use
Turn on the transformation
Process Redesign Built to Execute in Real Operations
Process reengineering matters only if it survives real constraints. GIGA IT combines executive consulting with production-grade Artificial Intelligence engineering to deliver actionable, fundable automation built for ROI, reliability, and compliance
Projects Delivered
Years in Complex Systems
Client Retention
Engineering Specialists
PRODUCTION-READY DECISIONS
GIGA IT validates automation priorities against data readiness, integrations, SLAs, and governance, so execution won’t stall in production.
EXECUTIVE ALIGNMENT
Decision workshops that align stakeholders on what to fund first, reducing friction and accelerating time-to-value with clear ownership.
FROM ROADMAP TO DELIVERY
Execute with your team, with our AI Engineering Teams, or via end-to-end delivery fast, accountable, and low-risk.
Measured Outcomes in Complex Production Environments
INDUSTRY
WHAT WAS AT STAKE
WHAT WE DID
BUSINESS IMPACT
» GIGA IT delivers speed, reliability, and operational confidence to Guidewire’s monthly financial close.
FAQ | AI Process Reengineering
What types of processes benefit most from AI Process Reengineering?
Processes with high manual effort, complex handoffs, or recurring bottlenecks typically in finance, operations, onboarding, supply chain, and shared services. GIGA IT redesigns mission-critical workflows where Artificial Intelligence eliminates waste and accelerates execution at scale.
Do we need process mining tools in place?
No. GIGA IT deploys process mining as part of the Assess phase to reconstruct the real workflow and quantify bottlenecks. There’s no prerequisite tooling. We work with your existing systems and data, even in legacy environments across 5 countries.
How long does a full reengineering cycle take?
Most engagements run 4 to 6 months, with measurable improvements often delivered within the first 6 to 10 weeks. GIGA IT’s production-grade approach ensures quick time-to-value while maintaining governance, reliability, and SLA-backed standards in mission-critical operations.
How is this different from traditional RPA projects?
Traditional RPA automates the existing workflow as-is. GIGA IT’s AI Process Reengineering redesigns the process first eliminating waste, standardizing inputs, and building a Target Operating Model before applying RPA, IDP, and AI agents to the redesigned, end-to-end flow.
What internal team is required?
Typically, one process owner, several SMEs, and an IT representative for system access. GIGA IT handles design, automation, and orchestration end-to-end through three delivery models: End-to-End Delivery, AI Engineering Teams, or Staff Augmentation minimizing the internal lift.
How do you ensure adoption?
Each GIGA IT initiative includes governance, training, performance metrics, and monitoring to ensure the new workflow is consistently followed. Adoption is treated as a core deliverable, not an afterthought backed by 97% client retention and 15+ years operating complex systems.
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Data science is used to study data in four main ways:
Descriptive Analysis
Descriptive analysis examines data to gain insights into what has happened or is happening in the data environment. It is characterized by data visualizations such as pie charts, bar or line graphs, tables, or generated narratives. For example, a flight booking service records data such as the number of tickets booked each day. Descriptive analysis will reveal peaks and dips in bookings, as well as months of high service performance.
Diagnostic Analysis
Diagnostic analysis is a deep or detailed examination of data to understand why something has occurred. It is characterized by techniques such as detailed analysis, data discovery and mining, or correlations. Various data operations and transformations can be performed on a given dataset to discover unique patterns in each of these techniques. For example, the flight service could perform detailed analysis of a month with particularly high performance to better understand the booking peak. This may reveal that many customers visit a specific city to attend a monthly sports event.
Predictive Analysis
Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. In each of these techniques, computers are trained to reverse-engineer causality connections in the data. For example, the flight services team could use data science to predict flight booking patterns for the next year at the beginning of each year. The computer program or algorithm can examine past data and predict booking peaks for certain destinations in May. By anticipating future travel needs of customers, the company could begin specific advertising for those cities as early as February.
Prescriptive Analysis
Prescriptive analysis takes predictive data to the next level. It not only predicts what is likely to happen but also suggests an optimal response to that outcome. It can analyze the potential implications of different alternatives and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and machine learning recommendation engines. Going back to the flight booking example, prescriptive analysis could examine historical marketing campaigns to maximize the advantage of the upcoming booking peak. A data scientist could project the results of bookings from different levels of spending on various marketing channels. These data forecasts give the flight booking company greater confidence in its marketing decisions.