Redesign Critical Workflows with AI-Driven Process Reengineering

Move beyond inefficient legacy workflows. We analyze, redesign, and automate end-to-end processes so cycle times shrink, errors decrease, and operations scale with speed, traceability, and operational control.

Transform Your Processes

Trusted in mission-critical environments

Diagnose what slows your operation down

Before redesigning a workflow, we analyze how work actually flows across teams, systems, and data sources. Using process mining, operational data analysis, and stakeholder input, we identify bottlenecks, manual dependencies, and structural constraints affecting cycle time, reliability, and scalability.

Process Mining

We analyze system logs and event data to reconstruct the real workflow, identifying delays, loops, and rework that slow execution.

Task Analysis
We break down manual activities and handoffs to quantify workload, effort, and automation potential across the process.
Bottleneck Identification
We identify operational bottlenecks such as queue delays, approval cycles, and inconsistent decision points.
Risk & Error Analysis
We detect tasks prone to errors, compliance risks, or inconsistent outcomes, improving reliability and control.
Tech & Data Assessment
We assess current systems, integrations, and data quality to understand technical constraints and automation readiness.
Value & ROI Mapping
We prioritize opportunities based on operational impact, cost reduction, cycle-time improvement, and feasibility.

Rebuild the process for speed, clarity, and scalability

Using insights from the assessment phase, we redesign the process architecture through a Target Operating Model (TOM) that removes friction, simplifies execution, and prepares workflows for AI-driven automation.
Target Operating Model (TOM)
We define the optimal flow steps, roles, rules, and controls—to eliminate waste and streamline execution.
Standardization
We unify criteria, SLAs, inputs, validations, and handoffs so work becomes predictable, measurable, and repeatable.
AI Opportunity Design
We identify where AI adds value, classification, routing, decisions, predictions and map intelligence into the workflow.
Automation Blueprint
We define which tasks will be automated (RPA, IDP, LLM/Agents) and how each connects to systems and data sources.
Orchestration Model
We plan how people, bots, and systems interact end‑to‑end to reduce delays and ensure continuity across the process.
Change & Adoption Plan
We prepare teams, roles, training, and governance to ensure the new workflow is adopted consistently.

Implement intelligent automation that delivers results in weeks

We deploy AI-driven automation, integrate systems, and orchestrate the full workflow so your critical process becomes faster, more reliable, and easier to scale.
RPA Implementation
We automate repetitive, rules-based tasks—accelerating execution and reducing human effort drastically
Intelligent Document Processing (IDP)
We extract, classify, and validate documents using AI to remove manual data entry and eliminate errors.
LLM & AI Agents
We incorporate language‑based automation for reasoning, triage, exception handling, and decision support.
System Integrations
We connect apps, data sources, and platforms to remove handoffs and ensure information flows seamlessly.
End‑To‑End Orchestration
We coordinate bots, humans, and systems across the full workflow for consistent, predictable execution.
Impact Tracking & Iteration
We measure cycle time, throughput, errors, and cost savings—continuously improving the automated process.
Assess

Diagnose what slows your operation down

Before redesigning a workflow, we analyze how work actually flows across teams, systems, and data sources. Using process mining, operational data analysis, and stakeholder input, we identify bottlenecks, manual dependencies, and structural constraints affecting cycle time, reliability, and scalability.

Process Mining

We analyze system logs and event data to reconstruct the real workflow, identifying delays, loops, and rework that slow execution.

Task Analysis
We break down manual activities and handoffs to quantify workload, effort, and automation potential across the process.
Bottleneck Identification
We identify operational bottlenecks such as queue delays, approval cycles, and inconsistent decision points.
Risk & Error Analysis
We detect tasks prone to errors, compliance risks, or inconsistent outcomes, improving reliability and control.
Tech & Data Assessment
We assess current systems, integrations, and data quality to understand technical constraints and automation readiness.
Value & ROI Mapping
We prioritize opportunities based on operational impact, cost reduction, cycle-time improvement, and feasibility.
Redesign

Rebuild the process for speed, clarity, and scalability

Using insights from the assessment phase, we redesign the process architecture through a Target Operating Model (TOM) that removes friction, simplifies execution, and prepares workflows for AI-driven automation.
Target Operating Model (TOM)
We define the optimal flow steps, roles, rules, and controls—to eliminate waste and streamline execution.
Standardization
We unify criteria, SLAs, inputs, validations, and handoffs so work becomes predictable, measurable, and repeatable.
AI Opportunity Design
We identify where AI adds value, classification, routing, decisions, predictions and map intelligence into the workflow.
Automation Blueprint
We define which tasks will be automated (RPA, IDP, LLM/Agents) and how each connects to systems and data sources.
Orchestration Model
We plan how people, bots, and systems interact end‑to‑end to reduce delays and ensure continuity across the process.
Change & Adoption Plan
We prepare teams, roles, training, and governance to ensure the new workflow is adopted consistently.
Automate

Implement intelligent automation that delivers results in weeks

We deploy AI-driven automation, integrate systems, and orchestrate the full workflow so your critical process becomes faster, more reliable, and easier to scale.
RPA Implementation
We automate repetitive, rules-based tasks—accelerating execution and reducing human effort drastically
Intelligent Document Processing (IDP)
We extract, classify, and validate documents using AI to remove manual data entry and eliminate errors.
LLM & AI Agents
We incorporate language‑based automation for reasoning, triage, exception handling, and decision support.
System Integrations
We connect apps, data sources, and platforms to remove handoffs and ensure information flows seamlessly.
End‑To‑End Orchestration
We coordinate bots, humans, and systems across the full workflow for consistent, predictable execution.
Impact Tracking & Iteration
We measure cycle time, throughput, errors, and cost savings—continuously improving the automated process.

Turn on the transformation

Let's talk

Strategy built to execute in real operations

AI strategy matters only if it survives real constraints in mission-critical environments. We combine executive consulting with production-grade engineering to deliver an actionable, fundable roadmap, built for ROI, reliability, and compliance.

Projects Delivered

Years in Complex Systems

Client Retention

Engineering Specialists

Sab Miller

PRODUCTION-READY DECISIONS

We validate priorities against data readiness, integrations, SLAs, and governance so execution won’t stall.

Sab Miller

EXECUTIVE ALIGNMENT

Decision workshops that align stakeholders on what to fund first, reducing friction and accelerating time-to-value with clear ownership.

Sab Miller

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

Guidewire | Reducing Financial Close from 3 Hours to 15 Minutes

INDUSTRY

Financial Services – Fintech | Operations back office with critical, manual-heavy workflows

WHAT WAS AT STAKE

Guidewire’s monthly financial close relied on manual validations across multiple teams and systems. The process required more than three hours of intensive work, introduced frequent error risk, and could not scale with the organization’s growth.

WHAT WE DID

We redesigned the end-to-end closing workflow. Using process mining, we identified bottlenecks and rebuilt the process architecture to remove unnecessary steps and standardize inputs. Then we implemented intelligent automation combining RPA, validation rules, and AI-assisted checks to orchestrate the entire workflow.

BUSINESS IMPACT

  • –91.67% in cycle time (from 3 hours to 15 minutes)
  • Near‑elimination of manual, repetitive tasks and operational errors
  • Standardized, scalable process ready for future growth

» Guidewire now runs its financial close with speed, reliability, and operational confidence.

View case

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.

Do we need process mining tools in place?

No. We can deploy process mining as part of the assessment phase to reconstruct the real workflow and quantify bottlenecks.

How long does a full reengineering cycle take?

Most engagements run 4–6 months, with measurable improvements often delivered within the first 6–10 weeks.

How is this different from traditional RPA projects?

Traditional RPA automates the existing workflow. AI Process Reengineering redesigns the process first, eliminating waste before automation.

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.

How do you ensure adoption?

Each initiative includes governance, training, performance metrics, and monitoring to ensure the new workflow is consistently followed.

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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.​