Intelligent Automation Services

Automation is only as reliable as the data it runs on. We find where data breaks, duplicates, or gets lost between systems, and fix it before any model makes decisions on it. Our AI Solution Accelerator™ speeds up the analysis of large and poorly documented systems.

• Architecture Mapping. We catalog systems, services, and their interactions, highlighting tight coupling, integration drift, and points of failure that limit scalability and observability.
• Data Lineage Analysis. We trace how data moves through the system, reconstruct process variants, and detect schema divergence, latency points, and logic fragmentation.
• Dependency Review. We surface fragile seams, shared state, and undocumented integrations that prevent modular orchestration and runtime control.
• Automation Readiness Evaluation. We assess each system component for feasibility, business impact, and ML compatibility, prioritizing areas that support stable, high-value automation.
• Full-System Data Mapping. We build a complete picture of data propagation across domains, pipelines, and environments, pinpointing structural inconsistencies and semantic ambiguity.
• Transformation Layer. We unify data definitions and encode transformations as versioned logic, enabling deterministic behavior and end-to-end traceability.
• Governance & Runtime Control. We establish classification, policy enforcement, and observability rules to ensure quality, lineage, and regulatory alignment across the data lifecycle.

Deliverable: clean, traceable data flows and a ranked list of processes that are ready for automation and those that need more work first.

Some systems can’t support automation because they’re too tightly coupled, poorly monitored, or risky to change. We modernize the parts that block automation, not the whole system, so models and bots connect to stable interfaces.

The scope includes:

• Architectural Refactoring. We deconstruct monoliths into composable units where inference pipelines, learning loops, and logic abstraction can be independently evolved. Interfaces are rebuilt for runtime flexibility and AI-native orchestration.
• Replatforming the Infrastructure. We migrate to environments optimized for real-time decision-making — containerized, event-driven, and cloud-native. We optimize each workload to support latency-sensitive machine learning tasks and elastic scaling of model execution.
• Building the Monitoring Layer. We redefine telemetry to track system health and AI behavior, expanding visibility into how models operate in real-world conditions. We engineer observability into inference, surfacing drift, confidence anomalies, and version degradation directly from production flow.
• DevOps Pipeline Automation. We extend CI/CD into the model lifecycle, incorporating version control, shadow testing, rollback strategies, and canary inference into your deployment pipeline. This enables safe, auditable releases of intelligence into operations.
• ML-Ready Runtime Design. We prepare your system to run embedded AI, from REST and streaming model execution to feature-store integration and feedback routing. Using our intelligent automation platform, we treat each model as a runtime component, with full traceability and governance.

Deliverable: stable integration points, monitoring and a release pipeline that can safely ship models and automation.

See how work actually flows before you automate it. We use event data from your ERP, CRM, and operational systems to show where work waits, loops back, or gets done twice, and which tasks are worth automating first.

What we deliver:

• Real-Time Process Intelligence. We extract event data from your ERP, CRM, and operational systems to visualize your actual end-to-end flows, including variants, detours, and delays.
• AI-Powered Automation Readiness. Our models identify tasks ripe for automation—repetitive, error-prone, or costly—and score them by ROI, complexity, and risk.
• What-If Simulation & Optimization. Predict the impact of automation before it is rolled out. Run “what-if” scenarios to evaluate efficiency gains, cost reductions, and systemic effects.
• Continuous Monitoring with ML Insights. Deploy intelligent automation tools like anomaly detection, performance baselines, and real-time KPIs to validate outcomes and catch degradation early.
• Compliance & Conformance Checking. We compare real-world execution to expected models and surface deviations, utilizing AI-supported diagnostics to identify and fix what’s broken.

Outcome: an automation plan ranked by impact, effort and risk, based on how your processes really run.

Handle messy formats.

Enterprise workflows often rely on large volumes of unstructured and semi-structured documents: contracts, invoices, requests, and declarations. Manual processing introduces latency, inconsistency, and compliance risk, especially at scale.

We build document automation pipelines that manage structural variability, enforce business rules, and integrate seamlessly with transactional systems.

Scope includes:

• Input Capture & Document Standardization. Ingest multi-format documents from email, portals, scanners, and APIs. Standardize encodings, orientations, and layouts to ensure consistent processing.
• Contextual Parsing & Field Extraction. Use sequence modeling, layout analysis, and position-invariant parsing to extract entities, nested values, and business-critical fields with high accuracy.
• Document Type Disambiguation. Apply classification models to distinguish structurally similar documents based on their semantic features. Route each document through a type-specific extraction and validation pipeline.
• Cross-Validation & Logical Inference. Compare extracted data against internal systems, business logic, and historical patterns. Detect inconsistencies, enforce thresholds, and apply inferred corrections.
• End-to-End Workflow Integration. Send verified data to core business systems, update state machines, trigger events, and generate audit logs. Documents that pass every check flow through without manual steps. The rest go to a reviewer with the issue already highlighted.

Outcome: routine documents are processed automatically, and your team spends its time only on cases that need judgment.

Make execution reliable.

Repetitive, rule-based operations introduce latency and variability when executed manually, particularly in high-volume environments such as finance, human resources, customer service, and supply chain management.

We design and deploy intelligent process automation services that mimic human interaction across interfaces and systems, with defined exception paths and full logging, so a changed screen or a missing field goes to a person instead of stopping the queue. Where a system offers an API, we use it instead of a bot: API integrations break far less often than screen automation.

Engagement scope includes:

• Process Deconstruction & Rule Capture. Analyze target workflows at UI, API, and data levels. Translate business rules, exception handling, and edge conditions into machine-executable logic.
• UI Interaction Automation. Automate interaction with legacy systems, browsers, desktop software, and custom interfaces. Stabilize input fields, button hierarchies, and navigation logic under version drift.
• Exception Management & Recovery Paths. Implement a structured approach for handling process failures, invalid data, missing fields, or downstream unavailability. Route exceptions to a human-in-the-loop where needed.
• Scheduling, Orchestration & Load Control. Integrate with enterprise schedulers and queueing systems. Control concurrency, retry logic, and throughput caps to align with system capacity and business service-level agreements (SLAs).
• End-to-End Monitoring & Audit Logging. Capture event traces, execution metrics, and error logs. Feed into observability platforms for compliance, performance analysis, and SLA validation.

Outcome: bots that handle repetitive work reliably, recover from errors in a defined way and leave a full audit trail.

Predict operational outcomes.

We design intelligent process automation solutions and ML systems to support tactical and strategic decisions in supply chain, finance, demand planning, and risk management. Models are engineered for production: trained on operational data, versioned, monitored, and connected to execution layers.

Scope includes:

• Objective Formulation. Turn business problems into prediction tasks: inventory needs, delivery ETA, credit default risk, fraud probability, revenue trajectory.
• Data Conditioning. Align raw operational data to modeling standards: normalize formats, impute gaps, enforce consistency, and engineer temporal and categorical features.
• Model Development. Use supervised learning, time series forecasting, anomaly detection, or classification, based on signal type and decision latency requirements.
• Deployment Integration. Serve predictions via APIs, message queues, or direct system calls. Embed output into UIs, automated rules, or human-in-the-loop review.
• Model Lifecycle Management. Monitor model performance, retrain on drift or new data distributions, and audit prediction behavior. Integrate metrics into the observability stack.

Output: forecasts and risk scores that appear inside the tools your team already uses, next to the decision they inform.

Modern assistants don’t stop at answering questions. Built on large language models, AI agents read a request, look up data in your systems, and complete the task: update an order, prepare a quote, open a ticket, or route a case to the right person.

We define what each agent can do, which systems it can access, and when it must hand over to a human. We log every action, and sensitive steps require approval.

• Understanding Requests. Agents work with free-form messages in multiple languages and ask clarifying questions when a request is ambiguous.
• Context Across Conversations. Agents remember the customer, the case, and earlier steps, so people don’t have to repeat themselves.
• Transactional Layer Integration. Assistant logic wired into APIs, CRMs, ERPs, and support platforms. Supports actions like booking, submitting, updating, escalating — all executed within secure environments.
• Monitoring, Traceability, and Governance. Comprehensive telemetry across usage, resolution paths, and exception flows. Each decision path is auditable. Includes human-in-the-loop controls, escalation handling, and compliance filters.
• Deployment Across Interaction Surfaces. One logic core deployed across chat, voice, embedded widgets, and messaging apps. Built-in uniform behavior, state synchronization, and identity continuity.

Outcome: routine requests are resolved end to end, and your team handles the cases that need a human. For complex multi-step processes, see our Agentic AI services.

Turn sensor data into action.

In logistics and manufacturing, sensor data is ubiquitous but often underused. We integrate edge telemetry with scalable inference pipelines to detect degradation patterns, model failure risks, and enable proactive intervention.

Key capabilities include:

• Signal Acquisition & Synchronization. Acquire and synchronize telemetry data from distributed assets, including machines, fleet vehicles, environmental sensors, and control systems. Normalize sample rates and synchronize time series from multiple sources for unified analysis and interpretation.
• Anomaly Profiling & Drift Detection. Train unsupervised and semi-supervised models to distinguish between acceptable fluctuations and signal degradation. Quantify temporal drift, persistence of outliers, and entropy changes in different signals.
• Health Index Modeling.  Composite health indicators per asset derived from multidimensional signal data. Track shifts against operational baselines and degradation thresholds to predict failure windows.
• Event Prediction & Maintenance Optimization. Model the relationship between observed signal patterns and historical failure events. Optimize maintenance intervals, resource allocation, and alerting logic based on probabilistic risk assessment.
• Deployment & Integration. Package models for edge or cloud deployment, with continuous monitoring and feedback loops. Integrate predictions into existing maintenance systems, dashboards, and alerting platforms to enhance operational efficiency.

Deliverable: early warnings about equipment at risk, delivered into the maintenance and alerting tools your team already uses.