Business Process Automation Services
Automate what rules
alone never could.
RPA handles the repetitive. AI handles the complex documents with variation, decisions with nuance, workflows that adapt when things change. We build both, and the layer that connects them.
Our Decade long experience, validated in numbers
Enterprise Customers
automation spectrum
From rule-based to fully autonomous.
AI-ENHANCED
✦ COGNITIVE
✦ AGENTIC
Trusted by Leading Enterprises
What gets automated
Processes across every function.
The highest-ROI automation candidates are usually hiding in the functions that have the most human touchpoints. Here’s where we typically find them.
Finance & Accounting
✦ AI document processing- Invoice capture, validation & 3-way match
- AI-powered accounts payable & receivable
- Month-end close reconciliation
- Expense report processing & policy checks
- Contract data extraction & obligation tracking
HR & Onboarding
✦ Agentic orchestration- Candidate screening & interview scheduling
- AI-driven onboarding workflow orchestration
- Payroll data processing & exception handling
- Benefits enrolment and leave management
- Policy document Q&A via LLM
Supply Chain & Procurement
✦ Intelligent workflows- Purchase order creation & approval routing
- Supplier data extraction from unstructured documents
- Inventory reconciliation & reorder triggers
- Demand forecasting and exception alerts
- Logistics tracking and status updates
Customer Operations
✦ Cognitive + agentic- AI-powered inquiry triage and routing
- Automated order status & fulfilment updates
- Complaint classification and response generation
- Returns processing and refund workflows
- Contract renewal and churn prediction workflows
AI capabilities
Where AI makes the difference in automation.
Platforms & tooling
Built on what your team already trusts.
✦ AI & Agents
✦ Document AI
✦ Process Mining
✦ RPA Platforms
✦ Workflow & Integration
✦ Cloud & ML
Everything you need to know
AI-powered application management uses machine learning models trained on a system's own telemetry (logs, metrics, and traces) to learn what normal behaviour looks like for each service, then detect anomalies, correlate alerts, suggest root causes, and trigger self-healing actions automatically. Instead of engineers discovering problems after a threshold is breached, deviations are typically surfaced 15 to 30 minutes before an actual incident occurs.
AI anomaly detection works by learning a baseline of normal behavior for every service from historical telemetry, then flagging deviations from that baseline as they start to emerge, rather than waiting for a fixed alert threshold to be crossed. This early detection window, typically 15 to 30 minutes, gives engineers time to investigate or lets a self-healing runbook trigger automatically before the deviation turns into a customer-facing incident.
A self-healing runbook is an automated response to a known, previously seen failure pattern that resolves the issue without requiring a human to manually intervene, such as automatically restarting a failed service or scaling resources in response to a detected load pattern. AI identifies which known pattern is occurring and triggers the matching runbook automatically, which is what allows incidents that match a known pattern to resolve faster than the typical SLA for manual response.
AI-scored patching evaluates CVEs (Common Vulnerabilities and Exposures) based on exploitability and blast radius rather than just severity score, so the highest actual risk gets prioritized instead of every vulnerability being treated equally. Automated remediation, including dependency update pull requests that are tested and deployed, is often raised directly from this scoring so the highest-priority patches move through review faster.
LLM-assisted modernization uses large language models to help understand legacy code, suggest refactoring approaches, and generate test coverage for modules that previously had none, before a migration or architecture change happens. This matters because modernizing legacy code without adequate test coverage is a common source of regressions, and generating that coverage manually for old, undocumented code is often the slowest part of a modernization project.
Essentials-level support typically covers AI-assisted monitoring, 24/7 coverage, and L1/L2 incident response without a dedicated full-time team, while fully managed support adds predictive anomaly detection, self-healing automation, AI-scored CVE patching, LLM-based ticket triage, and L3 engineering support. A further tier can also include an active AI-guided modernization program, covering code risk analysis, cloud migration, and architecture advisory on top of day-to-day operations.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Connect With us
Show us a process. We'll tell you what's automatable.
Describe a high-volume process the steps, the inputs, the exceptions and we’ll map what rule-based automation handles, where AI is needed, and what an agent could own end to end.