Transformation
Intelligent Automation
AI-powered process automation across the back office — combining process mining, workflow orchestration, document intelligence, and agents into automation that survives real-world exceptions.
Overview
Why it matters
Classic automation hit a ceiling. Rules engines and scripts handled the predictable 60% of a process, and the remaining 40% — unstructured documents, exceptions, judgment calls — stayed manual, along with most of the cost. Intelligent automation breaks that ceiling by adding AI to the toolkit: document understanding that reads what OCR could only digitize, classification and extraction that handle variation, and agentic reasoning for the cases where 'if X then Y' was never going to be written down.
Our practice designs automation as a portfolio, not a pile of bots. We start with process mining and discovery to find where the hours and errors actually are (which is frequently not where opinion says they are), then match each opportunity to the right technique: workflow orchestration for the deterministic backbone, RPA for legacy-system reach, document AI for the unstructured inputs, LLM-based agents for exception handling — and human-in-the-loop checkpoints wherever consequences demand judgment.
The economics compound when automation is engineered like software: centralized orchestration and monitoring, exception analytics that feed continuous improvement, and a governance model (a pipeline, standards, and an accountable owner per process) that keeps a hundred automations from becoming a hundred liabilities. That operating layer is where most automation programs fail — and where ours start.
Business challenges
The problems this practice exists to solve
The manual 40% that automation never reached
Rules-based automation handled the clean cases; everything unstructured or exceptional still lands on human queues — and those queues hold most of the residual cost and all of the backlog risk.
Back-office cost that scales with volume
Every growth quarter adds headcount in AP, claims, onboarding, and order management. Unit economics that should improve with scale quietly get worse.
Automation sprawl without governance
Dozens of bots and scripts built by different teams, undocumented, unmonitored, breaking silently on every upstream change — savings on paper, incidents in practice.
SLA and accuracy pressure on manual work
Regulatory clocks on onboarding and claims, error rates that trigger rework and penalties, and audit findings on processes that depend on tribal knowledge and inbox discipline.
Our solution
How we engineer it
We begin with evidence: process mining on your event logs and structured discovery with operators establish where time, cost, and errors concentrate, and which processes are automatable at what confidence. Each candidate gets a business case (hours, error cost, SLA exposure) and a technique map — because automating a broken process just makes the chaos faster, redesign comes before robotization where the process itself is the problem.
Architecture then assembles the right tools per layer. A workflow orchestration backbone (Temporal, Camunda, or your existing BPM) owns state, retries, and SLA tracking; document AI converts unstructured inputs into validated, confidence-scored data; RPA bridges the legacy systems without APIs; and LLM-powered agents take the exception queues — investigating mismatches, drafting resolutions, escalating genuinely novel cases with a complete file. Confidence thresholds route work between straight-through processing and human review, and every human correction becomes training signal.
Then we industrialize the program: a central automation platform with monitoring, alerting, credential management, and versioned deployment; exception analytics that show exactly where the residual manual work lives (so the next quarter's roadmap writes itself); and value tracking that reports hours returned, error rates, and SLA performance to your CFO in their own numbers. Automation becomes an operated capability with compounding returns, not a one-time project with decaying ones.
Capabilities
What intelligent automation covers
Process mining & opportunity discovery
Event-log analysis with Celonis-class tooling plus structured operator discovery — an evidence-based automation pipeline ranked by value, feasibility, and risk.
Workflow orchestration
Durable process backbones on Temporal or Camunda: state, retries, SLA timers, and full auditability for processes spanning systems, days, and departments.
Intelligent document processing
Classification, extraction, and validation across invoices, claims, contracts, and KYC packets — confidence-routed between straight-through processing and human review.
AI-powered exception handling
LLM agents that investigate and resolve the mismatches, edge cases, and judgment calls that rules never covered — under permission tiers and human oversight.
Human-in-the-loop design
Review queues, approval gates, and escalation paths engineered for speed and auditability — keeping people on judgment, not on data entry.
Automation platform & governance
Central monitoring, credential vaulting, versioned deployment, exception analytics, and value tracking — the operating model that keeps 100 automations healthy.
Technology stack
Tools we deploy to production every week
Pragmatic about tools, opinionated about architecture — the platforms below are the ones this practice ships with, chosen per engagement on evidence.
Orchestration & BPM
- Temporal
- Camunda
- Power Automate
- Airflow (data workflows)
RPA & Integration
- UiPath
- Automation Anywhere
- Power Automate Desktop
- REST / API integration
Document & AI
- Azure Document Intelligence
- AWS Textract
- GPT / Claude APIs
- LangGraph
- Custom extraction models
Mining & Analytics
- Celonis
- UiPath Process Mining
- Power BI
- Grafana
Implementation process
Five stages. No surprises.
A delivery model refined over 250+ engagements — sequenced so leadership gets visibility and your teams get momentum.
Discover & quantify
Process mining and operator discovery build the opportunity pipeline, each candidate scored on hours, error cost, SLA exposure, and automation confidence.
Design & redesign
Target-state process design — eliminating steps before automating them — with a technique map (orchestration, IDP, RPA, agents) and human checkpoints per decision class.
Build the first flows
The highest-value process built end to end on the platform: orchestration, document AI, integrations, review queues — live in production within the first quarter.
Prove & harden
Parallel-run validation against manual baselines, accuracy and SLA measurement, exception-path tuning, and sign-off with process owners and audit.
Scale the portfolio
A repeatable factory: pipeline governance, reusable components, exception analytics feeding the roadmap, and quarterly value reporting to the CFO.
Use cases
Where enterprises apply it
Accounts payable automation
Invoice capture, PO matching, exception resolution, and posting to the ERP — days of keying compressed to hours of review.
Claims intake & adjudication support
Document classification, data extraction, coverage checks, and triage routing — with adjusters focused on the judgment cases only.
Customer & vendor onboarding
KYC/KYB document processing, sanctions screening, system provisioning, and status orchestration against regulatory clocks.
Order management & billing exceptions
Automated investigation of failed orders and billing mismatches across ERP, CRM, and payment systems — resolved or escalated with a complete case file.
HR & employee lifecycle processes
Onboarding, transfers, and offboarding orchestrated across HRIS, identity, and asset systems — no more day-one-without-a-laptop.
Regulatory reporting preparation
Data gathering, validation, and report assembly for recurring filings — with lineage and checkpoints your auditors can inspect.
Outcomes
Results clients report to their boards
72%
straight-through processing rate on a claims intake process, up from 31%
45K
hours returned to the business annually across one automation portfolio
83%
reduction in processing errors on automated flows versus manual baseline
11 mo
median payback period across intelligent automation programs we operate
FAQs
Questions leaders ask us
Direct answers on intelligent automation — the same ones we give in the first consultation.
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Ready to put intelligent automation to work?
In a 45-minute consultation, our architects map your highest-ROI opportunity, outline a delivery plan, and give you a realistic budget range — no obligation.