Process discovery and mapping
We interview operators, review system logs, and use process mining where available to find the steps worth automating and the ones better left to people.
Robotics & Emerging AI
Intelligent automation for work rules can't cover.
Intelligent automation takes over the judgment-heavy tasks that traditional scripts leave behind: reading messy documents, routing exceptions, and deciding what happens next. We design workflows where bots, models, and people each handle the part they do best.
- RPA plus LLMs
- Human-in-the-loop
- Audit-ready logs
01 Overview
Automate the whole process, not just the clicks
Rule-based bots break the moment an invoice arrives in a new layout or a customer email does not match a template. Intelligent automation adds a reasoning layer on top of those bots: intelligent document processing to pull structured data from PDFs and scans, language models to classify requests, and decision logic that knows when to escalate. The result is business process automation that tolerates real-world variety instead of collapsing on the first exception.
We start by mapping the process as it actually runs, including the workarounds nobody wrote down. Then we pick the right tool for each step, whether that is UiPath or Power Automate for legacy screens, n8n or Temporal for durable orchestration, or an LLM agent for unstructured judgment calls. Every flow ships with confidence thresholds, review queues, and logs, so your team stays in control and can trace any decision back to its inputs.
02 Capabilities
Intelligent Automation capabilities.
End-to-end Intelligent Automation: strategy, engineering and the production operations that keep it working.
Intelligent document processing
Extraction models read invoices, claims, contracts, and forms, validate fields against business rules, and pass clean structured data into your ERP or CRM.
RPA with AI decisioning
Software robots handle legacy interfaces while classifiers and LLMs decide routing, priority, and next steps, turning brittle scripts into adaptable RPA with AI built in.
Durable workflow orchestration
Long-running processes built on Temporal, n8n, or Camunda that survive restarts, retry failed steps safely, and keep state across days-long approvals.
Human-in-the-loop review
Review queues surface only the uncertain cases, show the model's reasoning and source evidence, and feed reviewer corrections back into future decisions.
Systems integration and APIs
Connectors for SAP, Salesforce, ServiceNow, email, and internal databases so automated steps read and write the systems your teams already depend on.
03 How we deliver
From first call to production.
Every Intelligent Automation engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Accounts payable invoice handling
Invoices arrive by email in any format, get extracted and matched to purchase orders, and only mismatches reach an AP clerk for review.
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UC-02
Insurance claims intake and triage
Claim documents and photos are classified, key details extracted, and files routed to the right adjuster with a summary of what is missing.
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UC-03
Employee onboarding across HR systems
A single trigger provisions accounts, schedules training, generates paperwork, and chases signatures, keeping HR updated on every step that stalls.
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UC-04
Customer support ticket routing
Incoming tickets are read, tagged by intent and urgency, enriched with account history, and either resolved automatically or sent to the right queue.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Automation platforms
- UiPath
- Microsoft Power Automate
- Automation Anywhere
- n8n
Orchestration
- Temporal
- Camunda
- Apache Airflow
- AWS Step Functions
AI and document models
- Azure AI Document Intelligence
- Amazon Textract
- OpenAI API
- LangGraph
06 Industries
Built for your sector.
We deliver Intelligent Automation for regulated and fast-moving industries alike.
What is the difference between RPA and intelligent automation?
RPA mimics clicks and keystrokes following fixed rules, which works well for stable, structured tasks. Intelligent automation layers AI on top so the workflow can read unstructured documents, interpret text, and make judgment calls. In practice we often combine both: RPA handles the legacy screens while models handle the decisions those screens require.
Which processes are good candidates for intelligent process automation?
Look for high-volume work that follows a recognizable pattern but involves documents, emails, or exceptions that defeat simple rules. Invoice processing, claims intake, order management, and ticket triage are common starting points. We score candidates on volume, variability, data quality, and risk so the first project proves value without betting on the hardest case.
How do you keep humans in control of automated decisions?
Every model output carries a confidence score, and anything below a threshold you set goes to a review queue instead of executing automatically. Reviewers see the source document and the model's reasoning side by side. All actions are logged with inputs and outcomes, giving auditors a clear trail and giving your team a way to tune thresholds over time.
Do we need to replace our existing RPA platform?
No. If you already run UiPath, Power Automate, or Automation Anywhere, we extend those bots with AI services rather than starting over. We add document understanding, classification, or LLM steps through APIs and keep your existing orchestrator. Replacement only makes sense when licensing or maintenance costs clearly outweigh the effort of migrating.
How is sensitive data handled in workflow automation services?
We design data flows around your regulatory requirements, such as HIPAA, GDPR, or SOC 2 controls. That can mean redacting personal data before it reaches a model, running models inside your cloud tenant, restricting bot credentials to least privilege, and retaining logs according to your policies. Security reviews happen before any workflow touches production data.