Document-heavy workflows
Invoices, claims, onboarding packs, and correspondence still arrive as documents. We combine OCR, extraction, and validation so structured data reaches your ERP or case system - with a queue when confidence drops.
Process Automation
IPA and RPA for document-heavy workflows, with human-in-the-loop controls.
Agam Intelligence automates the work that still lives in inboxes, PDFs, and swivel-chair systems. We combine RPA, intelligent document processing, and ML where it earns its place - with exception queues so people handle the cases a bot should not.
Intelligence
What we do
Automation that respects the exception.
Bots that assume every invoice looks the same fail on Tuesday morning. We map the process, the systems of record, and the exception rate first - then automate the stable path with IPA and RPA, and keep humans on low-confidence, high-value, or policy-sensitive cases. Automation is only useful if operations still owns the outcome.
Enabling outcome-based intelligent process automation.
RPA where systems have no API
Some work still lives in a UI. We use attended and unattended RPA for the stable clicks, with credentials, logging, and change detection so a screen change does not silently break the process overnight.
Intelligent process automation
Where classification or extraction can take a first pass, we add ML to the bot path. IPA is a mix: rules, models, and people - chosen for the case type, not a single-vendor slogan.
Human-in-the-loop controls
Approvals, overrides, and four-eyes checks stay in the design. We build exception workbenches so staff are faster, not unemployed by a brittle bot - and so auditors can see who did what.
Process discovery and measurement
We baseline cycle time, error rate, and volume before a bot is built. After go-live we report the same numbers so automation is judged on operations, not a slide of hours theoretically saved.
Resilient operations
Selectors break. Vendors change screens. We design monitoring, alerting, and a support model so automation is owned after launch - including a manual fallback when the bot must stop.
Case studies
See the work
Clinical analytics leadership could finally trust
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
An enterprise copilot that survived legal and security review
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
Fraud signals surfaced before losses hit the ledger
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Why work with us?
Predictable business outcomes
Automation is measured on cycle time, straight-through rate, and error rate on live volume - not a happy-path recording.
Lower operating risk
Exception queues, audit logs, and manual fallback keep a brittle UI change from stopping the business.
Modernise with confidence
Automate the stable path first. Expand case types when exception rates and ownership are proven, instead of a big-bang bot farm.
Trends and insights
Process automation news and perspectives
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Future-ready data and AI platforms are designed for change from the start - pipelines that can evolve, models that can retrain, and architecture that does not force a rewrite every time the business asks a new question.
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Signs Your Engineering Team Needs a Technical Audit
When dashboards disagree, models drift, and nobody owns the pipeline, the problem is often systemic. This article covers the warning signs and what an independent audit should examine across data, process, and platform.
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