AI Process Automation
AI process automation that gives your team time back.
Less copying between systems. Fewer requests stuck in an inbox. We build AI automations that move everyday work forward—from the first document to the final approval—with your team in control.
Your workflows. Connected systems. Human oversight.
Start with one bottleneck. Build from there.
Choose a workflow your team knows well. Connect the tools, test the difficult cases, and measure the difference before expanding.
Map
Process baseline
Connect
System access
Review
Human oversight
Measure
Pilot evaluation
Where does the work get stuck?
An invoice waiting to be entered. A ticket passed between teams. A report rebuilt every Friday. Start with the repeat work that keeps interrupting your day.
From invoice inbox to review-ready record
Extract supplier details and line items, check required fields, and compare against purchase records. Give finance the source document and any discrepancies together, ready for review.
Give every support request a next step
Sort incoming tickets, retrieve relevant account context, and prepare replies from approved knowledge. Route sensitive or unresolved requests to a person with the conversation already summarized.
Keep approvals moving
Turn emails and forms into structured requests. Check for missing information, route to the right owner, and send reminders so requests stay visible until a decision is recorded.
Stop rebuilding the same report
Bring together data from agreed sources, check totals with deterministic rules, and draft a narrative linked to the underlying records. Your team reviews exceptions and commentary before sharing.
Make customer onboarding easier to follow
Collect intake information, identify missing documents, and prepare account records and handoff summaries. Keep the next action visible for sales, operations, and customer success.
Connect the handoffs between tools
Carry approved information from a helpdesk into a CRM, task queue, or internal system. Define which actions run automatically and which need review before records change.
One invoice. A clear path from inbox to approval.
Today, someone might download a PDF, retype its details, hunt for a purchase order, and chase an approver. Here is how an illustrative automation could carry that work forward.
Receive
An invoice arrives in a shared inbox. Its attachment enters the processing queue.
Connected inbox
Read & check
Extract the fields, check totals and duplicates, and compare with the purchase record.
AI extraction + rules
Resolve exceptions
A missing order number or mismatch goes to finance, with the original document attached.
Human review
Record & track
After the required approval, update the accounting system and record the outcome.
Approved system action
Measure: handling time, rework, and review volume.
Keep: finance ownership of payment approval.
The best first project is a problem you can name.
Bring us the process people keep working around. We help define a useful first release and what would make it worth continuing.
A recurring task with a clear owner
Start where someone can explain the steps, show representative examples, and tell us what a correct result looks like. Include the messy cases as well as the easy ones.
A bottleneck you can measure
Record monthly volume, handling time, rework, and time spent waiting. Compare those with pilot results, including review effort and the cost of running the automation.
Systems we can actually connect
Check available APIs, permissions, data quality, and vendor limits early. Where a system has no suitable API, assess a connector or desktop automation and its maintenance needs.
A sensible boundary for the pilot
Choose one document type, request category, or team. If the process changes constantly or examples are too scarce, clarify the workflow and collect evidence before automating it.
Automation your team can understand and oversee.
A useful workflow makes its status, source information, and next action visible. These controls are part of the implementation scope we agree with you.
AI for interpretation. Rules for checks.
Use AI to extract meaning from documents and messages. Use explicit rules to validate totals, required fields, and permitted actions. Add an agent only where the task needs flexible steps.
Review at the moments that matter
Pause for the designated owner before agreed actions such as payments or sensitive record changes. Missing information, failed checks, and uncertain results enter a review queue.
Access scoped to the workflow
Agree which records the automation can read or change, what data reaches a model provider, and what is retained. Keep credentials separate from prompts and limit tool permissions.
A recovery path when something fails
Design retries and duplicate checks for interrupted runs. Log actions, notify an owner when work stalls, and provide a way to pause automation and finish the task manually.
A working pilot. A measured decision. Then the next step.
Each stage answers a practical question, with deliverables your team can review before the scope grows.
01 / Discover the workflow
We map inputs, systems, owners, and exceptions. You get a scoped pilot proposal, an integration plan, baseline measures, and agreed acceptance criteria.
02 / Test with representative work
We build the core flow and evaluate it against examples with known outcomes. Your team reviews incorrect results and edge cases before we connect consequential actions.
03 / Launch with clear ownership
We introduce the workflow to a defined group, with review queues, monitoring, recovery steps, and operating documentation. Launch scope follows the acceptance criteria.
04 / Measure and refine
Compare handling time, completion quality, review volume, and running costs with the baseline. Use reviewed examples to test changes before expanding to another workflow.
Project experience
Ajentic: the work behind the service
Internal product · built by Imversion
Ajentic is our internal AI agent platform. It demonstrates the application and integration work surrounding an agent, rather than an independent client endorsement.
What we worked on
- LLM orchestration and graph-based agent workflows
- Admin dashboards and an embeddable chatbot
- Integrations including Slack, Zendesk, and WhatsApp
The published project account covers the platform backend, interfaces, and connected workflows. For customer projects, we agree evaluation criteria, data permissions, and escalation paths for the intended use case.
Read the Ajentic case study →Frequently Asked Questions
What makes a process a good fit for AI automation?
Look for repeated work with accessible inputs, a clear owner, and a result you can check. Documents, messages, and requests that need interpretation can be good candidates. A process with very low volume, unclear rules, or unreliable data may need improvement before automation is worthwhile.
How is this different from a chatbot or traditional automation?
A chatbot is an interface for a conversation. A workflow carries a task through systems and handoffs. Traditional automation follows defined rules; AI can help interpret variable inputs such as emails or documents within that workflow. We choose the simplest combination that meets the task, rather than making every step an autonomous agent.
Do we need to replace our existing tools?
Usually the starting point is your existing CRM, helpdesk, finance tools, and databases. Integration depends on their APIs, connectors, permissions, and licensing. We check those constraints during discovery and explain any required changes before implementation.
What happens when the AI gets something wrong?
The workflow should validate outputs against agreed rules and route failed checks or uncertain cases to an owner. We test representative examples, define approval points, and plan recovery for interrupted tasks. Review feedback informs tested updates; it does not automatically retrain the system.
How do we work out whether the investment pays off?
Compare the current cost and quality of completing the task with the pilot. Include task volume, handling time, rework, human review, software and model usage, support, and implementation costs. Time released is capacity your team can use; it is not automatically a reduction in payroll costs.
How long does a pilot take, and what does it cost?
Both depend on the workflow, available examples, integrations, and approval requirements. We scope a contained first release and identify dependencies before giving an estimate. A document extraction flow with a review queue has different requirements from an automation that updates several production systems.
What happens to our business data?
Data access, model providers, retention, and deployment requirements are agreed during discovery. We define what the workflow needs to read and write and check provider settings against your requirements. Do not assume every provider or deployment has the same data handling terms.
What should we bring to the first conversation?
Bring a description of one repetitive process, the tools involved, an approximate monthly volume, and the main frustrations. Sanitized examples and an estimate of handling time help. You do not need a finished technical specification to start.
What would you take off your team’s plate first?
Tell us about one repetitive workflow and the tools involved. We’ll help you explore what to automate, where review belongs, and what a useful pilot could look like.