“We read every document and re-enter the same details.”
Extract and organize information from invoices, forms or other documents, with validation and review for uncertain results.
Home / AI solutions & automation
Less repetitive work. More useful information.
Less time searching, copying and chasing. More time getting work done.
When your team spends hours reading documents, finding answers or moving information between systems, start with the task that needs to improve. Evaluate AI and automation around your workflow, your data and a measurable outcome. Test a focused use case before committing to a wider rollout.
Recognize the work, then choose the approach
Where is your team spending time it could use better?
Choose a specific bottleneck. Then assess what can be automated, what needs judgment and what success would mean.
“The answer exists. Finding it takes too long.”
Help employees retrieve relevant information from approved sources, with references and access boundaries appropriate to the task.
“Our team answers the same questions all day.”
Give staff or customers a clearer route to routine answers, with escalation when a question needs someone’s attention.
“Someone still has to move every task to the next system.”
Connect agreed steps across your applications. Set permissions, checkpoints and exception handling before automating actions.
“We want AI in our application, but it must work with our users and data.”
Scope the experience, integrations, response quality and running costs around a specific user task.
“The demo looks impressive. The business value is unclear.”
Test one workflow against real examples and an agreed baseline before deciding whether wider rollout is justified.
Confidence through evaluation
Know where AI helps. Know where control matters.
A useful solution must work with real inputs, everyday exceptions and the people responsible for the result.
Choose the simplest useful approach.
Some tasks need a better integration or a clear rule. Others benefit from AI. Evaluate the workflow before choosing the technology.
Know what the system can access.
Define data sources, user permissions and permitted actions. Assess model-provider and hosting arrangements against your requirements.
Decide where people stay involved.
Agree which outputs need review, which actions need approval and what happens when information is missing or uncertain.
Make the investment measurable.
Set a baseline and evaluation criteria. Include errors, review time and recurring costs when judging whether the workflow is better.
AI solutions & automation services
Put the capability where the work happens.
Start with the outcome you need. Define the implementation and safeguards around the task.
Make useful answers easier to reach.
Help customers or employees find information and complete defined tasks using approved knowledge sources, references and a clear handoff to people.
- Search across approved policies, manuals and internal content
- Ground answers in source material and show references
- Respect user access and escalate unanswered questions
Spend less time moving information from documents into systems.
Extract, classify and validate agreed fields from business documents. Route ambiguous or incomplete results for review before they enter downstream workflows.
- Extract and classify information from forms and invoices
- Validate fields and flag exceptions for review
- Send approved information into existing systems
Move routine work forward with the right checks.
Connect multi-step tasks across systems, using rules or AI where appropriate. Agree permitted actions, approval points, logs and recovery steps.
- Coordinate defined tasks across tools and workflows
- Route approvals, handoffs and exceptions
- Keep review, escalation and audit points clear
Add AI where your users already work.
Bring AI-assisted search, drafting, extraction or other defined capabilities into an existing application, with suitable access controls and review.
- Integrate with APIs, CRM, ERP and business applications
- Connect approved knowledge and operational data
- Plan for access, monitoring and maintainability
Find out whether the use case is worth scaling.
Test a bounded workflow with representative examples. Compare output quality, task time, review effort and operating cost against your current process.
- Assess use case, data readiness and practical constraints
- Define success measures and a contained pilot
- Review results and recommend a sensible next step
Need an application around the workflow?
Bring the AI capability into a web application, mobile app or portal your team and customers can use.
Illustrative workflows
Make the use case concrete. Make the result testable.
Examples to shape a discussion, not reported customer outcomes. Feasibility depends on your documents, systems and requirements.
| Work today | Possible workflow | What to evaluate |
|---|---|---|
| Staff read invoices and copy fields into a system. | Extract agreed fields, validate them and send exceptions for review before posting. | Field accuracy, review time, duplicate handling and total processing cost. |
| Employees search folders and ask colleagues for policy answers. | Retrieve relevant passages from approved material, show references and hand off unanswered questions. | Answer correctness, source relevance, permissions and time to a useful answer. |
| Requests arrive in email and someone routes each one manually. | Classify requests, prepare structured records and route them, with approval for agreed exceptions. | Routing accuracy, exceptions, handling time and whether work reaches the right owner. |
Before you scale
Prove the workflow is useful before expanding it.
Agree a pilot scope, real test examples and the conditions for the next investment.
From business task to daily use
A practical route from “could AI help?” to evidence.
Each stage gives you something concrete to review before proceeding.
Choose one task worth improving.
Map who does the work, how often, where time is spent and what a useful result looks like. Your review: workflow, baseline and business priority.
Check data and dependencies.
Review representative samples, access, integrations, exceptions and security requirements. Your review: feasibility, scope and unresolved constraints.
Test with real examples.
Build a bounded pilot and compare outputs with agreed evaluation criteria, including difficult cases. Your review: quality, effort, cost and a go/no-go decision.
Connect the useful parts to daily work.
Implement the agreed workflow, approval steps, monitoring and fallback behavior. Your review: acceptance tests and operational readiness.
Review performance after launch.
Monitor quality, exceptions, usage and cost under the agreed support arrangement. Your review: actual results and the next improvement priority.
The scope and depth of discovery depend on the use case. A successful pilot informs a rollout decision; it does not by itself establish production readiness.
Before you commit
Resolve the questions behind the investment.
Data, reliability, permissions and operating cost belong in the discussion from the start.
Where should we start if we don’t have an AI strategy?
Start with one repetitive or information-heavy task. Map the current workflow and the outcome you want, then assess whether AI, automation or a simpler change is the right fit.
How will we know whether AI is useful for this task?
Agree a baseline and measures before a pilot. Depending on the workflow, these may include quality, handling time, review effort, exceptions and operating cost.
Will the AI always produce correct answers?
No AI system should be assumed to be error-free. We define evaluation criteria, use approved sources where applicable and add review, escalation and fallback behavior appropriate to the risk.
What happens to our business data?
Data sources, access, hosting and model-provider arrangements are assessed during discovery against your requirements. We agree the intended data flow before implementation.
Can AI work with our existing applications?
Often, yes. Feasibility depends on available APIs, permissions, data quality and system constraints. These dependencies are checked early.
Do we need a large volume of data to begin?
Not necessarily. A focused assessment can often begin with representative, appropriately approved examples. Requirements depend on the use case and evaluation approach.
Can the system act without someone approving each step?
Automation boundaries are set around the task. Actions can require approval, include exception handling or remain fully human-reviewed where the workflow calls for it.
How much will it cost and how long will it take?
Scope, integrations, data readiness and assurance needs affect both. A bounded assessment or pilot can clarify effort and costs before a broader commitment.
Your next step
Bring one workflow. Get a clearer way forward.
Tell us what your team does today, where the effort goes and what you would like to improve. You don’t need to know which AI technology to choose.
What you can get from the first conversation
A clearer use case
Identify the task, the people involved and the outcome worth evaluating.
The questions that matter
Discuss source data, integrations, review needs and practical constraints.
A sensible next step
Establish whether to assess feasibility, scope a pilot or consider a simpler automation.
Start with a description. If samples are needed, agree a suitable way to share them.
Prefer email? sales@xpetize.com
Which task would you like to improve?
Describe the task and where the effort goes. Please don’t include confidential records or sensitive personal data.