Altwick develops practical AI features and automation around business workflows. We focus on the point where information enters a process, how it becomes usable and what a person needs to review before relying on the result. AI is one part of the software, not a substitute for the surrounding workflow.
Documents contain information that people repeatedly read and enter into another system.
A repetitive workflow has clear rules, but information still moves manually between tools.
An application needs an AI-assisted feature with explicit review and failure behavior, rather than an isolated demonstration.
01
Document & data extraction
Identify the fields a workflow needs, preserve their source context and bring the result into an application. Invoice extraction is one demonstrated example; other document types need their own samples, field definitions and evaluation.
02
Workflow automation
Use deterministic logic for defined rules and reserve AI for the parts that need interpretation. Specify the trigger, allowed actions, required validation and the point at which an uncertain result returns to a person.
03
Application integration
Put the output where it can be reviewed and used, with appropriate authentication and stored records. Connections to other services depend on their APIs, access permissions and the information the workflow is allowed to share.
Define what a useful result means
Does the task need AI?
Structured inputs and predictable rules may be better served by ordinary parsing or automation. AI-assisted interpretation becomes relevant when the input varies and a rules-only approach is insufficient. The decision should follow representative examples.
How will uncertain output be handled?
A confidence indicator is context, not proof of correctness. Plan warnings, review and access to the original material. Decisions with meaningful consequences need an explicit level of human oversight agreed for that workflow.
What are the data and running-cost constraints?
Agree which information may leave the application, which providers may process it and what usage budget is acceptable. Third-party AI processing can have operating costs; it should be selected and authorized as part of the project, not silently enabled.
AI-assisted invoice extraction with structured fields, confidence, warnings and retained raw text, inside an authenticated web application. The case study and real screenshots show how review context accompanies the structured result.
Before we start.
Can AI extraction be fully trusted without review?
Do not assume that every extracted field is correct. Evaluation should use representative documents and the consequences of an error. Invoicely shows confidence, warnings and raw text as context; none of these is an accuracy guarantee.
Can automation work with our existing software?
Potentially, through its supported APIs or other agreed access methods. The feasibility depends on permissions, available interfaces and how the existing workflow handles data. Share those constraints before treating the connection as a solved part of the project.
How do we scope a first AI feature?
Choose one recurring input, the output needed and the next action it supports. Provide representative examples, including difficult cases. Define an acceptable result, the review process and the constraints on data and usage before expanding the scope.
Start a conversation
Bring us the problem.
Describe the repetitive task, its inputs and the next action that depends on the result. Share the kinds of examples involved, where a mistake matters and any restrictions on data processing or provider usage.
