Workflow
Automate a manual workflow
Map the steps, remove the hand-copying, and keep approval points where a person still needs to decide.
- Lead intake
- Document handling
- Internal handoffs
- Operations queues
Your startupshouldn’t run oncopy + paste.
BARDICINSPIRATION.XYZ / THE PROBLEM
We connect the tools, data, and AI your team needs so routine work moves without being copied from one system to another.
What we do
A request arrives. Someone reads it, copies the details, creates a task, updates a record, and sends a reply. We turn that chain into one traceable workflow, with a person still approving what matters.
Ways we can help
Three kinds of project.
Systems
Connect systems that don’t talk
Move information between the software you already use, with explicit rules for failures, duplicates, and edge cases.
- SaaS APIs
- Custom connectors
- Data sync
- Event pipelines
Product
Add one useful AI capability
Put models to work on a defined job—extracting, classifying, searching, or drafting—with a way to review uncertain results.
- Knowledge search
- Extraction
- Classification
- Draft assistance
When to call
Manual work has become infrastructure.
More customers now means more admin.
Volume is growing, but the process only scales by adding people to repeat the same steps.
Your spreadsheet has become infrastructure.
Important work depends on formulas, manual updates, and knowledge that lives with one person.
A standard automation gets you 80% there.
The remaining 20% needs custom logic, another data source, or a connector that does not exist.
You have a real AI use case—not a feature checklist.
There is a specific task, a clear user, and a way to judge whether the output is good enough.
How projects move
Scope it. Prove it. Put it to work.
Trace the real process
We look at the actual inputs, decisions, tools, handoffs, and exceptions—not the version that fits neatly in a diagram.
Choose the first useful slice
We define what the system will do, what stays manual, how success will be checked, and where the project stops.
Test with real cases
We deliver the working path early, run realistic inputs through it, and deal with the exceptions that prototypes usually ignore.
Make it operable
You get documentation, visible failure states, and a system your team can understand rather than a black box only its author can run.
Project rules
No AI theatre.
If ordinary software is the better tool, we will say so. If a human should approve the result, the workflow will make room for that.
Begin with a measurable operational problem.
Use the simplest technology that can do the job.
Design the failure path before launch.
Keep consequential decisions reviewable.
Leave behind something your team can operate.
Questions
Before you send a brief.
Do we need to replace our current software?
Usually not. A first project should work with the systems you already trust unless one of them is the reason the process keeps breaking.
Can the first project be small?
Yes. One workflow with clear boundaries is a better place to start than a company-wide “AI transformation.” It gives us real evidence before either side commits to more.
How is human review handled?
Where the cost of a bad result matters, we can use approval queues, confidence thresholds, audit logs, and explicit fallbacks instead of pretending every output is equally reliable.
What should we include in the estimate request?
Tell us what happens today, which tools are involved, where time is lost, and what would be different if the problem were fixed. Rough notes are enough.
Project enquiry
Show us the messy part.
You do not need a technical specification. Describe the process as it exists now and where it stops working.