Structured capability discovery
Start a Capability Discovery
Add capability without disrupting what already works.
This intake maps your systems, proprietary data, workflows, and controls to find where software, automation, or protected AI can create safe, measurable value. Ten bounded answers now, a human conversation next — nothing on this page is scored by a model or answered by a machine.
Do not enter confidential, sensitive, or personal records. This form asks only for business contact details and controlled choices.
Prepared locally — sending
Prepared discovery request review
The request passed its local checks and is on its way to the VindexAI lead service.
- Contact name
- Work email
- Company or organization
- Who is this starting point for?
- How much of the operation is in view?
- What kind of need is closest?
- Which systems are in view?
- How sensitive is the data in scope?
- Which controls context applies?
- Which capability outcome is closest?
- Request source
Confirmed reference
Prepared request ID
Preparation reason codes
What we ask and why
Five bounded answers that shape the discovery.
Each question is a controlled choice with a fixed set of answers. Together they bound the discovery — what is in view, where it runs, what it touches, and what would make it worthwhile — so the conversation that follows starts from your operating reality instead of a pitch.
How much of the operation is in view
One workflow, one business unit, or the whole enterprise. The scope answer bounds the discovery before anyone proposes anything: a bounded first step and an operating-system conversation are different engagements, and the boundary is chosen before the work is.
Which systems are in view
An ERP or accounting system, CRM and sales systems, an industry platform, spreadsheets and shared drives, or several of these together. Naming the systems first keeps the conversation about the estate you actually run, not about a product looking for a place in it.
How sensitive the data is
Internal business data, customer or partner confidential data, regulated or audited records, or proprietary methods and trade secrets. The sensitivity answer decides what protection must exist before any new capability touches your data — designed in from the first conversation, never retrofitted.
Which controls context applies
From no formal framework yet to regulated and externally audited. Your controls context bounds what governed access has to look like, so nothing proposed later can quietly bypass the approvals your operation already answers to.
Which outcome is closest
Reducing manual effort, connecting systems, visibility over operating state, or protected AI enablement — and not sure yet is a legitimate answer, because finding the right outcome is exactly what the discovery is for.
What happens when you send
One prepared request, one record, one human reply.
The form above is the whole request. It is checked in this browser, shown back for review, and sent as a single structured record — then a person takes over. The page never claims more than the lead service has confirmed.
Only the fields above are sent
The prepared request carries the ten fixed answers shown in the review — three free-text business contact fields (your name, work email, and organization) and seven controlled answer choices — and nothing else.
One record per prepared request
Each prepared request carries one request ID, so retrying a failed send can never create a duplicate — the service accepts the same request exactly once, and a confirmed reference is shown only after the lead service returns one.
A person replies by email
A person reads the recorded answers and replies by email to schedule the discovery conversation. There is no automated sequence and no marketing enrollment — sending this request asserts nothing beyond the request itself.
Discovery is human-led fit analysis
The conversation examines fit against your systems, data, controls, and desired outcome. It can conclude that AI — or new software altogether — is not the right answer, and that conclusion counts as a correct result. Protected AI enablement is proposed only when it fits.