Discovery, automation and integration guidance for businesses building connected software and AI-enabled workflows.
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The best time to remove uncertainty is before it becomes code
Custom software projects become expensive when different people believe they are building different products. Discovery converts a business problem into an agreed model of users, workflows, data, constraints, risks and acceptance criteria.
The output is not necessarily a giant specification. It is enough shared clarity to estimate, design and build the next stage responsibly.
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1. Define the business problem before the feature list
Describe what is currently slow, risky, expensive or impossible. Record how the process works today, including spreadsheets, email, manual approvals and workarounds. Then identify the outcome the software should improve.
A feature that cannot be connected to a user or business outcome deserves challenge.
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2. Identify users, roles and boundaries
List the people and systems that will use the product. Define what each role can see, create, change, approve and export. Include administrators, external customers, service accounts and API clients where relevant.
Permissions discovered late are expensive because they affect screens, data models, APIs and audit behaviour.
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3. Map the core workflows
Walk through the important journeys from trigger to completion. Include alternate paths, cancellations, failures, rework and approvals. Real systems spend a surprising amount of time outside the “happy path”.
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4. Define data ownership and sensitivity
Identify the information the system will store or process, its source, sensitivity, retention needs, export requirements and system of record. Decide which data can be copied and which should remain in another platform.
For personal, financial, health or other sensitive information, privacy and security requirements should influence architecture from the start.
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5. Discover integrations before they become blockers
List external systems and verify that the required APIs, webhooks, credentials, rate limits and permissions actually exist. “Integrates with X” is not enough. Define which data moves, in which direction, how often and what happens when either system is unavailable.
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6. Separate MVP from eventual vision
An MVP should contain the smallest coherent set of capabilities that proves the highest-value workflow, not simply a random subset of screens. Record future requirements so the architecture does not block them, but do not force every future possibility into the first release.
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7. Write acceptance criteria people can test
For important requirements, define what must be true for the work to be accepted. Good acceptance criteria turn subjective statements such as “easy to use” or “secure” into observable behaviour and agreed controls.
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8. Decide operational responsibility
Before development finishes, someone needs to own hosting, deployments, backups, monitoring, incident response, user support, dependency updates and future change. Software without an operating model starts accumulating risk immediately after launch.
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What a useful discovery output should contain
- Problem statement and desired outcomes.
- User and role model.
- Core workflows and exception paths.
- Prioritised requirements.
- Data and integration map.
- Security and compliance constraints.
- MVP boundary and later phases.
- Acceptance criteria.
- Proposed technical approach.
- Delivery assumptions, risks and estimate range.
That package gives both buyer and developer something concrete to challenge before larger development spend begins.