Technical discovery and feasibility
Find out what makes the project difficult before estimating around assumptions. Technical discovery tests the dependency most likely to change the architecture or scope.
Typical architecture
- 01
Inventory source systems and the required business operations.
- 02
Test access and representative data in an isolated environment.
- 03
Compare viable architecture paths against operational constraints.
- 04
Document the selected boundary and the evidence still missing.
What is technical discovery and feasibility?
Technical discovery tests whether a proposed system can work with the real data, integrations and constraints before a build is committed. RESULTWARD tests uncertain dependencies, documents constraints and closes with a decision rather than an open-ended report.
What problem does technical discovery and feasibility solve?
A proposal can assume that an API exposes the needed data, an old database is consistent or an AI task is feasible. Those assumptions often determine the real effort once work begins.
When is technical discovery and feasibility a useful investment?
Use technical discovery for unfamiliar systems, legacy migration, uncertain integration access or AI workflows without an evaluation baseline. A routine, well-understood change may not need a separate phase.
What do you receive?
- A dependency and constraint inventory with evidence attached to key assumptions.
- Focused feasibility experiments and architecture options with tradeoffs.
- A scope recommendation, risk register and estimate assumptions for human review.
How does the work progress?
- Time-box the investigation around explicit go/no-go questions.
- Present what was demonstrated separately from what remains an assumption.
What should you plan for integration?
Verify credentials can be scoped, sandboxes exist and required objects are accessible. An API being documented does not establish that the relevant account has permission to use it.
The risk to design around
A small successful sample may hide production volume, data quality or permission differences. Record those limits in the recommendation.
Security and responsibility
Use non-production access and sanitized samples. Keep investigation artifacts free from tokens and personal records that are not needed for the decision.