Step 1Turn your validated solution into a spec your coding agent can use
Most specs are a paragraph in a ticket. The coding agent fills in the gaps with guesses, and you find out later which guesses were wrong. Revelica story maps capture the user stories, the customer context, the opportunity you're solving, and the assumptions that need to hold. Export the whole thing as structured JSON that any LLM-powered coding agent can parse and act on.
Step 1.1
Story maps capture what and why
User stories in sequence, customer segments as actors, the specific opportunity you're solving, and assumptions categorized by risk. One artifact with the full picture instead of scattered tickets and docs.
Step 1.2
Export specs your coding agent can parse
Export the story map as structured JSON. User stories, acceptance criteria, customer context, and assumptions in a format that fits in a context window and gives the agent everything it needs to make good decisions during implementation.
Step 1.3
Include the evidence that informed the solution
The spec references the customer segments, experience maps, and opportunities from your research. When the coding agent needs to understand why a feature works this way, the answer is in the spec, not in someone's head.
Step 2Keep your coding agent connected to discovery
A JSON export is a snapshot. But implementation raises questions the spec didn't anticipate. When your coding agent hits an ambiguous UX decision or an edge case that could go either way, it needs access to the discovery context that informed the solution. The Revelica MCP server gives coding agents on-demand access to your strategic context, customer research, and validated assumptions.
Step 2.1
Strategic context on demand
The coding agent can query your mission, objectives, desired outcomes, and product principles. When it needs to make a trade-off, it makes the one your team would make.
Step 2.2
Customer evidence when decisions get ambiguous
When the agent faces a UX decision the spec didn't cover, it can pull the relevant customer stories and experience maps. The answer comes from evidence, not from the agent's training data.
Step 2.3
Assumption awareness during implementation
The agent knows which assumptions are validated, which are risky, and which haven't been tested yet. It can flag when an implementation choice depends on an untested assumption instead of quietly building on it.
Step 3Keep learning as you build
Delivery isn't the end of discovery. New conversations reveal better opportunities. Solved problems change your competitive position. The teams that win keep the evidence flowing and let it redirect them when the data says to.
Step 3.1
Measure whether you're moving the outcome
Are the features you're shipping actually delivering the desired outcome? If the needle isn't moving, the opportunity may have been wrong, or the solution isn't addressing it well enough. The evidence will tell you which.
Step 3.2
Let new insights redirect you
New customer stories may reveal better opportunities than the one you're pursuing. That's not failure, that's the system working. Update your segments, reassess the opportunity scores, and shift if the evidence says to.
Step 3.3
Close the loop to strategy
When you've successfully addressed an opportunity, your competitive position changes. Your differentiation formula updates. Feed what you've learned back into the Strategy workflow. Customer evidence is what makes competitive analysis real instead of theoretical.
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