Marketing experiment archive navigator

01The offer

For growth teams running repeated tests, turn approved experiment reports and metric definitions into cited experiment learning brief. Address this specific problem: teams rerun failed experiments because learning is hard to retrieve. The aim: retrieve relevant past learning without treating correlation as proof. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.

For

Growth teams running repeated tests

Takes in

Approved experiment reports and metric definitions

Delivers

Cited experiment learning brief

Message

Retrieve relevant past learning without treating correlation as proof. Demonstrate the result with index thirty historical tests for growth teams running repeated tests. Use a concrete before-and-after example without promising unmeasured savings.

Lead magnet

Index thirty historical tests

02How it works

1. Index hypotheses
2. Link results and limitations
3. Match new proposals
4. Surface contradictory findings
5. Flag changed contexts
6. Export learning briefs

Workflow

The buyer creates a project, supplies approved experiment reports and metric definitions, and confirms scope and access. The working sequence is: 1. Index hypotheses. 2. Link results and limitations. 3. Match new proposals. 4. Surface contradictory findings. 5. Flag changed contexts. 6. Export learning briefs. Users correct extracted facts, resolve flagged uncertainties and approve the final cited experiment learning brief before use. Retain source links and a version history for the next cycle.

AI and people

Match experiment contexts and explicitly preserve limitations. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

Screens

Key screens: Experiment library, Similarity search, Evidence brief. Give end users a simple search or conversation surface with short answers and expandable citations. Administrators get source status, unanswered questions and handoff queues. Show the source date beside relevant answers. Keep conversation context available to the staff member receiving an escalation. Open with experiment library; move into similarity search for the detailed task; finish in evidence brief for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.

Admin

Source ownership, document permissions, freshness checks, conversation history, human handoff, feedback, test questions, usage limits and access logs. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.

03Market gap

Alternatives buyers use today

Manual search, static FAQs, general chat tools and support or intranet suites. Position this concept around retrieve relevant past learning without treating correlation as proof. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.

Where this wins

A maintained domain knowledge collection, realistic evaluation questions, useful escalation paths and integrations in the customer’s daily work. For this concept, accumulate permissioned examples and reviewer corrections around retrieve relevant past learning without treating correlation as proof. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

04Why now

Marketing teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: teams rerun failed experiments because learning is hard to retrieve.

05Proof & signals

Channels where buyers gather: Growth communities and experimentation consultants. Metrics that prove it works: Repeated proposals and evidence retrieval time.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run index thirty historical tests and deliver cited experiment learning brief. Compare repeated proposals and evidence retrieval time with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.

06Execution plan

MVP

Costed pilot: Archive retrieval; no automated causal conclusions. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: index hypotheses; link results and limitations. Support the third task through an assisted review queue: match new proposals. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of cited experiment learning brief. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

First 30 days

Week 1: interview five prospective buyers from growth teams running repeated tests and inspect how they handle teams rerun failed experiments because learning is hard to retrieve. Week 2: prepare index thirty historical tests using authorized or synthetic material. Week 3: share the demonstration through growth communities and experimentation consultants and seek one bounded paid pilot. Week 4: measure repeated proposals and evidence retrieval time, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.

After the pilot

After paying customers repeatedly accept cited experiment learning brief, automate surface contradictory findings; flag changed contexts; export learning briefs. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. Archive retrieval; no automated causal conclusions.

Retention

Build repeat use around cited experiment learning brief. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on repeated proposals and evidence retrieval time. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Integrations

Approved brand material, campaign exports and authorized customer research. Approved knowledge repositories, websites, service desks and staff messaging systems. Validate access inheritance and use read-only ingestion for the initial deployment. Begin with uploads and exports of approved experiment reports and metric definitions. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

07Investment and running costs

Revenue model to test

Test USD 500-2,000 setup plus USD 150-600 monthly for one defined source collection and usage allowance. Price multi-location deployments and specialist support separately. Validate willingness to pay; these are hypotheses. For this buyer, package the first sale around index thirty historical tests and the defined cited experiment learning brief. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Cost drivers

Document ingestion, retrieval and generation, source maintenance, support, evaluation and staff time handling unresolved cases. Initial validation additionally budgets for experimentation specialist review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.

Safeguards

Verify product claims and permissions. Distinguish observed campaign results from causal explanations and keep customer data collection authorized. Archive retrieval; no automated causal conclusions. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.