Solution Database / Science and Research
Evidence mapping service
Published evidence is difficult to compare systematically. A documented coding protocol and source passages behind each extracted field.
01The offer
For research teams preparing scoping studies, turn study collection, research questions and coding protocol into reviewed evidence matrix. Address the recurring problem: published evidence is difficult to compare systematically. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Research teams preparing scoping studies
- Takes in
- Study collection, research questions and coding protocol
- Delivers
- Reviewed evidence matrix
- Message
- Evidence mapping service for research teams preparing scoping studies. A documented coding protocol and source passages behind each extracted field. Demonstrate the claim through a small evidence map with traceable extraction.
- Lead magnet
- A small evidence map with traceable extraction
02How it works
- Define extraction fields
- Code study methods
- Map populations
- Capture reported outcomes
- Track reviewer disagreements
- Export structured evidence
Workflow
Agree the decision and research questions, define permitted sources or participants, collect evidence, code findings, compare supporting and contradictory material, review interpretations, and deliver a cited brief with next questions. Start with study collection, research questions and coding protocol and finish with reviewed evidence matrix.
AI and people
Assist with retrieval, transcription, structured extraction and thematic synthesis. Preserve source passages and methodological context. Human researchers validate inclusion, quotations and conclusions. Use real participants when customer research is required.
Screens
Key screens: Study matrix, evidence map, reviewer disagreements. Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. In this product, the first view is study matrix, followed by evidence map and reviewer disagreements.
Admin
Source provenance, participant consent where applicable, research questions, coding definitions, reviewer disagreements, citations and versioned conclusions.
03Market gap
Alternatives buyers use today
Research consultants, internal analysts, literature databases and general search or summarization tools. Differentiate on this specific proposed advantage: a documented coding protocol and source passages behind each extracted field. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
Niche research protocols, credible researcher relationships and a rights-cleared evidence archive with consistent interpretation methods. For this solution, build around a documented coding protocol and source passages behind each extracted field. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Science and Research 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: published evidence is difficult to compare systematically.
05Proof & signals
Channels where buyers gather: Research methods consultants. Metrics that prove it works: Reviewer agreement, source accuracy.
Paid pilot
Answer one practical question using a bounded evidence set. Ask a domain expert to review citations and reasoning, identify contrary evidence and assess whether the deliverable supports the intended decision. For this solution, use study collection, research questions and coding protocol and evaluate reviewed evidence matrix. Agree success thresholds with the buyer before starting; collect a baseline for reviewer agreement, source accuracy. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with research teams preparing scoping studies and one recurring use case. Build the first two modules: define extraction fields; code study methods. Provide operator assistance for the third module: map populations. Deliver reviewed evidence matrix through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
First 30 days
Week 1: interview five prospective buyers in this segment: research teams preparing scoping studies. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: a small evidence map with traceable extraction. Week 3: present it through research methods consultants and seek one narrowly scoped paid pilot. Week 4: review reviewer agreement, source accuracy, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: capture reported outcomes; track reviewer disagreements; export structured evidence. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
Retention
Maintain the research question and evidence archive, offer follow-up studies and refresh important sources. Build repeat work around the buyer’s decision cycle.
Integrations
Authorized datasets, papers, protocols, code and research records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. These are candidate integration categories, not verified supported connectors.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: define extraction fields; code study methods. Manual review in the loop. | 4 days | $8,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 5 days | $9,000 |
| Full product | Remaining modules: capture reported outcomes; track reviewer disagreements; export structured evidence. Self-serve onboarding, billing, monitoring and the wider integration set. | 9 days | $12,500 |
| Total | $29,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $80–$160 | $110–$220 |
| Full product (about 50 customers) | $110–$210 | $880–$1,750 | $990–$1,960 |
Revenue model to test
Test USD 750-3,000 for one tightly bounded research question and evidence pack. Participant recruitment, specialist review and licensed data are separately scoped. Repeat tracking can become a retainer. Prices are hypotheses.
Cost drivers
Researcher time, source access, participant recruitment, transcription, evidence coding, expert review and report revisions.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.