Research collaboration discovery
Method-level complementarity supported by specific published work.
- For
- R&D groups seeking complementary expertise
- Solves
- Potential collaborators are identified from incomplete informal networks.
- Delivers
- Collaboration research briefs
- Built in
- about 4 weeks of creation time, MVP in 4 days
- Investment
- $8,000 for the MVP, $29,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For r&D groups seeking complementary expertise, turn published work, declared project needs and collaboration criteria into collaboration research briefs.
- Define capability gaps.
- Identify relevant teams.
- Cite supporting publications.
- Compare complementary methods.
- Flag outdated affiliations.
- Prepare collaboration concepts.
What goes in, what comes out
- Published work
- Declared project needs
- Collaboration criteria
AI drafts, people review. Transparent opportunity matching and shortlist platform.
- Collaboration research briefs
How it works
The workflow
- InStart with
Published work, declared project needs and collaboration criteria
- 1
Define buyer-selected criteria
- 2
Gather authorized opportunity information
- 3
Apply explicit eligibility rules
- 4
Propose matches with evidence
- 5
Let the user review uncertain conditions
- 6
Save a shortlist and track the resulting conversations or applications
- OutFinish with
Collaboration research briefs
AI does the heavy lifting, people stay in charge
Extract criteria, normalize opportunity descriptions and explain possible fit. Use explicit rules for hard requirements. Do not invent missing eligibility facts or represent a suggested match as a verified qualification.
What your team sees
Key screens: Expertise map, team profiles, fit evidence. Open with a filterable opportunity feed and clear fit explanations. Each profile shows source evidence, eligibility conditions and missing information. Keep saved, rejected and needs-review states. Include a deadline or next-action view without hiding the basis of recommendations. In this product, the first view is expertise map, followed by team profiles and fit evidence.
Accounts and administration
Editable criteria, dated sources, eligibility evidence, missing-data flags, saved shortlists, rejection reasons, deadline alerts and owner follow-up.
Integrations and data access
Authorized datasets, papers, protocols, code and research records. Permitted opportunity feeds, customer profiles, calendars and CRM exports. Keep initial outreach or applications as user-reviewed drafts. These are candidate integration categories, not verified supported connectors.
How we build it
We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.
- 1
Scoping call
Day 1Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.
- 2
MVP
4 daysOne buyer segment, one recurring use case; first modules: define capability gaps; identify relevant teams. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
5 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
9 daysRemaining modules: compare complementary methods; flag outdated affiliations; prepare collaboration concepts. Self-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.
Why we start with an MVP
An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.
- Pick the riskiest assumption. Here: will R&D groups seeking complementary expertise use it to solve "potential collaborators are identified from incomplete informal networks"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Produce a small shortlist and ask the buyer to verify fit independently.
- Measure, then decide. Track verified relevance and useful introductions. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Begin with r&D groups seeking complementary expertise and one recurring use case. Build the first two modules: define capability gaps; identify relevant teams. Provide operator assistance for the third module: cite supporting publications. Deliver collaboration research briefs 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.
After the MVP. After paid pilots establish value, automate the remaining modules: compare complementary methods; flag outdated affiliations; prepare collaboration concepts. 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.
What the build depends on. Current source information, explicit eligibility rules, entity identity checks and inspectable fit reasoning. Sparse evidence limits match quality.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: define capability gaps; identify relevant teams. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Remaining modules: compare complementary methods; flag outdated affiliations; prepare collaboration concepts. Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$29,000about 4 weeks of creation time · start with the MVP from $8,000
Running costs per month
A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $40–$90 | $70–$150 |
| Full productabout 50 customers | $110–$210 | $280–$560 | $390–$770 |
Run it or resell it
For your own team
R&D groups seeking complementary expertise run it inside the business: published work, declared project needs and collaboration criteria in, collaboration research briefs out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#91273f - accent
#54c9c7 - surface
#f1e4e7 - ink
#22201e
- Headings
- Sora
- Text
- Work Sans
- Voice
- Rigorous, transparent, cited
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 150-600 monthly for one narrow opportunity feed, or USD 750-2,500 for a bespoke researched shortlist. Price manual verification and custom research explicitly. These are pricing hypotheses.
Message to test
Research collaboration discovery for r&D groups seeking complementary expertise. Method-level complementarity supported by specific published work. Demonstrate the claim through a shortlist of complementary teams with evidence.
Where to find buyers
Research partnership offices
Lead magnet
A shortlist of complementary teams with evidence
The first 30 days
- Week 1: interview five prospective buyers in this segment: r&D groups seeking complementary expertise. Ask to see a recent example of the problem and their current process.
- Week 2: prepare this demonstration using authorized or synthetic material: a shortlist of complementary teams with evidence.
- Week 3: present it through research partnership offices and seek one narrowly scoped paid pilot.
- Week 4: review verified relevance, useful introductions, total delivery effort and a concrete renewal decision before increasing scope.
Paid pilot
Produce a small shortlist and ask the buyer to verify fit independently. Record why each option is accepted or rejected and whether it leads to a useful next step. Review missed eligible options too. For this solution, use published work, declared project needs and collaboration criteria and evaluate collaboration research briefs. Agree success thresholds with the buyer before starting; collect a baseline for verified relevance, useful introductions. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
Success metrics
Verified relevance, useful introductions
Retention and expansion
Refresh opportunity profiles, improve criteria from accepted and rejected matches, and offer deeper verification for shortlisted options.
Why clients would pick it
A maintained niche opportunity dataset and documented relevance feedback, supported by relationships with the intended buyer community. For this solution, build around method-level complementarity supported by specific published work. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
Alternatives and positioning
Manual research, directories, generic databases, referrals and existing opportunity marketplaces. Differentiate on this specific proposed advantage: method-level complementarity supported by specific published work. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Main delivery costs
Source collection, profile updates, entity resolution, eligibility verification, analyst research and customer feedback review.
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.