Multi-source research synthesis and reporting workspace

For

Research teams and analysts producing evidence-backed reports from multiple sources

Solves

Research is scattered across many tools, so gathering, summarizing, citing and reporting take repeated manual work and citations drift from sources.

Delivers

Reviewer-approved evidence-backed reports with linked citations

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual research assembly while keeping every claim traceable to a source.

1. Run automated searches across permitted sources from a research brief.
2. Summarize long documents into key points.
3. Generate structured report drafts from analyzed findings.
4. Attach source citations to every claim.
5. Support shared editing, annotation and comments.
6. Render charts and graphs from supplied data.
7. Apply customizable report templates.
8. Analyze text, images and PDFs together.
9. Filter and refine searches with advanced options.
10. Answer follow-up questions to deepen exploration.
11. Generate articles and storylines from organized data.
12. Automate routine research and formatting tasks.
13. Connect to approved external platforms and destinations.
14. Offer real-time grammar and clarity suggestions.
15. Support forking and commenting on shared content.
16. Run plagiarism and moderation checks.
17. Show a productivity and performance dashboard.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewer-approved evidence-backed report with source references and unresolved questions.

Everything these tools do, in one app

• Automated research Automatically conducts searches and gathers information from multiple sources based on user prompts.Found in Perplexity Deep Research, ChatGPT Deep Research, Tavily and 1 more
• Content summarization Condenses lengthy documents and articles into concise summaries highlighting key points.Found in Inquisite, Sider 5.0: Deep Research with Wisebase, Sonar by Perplexity
• Report generation Creates detailed, structured reports from analyzed data and research findings.Found in Inquisite, Tyles, Perplexity Deep Research and 1 more
• Source citations Automatically generates citations and references to help users verify information credibility.Found in Sider 5.0: Deep Research with Wisebase, Perplexity Deep Research
• Collaboration tools Enables sharing, editing, and annotation of research findings for team projects.Found in Inquisite, Sider 5.0: Deep Research with Wisebase, ayraa 2.0
• Data visualization Provides charts and graphs to represent data visually for clearer communication.Found in Inquisite
• Customizable templates Offers pre-designed templates that can be tailored for different types of analyses and presentations.Found in Inquisite, Kompas AI, ayraa 2.0
• Multi-format analysis Analyzes and synthesizes data from diverse formats including text, images, and PDFs.Found in ChatGPT Deep Research
• Advanced search filters Allows precise queries and refined results through advanced filtering options.Found in Sider 5.0: Deep Research with Wisebase
• Interactive follow-ups Supports follow-up questions to refine search results and deepen exploration.Found in Sonar by Perplexity
• Content generation Generates articles, reports, or storylines from organized research data.Found in Tyles, Kompas AI, ayraa 2.0
• Task automation Automates routine tasks to reduce manual workload and improve efficiency.Found in ayraa 2.0
• Third-party integrations Connects with popular platforms and services to streamline workflow and content distribution.Found in Inquisite, Kompas AI, ayraa 2.0 and 1 more
• Real-time editing Provides real-time suggestions and editing assistance to improve grammar and clarity.Found in Kompas AI
• Community contributions Allows users to create, fork, and comment on content, fostering a collaborative environment.Found in Genspark
• Quality control Undergoes plagiarism checks and moderation to maintain accuracy and reliability.Found in Genspark
• Analytics dashboard Monitors productivity and performance through a dedicated dashboard.Found in ayraa 2.0

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, datasets, images and web sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Datasets

5. 4

   Images and web sources

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved evidence-backed reports with linked citations

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final interpretation, citation checks and publication decisions remain with qualified researchers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research brief and sources, Editable report workspace, Review and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, citations, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant claim. Make the task-specific outcome reviewer-approved evidence-backed reports with linked citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Author-owned documents, permitted datasets and licensed research sources. Cloud storage, document import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

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. 1

   Scoping call

   Day 1

   Thirty 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. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: run automated searches across permitted sources; summarize long documents; generate structured report drafts; attach source citations. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We 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.

1. Pick the riskiest assumption. Here: will research teams and analysts producing evidence-backed reports from multiple sources use it to solve "research is scattered across many tools, so gathering, summarizing, citing and reporting take repeated manual work and citations drift from sources"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted report sections per research hour and citation corrections after review.
4. Measure, then decide. Track accepted report sections per research hour and citation corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One research domain and one approved source set; final interpretation and citation checks remain with qualified researchers. Implement one approved input format, a bounded representative case set and the first four task modules: run automated searches across permitted sources; summarize long documents; generate structured report drafts; attach source citations. Support the remaining modules with operator review. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved evidence-backed reports with linked citations. Retain the explicit scope boundary: One research domain and one approved source set; final interpretation and citation checks remain with qualified researchers.

What the build depends on. Source upload and preview, asynchronous research jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One research domain and one approved source set; final interpretation and citation checks remain with qualified researchers.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

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.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined research package. Offer a monthly production allowance after repeat demand. Quote complex multi-source or specialist research separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved evidence-backed report with linked citations. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual research assembly while keeping every claim traceable to a source. Demonstrate a concrete reviewer-approved evidence-backed report with linked citations using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams and analysts producing evidence-backed reports from multiple sources professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved evidence-backed report with linked citations from a small authorized input set, with a transparent calculation of accepted report sections per research hour and citation corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five research teams and analysts producing evidence-backed reports from multiple sources and inspect a recent example of research scattered across many tools, so gathering, summarizing, citing and reporting take repeated manual work and citations drift from sources.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted report sections per research hour and citation corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted report sections per research hour and citation corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted report sections per research hour and citation corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved evidence-backed reports with linked citations. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved source sets, citation rules and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for research teams and analysts producing evidence-backed reports from multiple sources. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Inquisite, Tyles, Sider 5.0: Deep Research with Wisebase, Perplexity Deep Research, Kompas AI, ChatGPT Deep Research, Sonar by Perplexity, Tavily, ayraa 2.0 and Genspark. Compare this product with the buyer's present method on accepted report sections per research hour and citation corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Search and model calls, document processing, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved evidence-backed reports with linked citations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Researchers approve substantive claims and publication scope. One research domain and one approved source set; final interpretation and citation checks remain with qualified researchers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.