Source-linked multimodal document and screen assistant

For

IT and development teams that process images, documents and screen content together

Solves

Visual and textual material sits in separate tools, so teams cannot answer questions or generate content from combined evidence.

Delivers

Reviewed source-linked answers and generated content with citations and coordinates

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce manual cross-referencing while keeping answers tied to their source.

1. Ingest images, documents, video frames and screen captures.
2. Recognize objects, scenes and details in visual inputs.
3. Understand natural language text alongside visual content.
4. Generate coherent written content from combined evidence.
5. Maintain context across multi-turn conversations.
6. Analyze video frames for events over time.
7. Return coordinates and timestamps for objects and events.
8. Answer event-level questions such as counting occurrences.
9. Extract precise answers from diverse content sources.
10. Summarize long documents into key points.
11. Support websites, PDFs, databases and image formats.
12. Adapt workflows to team and project needs.
13. Process inputs in real time with immediate updates.
14. Handle high-resolution images and screen content.
15. Support GUI and screen automation tasks.
16. Switch between fast perception and deeper reasoning by task complexity.
17. Connect to external apps and services.
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 reviewed source-linked answer set with source references and unresolved questions.

Everything these tools do, in one app

• Multimodal understanding Combines visual and textual data to analyze and interpret content together.Found in DeepSeek-VL2, Janus, NVLM 1.0 and 2 more
• Image recognition Identifies and interprets objects, scenes, and details within images.Found in DeepSeek-VL2, Janus, NVLM 1.0 and 1 more
• Language understanding Comprehends and processes natural language text for accurate responses.Found in DeepSeek-VL2, Qwen 2.5, Llama 3.3 70B and 2 more
• Text generation Produces coherent and contextually appropriate written content.Found in Qwen 2.5, Llama 3.3 70B, Janus
• Multi-turn conversation Maintains context over extended back-and-forth interactions.Found in Qwen 2.5
• Video analysis Processes multiple frames or video clips to understand events over time.Found in Molmo 2
• Spatial pointing and tracking Returns coordinates and timestamps for objects or events in visual media.Found in Molmo 2
• Event-level answers Provides answers linked to specific instances in time and space, such as counting occurrences.Found in Molmo 2
• AI-driven search Extracts precise answers from diverse content sources using AI.Found in Ferret
• Document summarization Condenses lengthy documents to highlight key points.Found in Ferret
• Multiple content type support Handles various input formats like websites, PDFs, and databases.Found in Ferret, DeepSeek-VL2
• Customizable workflows Adapts processes to fit different industry or project needs.Found in DeepSeek-VL2, InternVL3, Skywork-R1V
• Real-time processing Delivers immediate data interpretation and updates.Found in DeepSeek-VL2, Skywork-R1V
• Open-source availability Provides free access to model weights, code, or data for customization and community contribution.Found in Janus, NVLM 1.0, Molmo 2 and 1 more
• High-resolution input handling Processes high-resolution images or screen content effectively.Found in Phi-4-reasoning-vision
• GUI and screen agent optimization Tailored for tasks involving graphical user interfaces and screen automation.Found in Phi-4-reasoning-vision
• Adaptive processing Switches between fast perception and deeper reasoning based on task complexity.Found in Phi-4-reasoning-vision
• Integration with third-party tools Connects with external apps and services for enhanced workflow.Found in Skywork-R1V, Ferret

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted images, documents, video frames, screen captures and text sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted images

4. 3

   Documents

5. 4

   Video frames and screen captures

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed source-linked answers and generated content with citations and coordinates

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 factual, legal and security checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source intake and permissions, Assistant console with evidence panel, Admin console for models, access and audit. Use a thumbnail gallery for sources, a large central answer canvas, and a right-hand panel for citations, coordinates and comments. Let users compare draft and approved answers side by side. Display draft, changes requested and approved states. Provide a share link with comments anchored to the relevant source region. Make the task-specific outcome reviewed source-linked answers and generated content 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

Customer-owned document stores, screen capture tools, ticketing systems and permitted external apps. Cloud storage, design-file 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

   5 days

   One buyer segment, one recurring use case; first modules: ingest images, documents, video frames and screen captures; recognize objects, scenes and details in visual inputs; understand natural language text alongside visual content. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 IT and development teams that process images, documents and screen content together use it to solve "visual and textual material sits in separate tools, so teams cannot answer questions or generate content from combined evidence"?
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 answers per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted answers per reviewer hour and corrections after approval; 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 approved input format set and one bounded case library; final factual, legal and security checks remain human. Implement one approved input format, a bounded representative case set and the first three task modules: ingest images, documents, video frames and screen captures; recognize objects, scenes and details in visual inputs; understand natural language text alongside visual content. 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 reviewed source-linked answers and generated content. Retain the explicit scope boundary: final factual, legal and security checks remain human.

What the build depends on. Source upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity screen automation requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: final factual, legal and security checks remain human.

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 source package. Offer a monthly processing allowance after repeat demand. Quote complex video, screen automation or specialist integration separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed source-linked answer set. 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 cross-referencing while keeping answers tied to their source. Demonstrate a concrete reviewed source-linked answer set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked answer set from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams that process images, documents and screen content together and inspect a recent example of visual and textual material sitting in separate tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted answers per reviewer hour and corrections after approval, 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 answers per reviewer hour and corrections after approval. 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 answers per reviewer hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed source-linked answers and generated content. 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 cases, reviewer corrections and verified operating constraints, together with reliable delivery for a narrow IT and development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams that process images, documents and screen content together. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

DeepSeek-VL2, InternVL3, Skywork-R1V, Janus, NVLM 1.0, Molmo 2, Ferret, Qwen 2.5, Llama 3.3 70B and Phi-4-reasoning-vision, used separately or as rented subscriptions. Compare this product with the buyer's present method on accepted answers per reviewer hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, video or image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed source-linked answers and generated content. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external actions. Final factual, legal and security checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.