WoundIQ needed one number per wound. We built the system that produces it.
Ruler readings never matched. Neuramonks built a clinical wound assessment AI tool — one measurement standard across every clinician, visit and remote consult. Shipped in 8 weeks.
Delivered for WoundIQ
- 70%
- Less production effort
- 60%
- Faster time-to-publish
- Catalogue
- Straight to episode
- Voice
- Human, not robotic
Delivered for the L&D team
- 70%
- Less production effort
- 60%
- Faster time-to-publish
- Catalogue
- Straight to episode
- Voice
- Human, not robotic
The Client's Problem
- Product docs nobody on the floor read
- An episode reps listen to on the commute
- Training material rebuilt by hand each launch
- Generated from the product catalogue itself
- Sales ramping weeks after launch
- Episodes out 60% sooner
What We Delivered
- 01 · Content Engineering
- Catalogue ingestion
- Reads product data, specs and positioning from the catalogue.
- 02 · Generative AI
- Episode script generation
- Turns product detail into a conversation worth listening to.
- 03 · Voice AI
- Human-sounding narration
- Natural delivery, not a robotic document readout.
- 04 · Audio Engineering
- Assembly and mastering
- Segments stitched, levelled and topped and tailed.
- 05 · Product Engineering
- Review console
- L&D approves each episode before the team hears it.
- 06 · Integration
- Distribution to sales
- Episodes and transcripts pushed to where reps listen.
- Across all six · Pre-launch product data
- Encrypted end to end
- Pre-launch catalogue data and audio encrypted in transit and at rest.
- Access-controlled by role
- Only the launch team sees material before release.
- Approved voices only
- Voice configuration restricted to sources the client approves.
Launching to a team that has not read the docs?
We scope which pieces your build actually needs, in 30 minutes.
The Result
70%
Less production effort per launch, with episodes reaching the sales team 60% sooner.
How The Engagement Ran
- Phase 1
- Discovery
- Launch training workflow mapped with the L&D team.
- Phase 2
- Catalogue ingestion
- Product data structured into teachable episode material.
- Phase 3
- Script and voice
- Conversational scripts and human-sounding narration tuned.
- Phase 4
- Review and rollout
- Approval console released, then distribution to sales.
- PyTorch
- OpenCV
- Python
- React
- PostgreSQL
- AWS
- Encrypted storage
- Role-based access
Why Neuramonks
Why WoundIQ chose Neuramonks.
- Outcome-driven delivery
- Effort and publish-time targets set before development started, 9-week average build.
- Generative systems in production
- 200+ AI models shipped, pipelines that deliver finished output, not drafts.
- Human in the loop by design
- L&D approves every episode before release.
- Deployable on your terms
- On-premises or air-gapped where policy requires it
See the full build approach on the Agentic AI Services page.
Common Questions
What teams ask about this build.
How does AI measure wound size from a photo?
An Attention U-Net model segments the wound boundary and a calibration marker in frame sets the real-world scale, returning length, width and area in under 5 seconds.
How does it compare to manual ruler measurement?
A ruler reading changes with who holds it and at what angle. Calibration fixes the scale automatically, which cut variability between WoundIQ clinicians by roughly 90 percent.
What is remote wound monitoring technology?
Measuring a wound from a photo taken between visits. It only works if the remote reading is comparable to the in-clinic one, which is what the calibrated measurement layer provides.
How is patient image data secured?
Images and measurement records are encrypted in transit and at rest, access is restricted by role, and every read of a patient record is logged.
How long does a build like this take?
WoundIQ went from kickoff to clinic pilot in 8 weeks. Scope drives the number, but a working segmentation and measurement pilot typically ships in 6 to 8 weeks.
