VaaS for scientific marketing content
You’ve adopted AI. Your team is creating content faster than ever. Blogs, social media posts, email campaigns – it’s all flowing. But here’s the question keeping you up at night: is it scientifically accurate?
One inaccurate claim about a diagnostic assay, misrepresented mechanism of action, garbled explanation of a technical process or inaccurate reference is all it takes to undermine years of carefully built brand credibility with your scientific audience.
AI is brilliant at generating content. It’s terrible at verifying scientific accuracy.
That’s where kdm’s VaaS comes in. As a specialist scientific agency with PhD-level writers across multiple disciplines, we act as your technical validation layer, auditing your AI-generated content to ensure it’s scientifically sound, on brand, technically precise and fit for publication.
Think of us as your scientific quality assurance team. You get the efficiency gains of AI without the risk of publishing something that damages your reputation.
Our approach step by step
1. Listen: understand your content landscape and risk tolerance
We start by understanding what you’re creating, how you’re creating it, and who you are creating it for.
What AI tools are you using? What types of content are you generating – social posts, blogs, white papers, case studies? How technical is your subject matter? What’s your internal review process? Who currently checks for scientific accuracy, are they the right person, and how much time does that take?
More importantly, we explore your risk landscape. What happens if you publish something scientifically inaccurate? Are there legal or regulatory implications? How technically sophisticated is your audience? (Spoiler alert: if you’re marketing to PhD scientists, clinicians or technical buyers, they’ll spot errors immediately, and relish doing so!)
We also assess where you’re most vulnerable. AI-generated content about general topics (company news, event announcements) carries low scientific risk. AI-generated content about mechanisms of action, assay protocols, clinical applications or technical specifications? That’s high risk.
This discovery phase helps us to understand which content needs verification, and how rigorous that verification needs to be.
2. Think: design a verification workflow that fits your process
Based on your content landscape, we design a verification workflow that integrates seamlessly with your existing processes.
For teams creating content regularly, we typically recommend a retainer-based approach:
- Submit content to a shared folder or project management system
- We review within an agreed timeframe (with flexibility to turn things around faster for urgent tasks)
- We provide marked-up documents with corrections, suggestions and explanatory comments
- You make final edits and publish with confidence
For teams with sporadic needs, we offer project-based verification:
- Submit content as needed
- We provide quotes based on length, technical complexity and turnaround time
- You only pay for what you use
We also establish the level of verification you need:
Light verification – check for obvious scientific errors, terminology issues and technical inaccuracies. Suitable for social media posts, short blog articles and general marketing copy.
Standard verification – comprehensive review of scientific claims, technical explanations, data interpretation and citations. Suitable for longer blog articles, case studies, web copy and marketing materials.
Deep verification – rigorous technical review including checking primary sources, validating methodology descriptions, ensuring regulatory compliance and verifying all scientific claims. Suitable for white papers, application notes, technical guides and thought leadership content.
The goal is to create a workflow that protects accuracy without becoming a bottleneck.
3. Do: verify content with PhD-level scientific expertise
Once content is submitted, our PhD-level team gets to work. Depending on the subject matter, we match content to writers with relevant expertise – proteomics, genomics, diagnostics, clinical research, medical devices, drug discovery, imaging, spectroscopy, components, semi-conductor industry, nanotechnology, etc.
What we check:
Scientific accuracy – are the claims correct? Is the terminology used appropriately? Are mechanisms of action explained accurately?
Technical precision – are protocols, methodologies and processes described correctly? Are technical specifications accurate?
Citation verification – if claims reference research, are those references accurate and appropriately used?
Regulatory sensitivity – could any claims create regulatory issues? Are there assertions that need qualification or evidence?
Consistency – does the content align with your established messaging, product information and brand voice?
Clarity for the target audience – is the technical level appropriate? Will your audience understand this, or is it too simplified/too complex?
We don’t rewrite your content (unless you want us to). We provide marked-up documents showing:
- Errors flagged for correction
- Suggestions for improvement
- Comments explaining why something is problematic and how to fix it
- Questions where we need clarification from your subject matter experts
You maintain control of your content and your voice. We simply ensure it’s scientifically sound and grammatically correct.
4. Review: track accuracy, build confidence and refine the process
After each verification cycle, we track metrics that matter:
- Turnaround time – are we meeting your deadlines?
- Error rate – what percentage of content required corrections? (This typically decreases over time as your team learns)
- Content types – which formats need the most attention?
- Common issues – are there recurring accuracy problems we can address through training?
We also provide periodic summaries showing what we’ve verified, what issues we’ve caught, and where your team’s AI usage is most successful. This helps you to refine your AI workflows and identify where human-led content is essential.
The goal isn’t just to catch errors – it’s to help your team get better at using AI responsibly over time.