Without compromising quality or compliance market access teams are under pressure to deliver more strategy, faster. So all above the AI hype train? Not so fast!
Over 30 years' experience revolutionizing strategy within healthcare, pharma and life sciences.
Without compromising quality or compliance market access teams are under pressure to deliver more strategy, faster. So all above the AI hype train? Not so fast!
Most generative AI tools, including large language models (LLMs) like GPT-4, were not built for this world. They sound confident, but they don’t understand what’s relevant, what’s verifiable, or what’s at stake.
That’s because LLMs have only been trained to predict language. Without guardrails, they hallucinate. They blend fact with fiction. And in regulated environments like market access, that’s dangerous.
At Knowledgeable, we’ve taken Retrieval-Augmented Generation (RAG), an approach that anchors AI responses in verified content, and brought it to the next level. By combining expertly curated knowledge, semantic structure, and strict attribution boundaries, we’ve created a platform that generates content only from source material you can trust.
This whitepaper explains how we do it.
We unpack why grounding matters, how our system ensures AI never steps beyond the data, and what this means for market access teams looking to work faster, smarter, and with total confidence in every recommendation they deliver.
Strategy can only happen when AI knows its place.
If you're a market access consultant, your job is about making sense of complexity. Every recommendation you deliver: whether in a Global Value Dossier (GVD), Target Product Profile (TPP), or payer engagement plan depends on interpreting evidence and aligning it with commercial strategy, regulatory precedent, and unmet need.
You need confidence in your data. You need traceability. You need outputs you can defend.
Generic LLMs are trained on internet-scale data—books, forums, research abstracts, Reddit threads. That breadth gives them fluency. But it doesn’t give them judgment. LLMs don’t know the difference between exploratory and primary endpoints, or that price dynamics in oncology don’t map neatly to rare disease. They generate content based on probability, not on truth.
And this leads to a familiar problem: hallucination.
A 2023 Stanford study found that even state-of-the-art models hallucinated between 15–38% of the time in complex healthcare tasks.* That’s unacceptable in market access.
Imagine a consultant using a generic AI tool to speed up proposal writing. They ask for the most recent trials in ulcerative colitis comparing JAK inhibitors. The model returns three studies. One is real. One is outdated. One never happened. The names are right. The citations sound plausible. But the output can’t be trusted.
And now the consultant spends more time checking and correcting than they saved by using the tool in the first place.
"AI should accelerate our work, not create extra steps just to verify it didn’t make things up." - Senior Director, Market Access Consultancy
Grounding AI means limiting what it can talk about to only what you’ve told it is true. In technical terms, it means pairing a generative model with a curated retrieval system. Before the model generates a response, it pulls in specific documents, and is instructed to use only those for its output.
This is Retrieval-Augmented Generation (RAG).
And when implemented well, it transforms AI from a loose cannon into a powerful, focused partner.
But here's the thing most companies get wrong:
The retrieval layer is everything.
If you're feeding an LLM a mountain of unstructured, duplicative, and messy content, you're not grounding it. You're just giving it a bigger sandbox to hallucinate in.
We didn’t start with the model.
We started with the data.
Before AI was introduced, our team spent over a decade building the structured foundation that market access actually needs:
So when our AI works, it’s working inside a perfectly defined sandbox—one that’s shaped by how real strategy is delivered, not by what’s trending on PubMed.
“We don’t ask AI to guess. We give it only the evidence that matters, and make sure it knows what to do with it.”
LLMs generate language, not truth.
In market access, that means risk: hallucinations, oversimplifications, and misplaced confidence.
Grounding solves this by restricting AI to only verified, relevant data.
At Knowledgeable, we’ve built the structure so every output is accurate, traceable, and strategically useful.
The idea of Retrieval-Augmented Generation (RAG) has quickly become a leading solution to the most pressing limitation of large language models: they can’t be trusted to recall specific, factual, or current information on their own.
And in theory, RAG solves that.
But in practice? Not all RAG is created equal.
At its simplest, RAG adds a retrieval step before generation.
Instead of relying solely on what the model was trained on (which may be outdated, incomplete, or wrong), a RAG-enabled system first searches a curated dataset for relevant documents, extracts those documents, and then asks the AI to generate an answer based only on those results.
Think of it as giving AI a reading list before it opens its mouth.
This is a major improvement over vanilla LLMs, especially in fast-moving or high-risk domains like medicine, finance, or law. By anchoring responses in defined source material, RAG boosts relevance, accuracy, and (critically) traceability.
The problem is that most retrieval pipelines are built for scale, not sense. They’re designed to return content fast, but not necessarily the right content.
Here's what often happens:
So while this kind of RAG prevents some hallucination, it still allows for semantic drift, factual gaps, and loss of nuance. This is especially dangerous in a field as interconnected and evidence-sensitive as market access.
At Knowledgeable, we’ve taken RAG much further.
We’ve built Grounded RAG: a domain-specific, expert-informed implementation that operates like a strategic research assistant, not a search engine with a chatbot wrapper.
Traditional RAG: Pulls from raw or loosely structured data | Knowledgeable: Pulls from semantically structured, validated knowledge
Traditional RAG: Matches based on surface-level similarity | Knowledgeable: Matches based on concept, context, and strategic use case
Traditional RAG: No understanding of market access-specific logic | Knowledgeable: Fully mapped to market access frameworks, like TPPs and GVDs
Traditional RAG: Hallucination still possible if retrieval is vague | Knowledgeable: Output is strictly confined to retrieved, verifiable content
Traditional RAG: Little or no attribution | Knowledgeable: Embedded citations and lineage with every insight
We call it ringfencing the truth.
Most RAG systems focus on factual recall. What trials were published, when, and where. That’s a good start. But strategy demands more than just facts. It requires:
Our structured retrieval layer understands all of this, because it’s built on a market access-specific ontology that defines how evidence flows into strategy.
This means consultants get insight, not just information.
They get answers with context, not just summaries with citations.
Here’s one of the most underappreciated parts of grounded RAG:
Sometimes, the most strategic output is silence.
In a traditional LLM, if you ask a question and there’s no good answer, it will still try to respond. That’s what it’s trained to do. But in high-stakes workflows, guessing is worse than saying nothing at all.
Our system is different.
If the evidence doesn’t exist, or if the retrieval layer can’t support the generation with confidence and traceability, the AI simply doesn’t answer.
Or it tells you clearly what’s missing.
“The difference? With Knowledgeable, when the AI speaks, you can trust that it had something worth saying.”
RAG is a good starting point.
But without structured data, semantic logic, and clear retrieval guardrails, it’s just a smarter-sounding search engine.
Grounded RAG, done the Knowledgeable way, delivers strategic, citation-backed, and context-aware outputs that consultants can actually use.
Grounding AI in the right data transforms how work gets done.
For market access consultants, the value isn’t just that the AI is “correct.” It’s that it helps them operate at a higher strategic level, faster. It automates the grunt work, guides decisions with relevant evidence, and makes every output easier to defend, adapt, and reuse.
This section outlines how, when done right, Grounded RAG directly improves the three phases that matter most in consulting delivery: research, analysis, and reuse.
Traditionally, consultants spend hours sifting through PubMed, internal slides, and messy PDFs to find relevant studies, endpoints, or stakeholder commentary. With Grounded AI, this is transformed into a system that:
“Instead of 15 tabs open and Ctrl+F, I now get three sources, all relevant, all cited. I can actually start the thinking work right away.” - Consultant, Knowledgeable testing partner
And because all retrieved content is semantically tagged (e.g. study design, outcomes, indications, payer relevance), search becomes intent-aware, not keyword-dependent.
Even when data is found, the challenge is often understanding how it supports your strategic argument. Consultants spend countless hours manually building narratives: value messages, trial hierarchies, competitive positioning.
With a grounded, ontology-driven system, this changes:
All of this reduces the time and mental load required to move from data to decision.
And because every AI-generated insight is cited, consultants can move forward with confidence.
"We went from a stack of notes to a clear story in one hour. Every claim backed, every link traceable. That used to take three people a week."
In most consultancies, project work is siloed. Slides are stored, but insights are lost. Grounded AI changes this by structuring every output in a way that makes it searchable, sharable, and relevant for future work.
This is institutional memory that improves with time.
It’s how firms stop reinventing the wheel and start building compound strategic intelligence.
Task: Identify relevant literature | Traditional workflow: Manual search, high volume of irrelevant results | With Knowledgeable: Semantically targeted retrieval, high signal-to-noise
Task: Summarise trial evidence | Traditional workflow: Manual synthesis, risk of omission | With KNowledgebale: Context-aware AI summaries, tagged by strategic use
Task: Draft value message section | Traditional workflow: Consultant intuition + time-consuming collation | With KNowledgeable: AI-supported drafting, backed by citations and frameworks
Task: Build a proposal | Traditional workflow: From scratch, often partial visibility | With KNowledgeable: Reuse strategic logic and evidence from prior projects
Task: Collaborate across teams | Traditional workflow: Files, slides, and emails | With Knowledgeable: Shared live workspaces with embedded data context
With Grounded AI, consultants can elevate the quality of their thinking.
They stop drowning in data and start leading with insight.
They build trustable outputs faster.
And they turn every project into an asset that adds value to the next.
Most market access consultancies don’t have a data problem. They have a memory problem.
Strategic knowledge: trial insights, pricing logic, value propositions is created daily. But because it lives in scattered slides, unstructured PDFs, or someone’s inbox, it disappears once a project ends. This means every new brief starts from scratch. Every insight has to be rediscovered. Every deliverable rebuilt.
At Knowledgeable, we’ve flipped this paradigm.
We’ve designed a system where every project makes the next one faster, smarter, and more profitable.
This is the promise of reusable intelligence and it’s only possible when your AI is grounded in structured, attributable knowledge.
In a traditional setup, a consultant spends 20 hours crafting a GVD section. The rationale behind each value message is sound, but undocumented. The studies that support them? Buried in a slide. Next time a similar project comes along, that work is either forgotten, or re-done.
In Knowledgeable:
Both the raw data and the thinking behind it is saved.
Thanks to our ontology and semantic enrichment, the system doesn’t just remember “Study X supported Drug Y.” It remembers that:
That means a consultant working on a new asset in the same indication doesn’t have to guess where to start. The system surfaces the relevant thinking, backed by the original evidence trail.
This is how you build a living strategy engine.
Proposals, landscape reviews, stakeholder maps: these all follow familiar patterns. Yet most consultancies rebuild them each time, losing hours on manual search, formatting, and synthesis.
With grounded AI and structured knowledge:
The result? A consultancy that compounds knowledge, not labour.
“It’s like each project leaves breadcrumbs. And the next team just follows the trail to insight.” - Strategy Lead, EU Market Access
Reusable intelligence transforms how teams collaborate.
When every insight is structured, cited, and linked to strategic outcomes:
We hope that this unlock something more than just operational efficiency and transform cultures.
As quality doesn’t depend on one person’s memory.
It lives in the system, and it gets better every time it’s used.
Consultancies lose enormous value by treating deliverables as one-offs.
Grounded AI, powered by structured data, turns every project into a reusable, searchable knowledge asset.
The result? Less rework. Faster ramp-up. Stronger proposals.
And a team that gets smarter every time they work.
As healthcare data continues to grow in complexity and volume, the tools used to process, interpret, and act on that data must evolve in lockstep. Grounded AI - particularly in the form of domain-specific, ontology-driven RAG (Retrieval-Augmented Generation) - offers a powerful step forward. But its full potential is still unfolding.
At Knowledgeable, we believe the future of AI in market access lies not in novelty, but in deep alignment with real-world consulting workflows, strategic reasoning, and regulatory standards. In this final section, we outline where grounded AI is heading, and why it represents a foundational shift for the field.
Currently, most AI use in consulting is reactive. Users prompt the system with a question, and the model generates an answer from retrieved content. However, as usage increases and systems accumulate structured, semantically rich project data, we move toward context-aware proactivity.
This evolution enables the system to:
Rather than merely responding to a prompt, the system becomes a real-time decision support engine: recognising patterns across documents, proposals, and clinical data to inform next-best actions.
For example, in an early pilot, Knowledgeable flagged an emerging comparator mentioned in recent HTAs that was missing from a client's proposed TPP. This allowed the team to proactively revise their strategy before submission.
The next generation of RAG systems will go beyond grounding responses in a shared corpus of publications and internal documents. They will be contextually grounded at the project and client level, adapting to the nuances of prior work, ongoing deliverables, and specific client preferences.
Technically, this involves the intersection of:
This unlocks the ability to generate content (summaries, proposals, arguments) that not only reflect current evidence but do so in a way that aligns with a consultancy’s house style, a client’s decision-making framework, and previously used logic.
This personalisation enables consultancies to scale tailored, high-quality work without sacrificing rigour or voice consistency.
A core requirement of any market access solution is compliance. Unlike general-purpose knowledge tools, strategic consulting must withstand scrutiny; whether from internal reviewers, external clients, or regulatory bodies. To support this, Knowledgeable is moving toward an audit-by-design architecture, where every system action is traceable, transparent, and exportable.
Key features in development include:
These features align with growing regulatory expectations around AI explainability (e.g. EMA’s Good Machine Learning Practice principles) and the need for defensibility in reimbursement or HTA-facing work.
As the platform continues to evolve, its function shifts from a research assistant to a strategic operating system for evidence-based consulting. That is, a platform that does not simply improve isolated tasks but orchestrates the entire lifecycle of a market access project.
This includes:
Because of its ontology-first foundation, Knowledgeable can do this without losing flexibility. It adapts to each agency’s preferred methods and structures while ensuring consistency, traceability, and scalability.
The implications of this roadmap extend beyond feature development. They point to a fundamental shift in how knowledge work is delivered in high-complexity environments:
With data growing exponentially, only systems that can structure, interpret, and evolve with that data will create lasting value. Grounded AI, done right, is more than a tool, t’s a new layer of thinking.
The future of market access consulting will be defined not just by how fast teams can work, but by how intelligently they can scale expertise. That future demands systems that are proactive, personalised, auditable, and continuously improving. With our grounded, ontology-driven platform, Knowledgeable is building exactly that.
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