Pricing a First-of-its-kind Drug Discovery Platform
The Challenge
An early-stage AI biotech company had built a generative AI co-pilot for biologics drug discovery – combining proprietary machine learning models with large-scale patent sequence mining to accelerate drug design.
The timing was critical.
With Series A funding approaching and M&A interest beginning to emerge, the leadership team needed to answer a key question.
What should we charge?
The challenge was structural. None of their direct competitors published their pricing and most operated through custom enterprise partnerships, milestone-based collaboration, or were still pre-revenue.
Internal pricing estimates existed, but they lacked any external validation. Before presenting to the board and any prospective investors, the CEO & CTO needed market-backed pricing anchored in real data, not assumptions.
What We Found
A typical competitive pricing analysis was not viable. Of the 15+ competitors identified, almost none disclosed list pricing. Revenue disclosures, where available, were bundled into collaboration agreements or structured deals that hid unit economics.
Due to this we had to pivot. Rather than searching for explicit price points, we built a benchmark from first principles.
We anchored our analysis on publicly listed AI and life sciences technology firms that disclosed customer revenue distributions within their annual reports. These provided detailed insights into revenue bands, average contract values, and concentration metrics.
The results were a benchmark grounded in real public market data – statistically robust and structurally defensible.
Our Recommendations
The work led to two key pricing adjustments.
Firstly, an increase to the price range of the Enterprise Tier as original pricing was conservative. Our analysis showed that comparable platforms frequently secured multi-module, multi-year enterprise agreements with significantly higher annual contract values that had first been assumed.
This repositioned the Enterprise Tier as a strategic investment for clients and not a tooling expense.
This marked a strategic shift, pricing not just for margin, but for market entry sequencing. The Professional Tier, not Enterprise, would likely drive the majority of revenue in their first 3-5 years. Beyond pricing, the analysis surfaced a broader commercial strategy
- Launch with strategic pilots
- Convert to structured SaaS contracts
- Later in proprietary IP modules
- Offer an optional consulting wrapper
- Expand via channel partnerships
- Progress toward selective IP licensing
This created a sequenced go-to-market roadmap aligned with both capital raising and long-term value creation.
The Result
The CEO & CTO entered board and investor discussions with:
- Externally validated pricing
- Statistically defensible benchmark
- A scenario-tested five-year revenue model
- A clear sequencing strategy for adoption and scale
What began as a pricing validation exercise became a commercially grounded growth strategy – built not on guesswork, but on structured market evidence.
