We help biopharma executives identify the few AI and accelerated-compute use cases with line of sight to ROI — and execute against them. Built by operators who’ve shipped the platforms most consultants are reselling.
Three failure modes we repeatedly encounter inside life science organizations. Failure to address them results in poor outcomes.
Sharp tools beat broad capability decks. We anchor every engagement on three pieces of IP — one identity, one diagnostic, one prescriptive.
The fusion of pharma judgment with builder-grade fluency AI and accelerated-computing. We focus your team on the business objective. We teach your team how to reason about the technology in context of your problem. We keep your focus on whether AI can achieve your outcome.
Five tests every agentic AI program in pharma should pass before another dollar is committed. Apply them once and you can predict whether the second deployment will cost cents or eight figures.
A four-step process that produces a real AI strategy — not a wish list, not FOMO. Measure, fix the process, do the arithmetic, then deploy compute against what survives.
A major pharma paid high eight figures for an AI agent to automate one manufacturing plant. Because the solution didn't scale, the vendor quoted similar sums for each subsequent plant. The agent failed all five tests below. With them in place, all subsequent deployments would have cost cents on the dollar.
A common interface over messy specifics. Semiconductors solved this with the Process Design Kit. Pharma has no equivalent — yet.
Reusable pieces — scheduler, deviation investigator, compliance checker — swappable without rewriting the whole system.
Each task carries its own context, finishes cleanly, and is validatable as a unit. No hidden memory across runs.
Adding the second instance approaches zero marginal cost. When the vendor quotes another eight figures, the price tells you it doesn’t scale.
Every action captured, timestamped, traceable at scale, existential.
The "Global System Integrator" model — wrap the Anthropic, OpenAI and Nvidia stacks in a custom interface, bill the implementation, repeat — is being eaten from both ends. Pharma can perform the same integration work internally. The frontier labs themselves are deploying enterprise consultants directly.
We spent the last decade at the highest levels of both sides — building the GenAI and accelerated-compute platforms big consulting now resells, and running AI strategy at a top-3 pharma where we managed the projects others delivered. We’ve rescued several. We know what those firms get right, what they get wrong, and how to translate decks into delivery.
No platform. No licensing kickbacks. No incentive to recommend the partner closest to closing their quarter. Our recommendations are vendor-agnostic across foundation-model labs, accelerated-compute providers, scientific ISVs, and enterprise AI orchestration. Our judgment is the product.
Each engagement names the failure mode it addresses and the deliverable it produces. No capability buckets. No retainer mysteries.
AI strategies that are wish lists, FOMO shotguns, or top-down decks disconnected from organizational reality.
The HyIQ Strategy Method™ applied end-to-end to a target business problem. Output: a power-law–prioritized initiative map plus the bottleneck inventory plus the arithmetic showing where automation can and can’t deliver.
Bespoke agent whose functionality doesn’t scale: per-document, per-region, per-sponsor customizations that never consolidate.
The Five Hallmarks™ applied to a live or planned agentic program. Output: pass/fail scorecard on each hallmark plus a remediation roadmap with cost-to-replicate projections.
Multi-million-dollar compute procurements that miss cost-effectiveness by 2× and run applications 2× slower than possible.
Workload-mix analysis (GenAI vs. Agentic AI, molecular dynamics, genomics, structure determination, training, inference) plus right-sized infrastructure recommendation grounded in current architecture realities — Nvidia Blackwell, GH200, MIG/MPS, disaggregated inference.
Mission-critical AI programs with multiple years and tens of millions sunk and zero productivity gain. Sponsoring CxO and vendor both facing fallout.
Amdahl’s-Law reframing of the business problem. Identification of the true (typically downstream) bottleneck. Targeted restructuring plus selective automation. Output: re-baselined program plan and decision artifact.
Business units that can’t translate priorities to frontier labs. Frontier labs lacking the pharma domain to meet you where you are.
Ongoing technical translation between client business units and frontier providers (Anthropic, OpenAI, Nvidia, Google, AWS, AMD, scientific ISVs). Roadmaps, partnership terms, technical brokerage on bespoke agentic workflows.
AI-pharma diligence performed by people who’ve read the white papers but haven’t built the platforms.
For PE, VC, and corporate development teams. Technical assessment of AI-pharma platform investments, pharma-tech integration plays, or compute-stack-dependent commercial pipelines. Investment-grade technical opinion.
The full versions are available under NDA. The pattern is what matters: business problems reframed, bottlenecks identified, technology applied where the arithmetic justifies it.
"Lack of AI automation" was the wrong framing. The bottleneck sat downstream of the computational stage. Reframed via Amdahl’s Law, the fix was a major team restructuring plus targeted automation of the steps that actually constrained throughput.
A cryo-EM workflow procurement was based on vendor specs, not workload analysis. We performed a workload-to-infrastructure audit and corrected the configuration before commit.
Outdated hardware mapping and unoptimized vendor code on the active-learning docking pipeline. We led a technical partnership with the ISV to optimize on current-generation accelerated compute.
Long-form thinking on the structural failures we keep watching pharma make — and the playbook that fixes them.
Why pharma fails to wrap its head around advanced computation — and a stakeholder-by-stakeholder breakdown of where the disconnect lives, from C-suite to ISV. The full HyIQ thesis.
Read the essayExecutives are spending eight figures per plant on agents that don’t replicate. The problem isn’t the technology — it’s the diagnosis. Introducing the Five Hallmarks of Scalable Software.
Read the essayTell us the problem you’re trying to solve. We’ll tell you whether AI is part of the answer — and where, in your organization, the answer actually lives.
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