Labs & pharma · use case
AI that passes the validation step
“Our discovery team has a list of AI tools we would like to use. Our QA team has a list of reasons none of them will pass validation. Today those lists don’t overlap.”
Pharma and regulated labs sit at the intersection of the hardest AI-adoption problems: data is extraordinarily sensitive, the regulatory load is extraordinary, and the payoff from getting AI into the workflow is also extraordinary. The failure mode — a vendor tool that won’t pass the site’s validation step — is the default outcome.
Eldric AI OS was built with this audience in mind. The Science Worker ships the domain surface (140+ APIs, bioinformatics, CRISPR, docking, LIMS); the rest of the stack ships the compliance primitives (hash-chained audit, identity with four account types, single signed RPM as the validation artefact).
Value propositions
Scientific surface on day one
Sequence analysis, BLAST, variant calling, AlphaFold, molecular docking, ADMET, CRISPR guide design, off-target analysis, base / prime editing, LIMS. Shipped in the binary, not built by the customer.
21 CFR Part 11 primitives
Hash-chained audit log, identity service with four account types (human / system / service / device), tamper-evident privacy toggles. Electronic signatures as an API call.
GLP-ready LIMS
Sample tracking, experiment management, audit trails, regulatory-compliance templates. Audit trail captures sample + experiment + reviewer lineage.
Validation-friendly install
One RPM, one systemd unit, one process. Reproducible install. Signed with a 4096-bit RSA key, published with release notes. DQ / IQ / OQ documentation is tractable.
IP protection by construction
On-prem llama.cpp default. Confidential compound structures, patent drafts, and clinical data never reach a third party.
Cross-program memory
Matrix Memory scoped to the program / chemotype / tissue. Decade-long institutional recall survives scientist turnover.
AI-driven differentiator
The pharma AI market has two classes of tool: domain-specific (Benchling AI, Veeva AI) and general (consumer LLMs). The first is narrow; the second is non-validatable. Eldric is a third option — general-purpose but with the compliance primitives built into the kernel. 21 CFR Part 11 isn’t a feature to check off; it’s what the audit-log subsystem does by default.
Scalable use cases
- Early discovery. Target review, lit triage, CRISPR screens, in-silico docking in one chat. Matrix Memory records what didn’t work for the next program.
- Preclinical. LIMS + audit trail captures sample-to-experiment-to-data lineage. Chat answers GLP-ready questions about any past study.
- Clinical research. Per-study tenant, PHI-shaped
session.localposture. Grounded chat on protocol + CRF + prior-study archive. - Manufacturing + QA. SOP archive chat, batch-record review, deviation analysis. 21 CFR Part 11 audit log is the validation evidence.
- Regulatory submissions.
data.pageindexover FDA / EMA guidance documents. Draft responses cited to specific guidance sections.
Runs on commodity hardware
Eldric AI OS was built to land on small clusters, not on hyperscaler fleets. The whole stack is one binary; the on-prem LLM is embedded llama.cpp. The hardware plan that gets most organisations into production looks like this:
3× RTX 4090 — sweet spot
72 GB total VRAM with tensor-split. Llama 3.3 70B Q4 at 60–80 tok/s, a parallel 8B routing model, and an embedding server concurrently. One-time hardware cost ~€5–7k.
Single RTX 4090 / 4080 — team scale
24 GB. Llama 3.1 8B at 80+ tok/s, 13B comfortable, 32B Q4 possible. Enough for a small department chat with fan-out retrieval.
CPU-only — pilot scale
llama.cpp on 32+ core x86 runs 8B Q4 usefully. Matrix Memory is CPU-memory-bound. A refurbished server from the rack is enough to prove the architecture.
Scale up
Multi-node cluster with H100 / GH200 for research-grade workloads. Same binary, same role modules, topology-aware. See the HPC article.
Sandbox-to-production
One 4090 workstation as the discovery-team sandbox; 3×4090 for program-scale; scale to H100 as you approach IND-enabling. All three run the same binary.
The arithmetic: a €6k workstation displaces a €30–60k-per-year SaaS-AI contract that still leaks IP, still can’t reach your mainframe, and still has a “we may use your data for training” clause hiding somewhere.
What the disk bill looks like
| Artefact | Size | Notes |
|---|---|---|
eldric-aios-5.0.0-3.alpha3.fc43.x86_64.rpm | ~1.4 MB | CPU baseline binary; one RPM, one systemd unit. |
eldric-aios-cuda add-on | ~512 MB | Pulled in automatically via Supplements: cuda-drivers on GPU hosts. Contains GGML_CUDA llama.cpp. |
| Llama 3.1 8B Q4_K_M GGUF | ~4.9 GB | Good default for team-scale chat on a single 4090. |
| Llama 3.3 70B Q4_K_M GGUF | ~40 GB | The sweet spot for 3×4090 tensor-split. Holds a 16k context comfortably. |
| Mixtral 8x22B Q4 GGUF | ~80 GB | Tight on 3×4090; comfortable on 4×4090 or 2×H100. |
| nomic-embed-text (embedding) | ~700 MB | CPU or GPU. One per cluster; handles vector indexing. |
Matrix Memory .emm per domain | 50–500 MB | Depends on rank × dim (see memory article). chat 64/768 ~200 kB; particle_physics 512/1024 ~500 MB. |
| Vector store per 1M chunks | ~6–10 GB | Depends on embedding dim. SQLite backend; FAISS optional. |
| Hash-chained audit log | ~200 MB / 1M calls | JSONL, append-only, rotation at 500 MB files by default. |
Three reference hardware setups
| Pilot / team | Department / BU | Production / enterprise | |
|---|---|---|---|
| CPU | 1× EPYC 7313 (16c) or i9-14900K | 2× EPYC 9354 (32c each) | 2× EPYC 9654 (96c) per node |
| GPU | 1× RTX 4090 (24 GB) | 3× RTX 4090 (72 GB) | 4× H100 (320 GB) or 8× H200 |
| RAM | 128 GB DDR5 | 256 GB DDR5 ECC | 1 TB DDR5 ECC per node |
| Storage | 2× 4 TB NVMe (RAID-1) | 6× 8 TB NVMe (RAID-10) + SSD cache | Tiered: NVMe hot + TB-scale HDD / Lustre |
| Network | 1 GbE OK | 10 GbE with link agg | 25/100 GbE or IB-HDR for multi-node |
| Power | ~1 kW typical / 1.5 kW peak | ~2 kW typical / 3 kW peak | 4–6 kW per node |
| Hardware cost | ~€4–5k | ~€12–15k | €80–250k per node |
| Serves | 8B model, 10–30 concurrent chat users | 70B Q4 at 60–80 tok/s, 200–500 users | Mixtral / Llama-405B, 2k+ users per node |
Network + ops footprint
- Ports. One outward port (443 at the edge). Internally: controller on 8880, data on 8892, inference on 8883, science on 8897, etc. — all behind the edge.
- Storage layout.
${ELDRIC_DATA_DIR}defaults to/data/eldricif writable, else/var/lib/eldric. Subdirs:models/,vectors/,memory/(matrix memory),storage/(file storage),agent/,edge/, and per-module dirs. - Backup. The audit log and
.emmfiles are the two artefacts that matter. Everything else regenerates. Snapshot the data dir nightly; off-site every week. - Updates.
dnf upgrade eldric-aios. Rollback isdnf downgrade. Zero vendor dance. - Ops team. A single systems engineer can run a pilot install. A team of two runs a department deployment. Production enterprise uses your existing Linux sysadmin rota.
SWOT — an honest read
Strengths
- Science Worker + compliance primitives in the same kernel — no integration project
- Hash-chained audit log + identity service real in alpha.3
- Single signed RPM — DQ/IQ/OQ documentation tractable
- On-prem by default — zero IP / PHI egress surface to argue about
Weaknesses
- AlphaFold integration gated to Enterprise tier
- Not certified for 21 CFR Part 11 yet — primitives shipped, formal audit in preparation
- Domain-specific vendor tools (Veeva, Benchling, LabVantage) have deeper pharma-specific workflows
- Proprietary ML models (AlphaFold3, Boltz-1) need the user’s own license / API access
Opportunities
- FDA AI Guidance (2024+) pushing reconstructable-decision AI
- EMA Big Data Steering Group framework
- R&D budget pressure — “two years to integrate vendor AI” is no longer tolerable
- CRO-side AI offerings creating enterprise demand for sovereignty
Threats
- Benchling AI / Veeva AI embedded in existing contracts
- ELN / LIMS vendor-bundled AI (LabVantage, STARLIMS)
- CRO hyperscaler AI (AWS HealthOmics, Azure Health Data Services)
- Internal data-science team consuming AI budget before platform consideration
First entry points — concrete value in 30 / 90 / 180 days
Discovery sandbox
Install on a single GPU workstation inside the firewall. One chemist + one bioinformatician onboarded. Demo CRISPR design + docking cascade.
Program-level deployment
Tenant = discovery program. LIMS connected. Matrix Memory seeded with prior-program results. Audit log reviewed by QA.
Regulatory-ready
Preclinical program tracked end-to-end. 21 CFR Part 11 evidence package generated. IND-enabling readiness documented.