Assay record
Setup, readout, controls, timing, operator notes, lot information, ambiguous outcomes, failed runs, and interpretation rationale.
SignalForge Advisors
Life-sciences AI/data advisory for decision-ready R&D workflows.
Wet-Lab Data Substrate
Turn lab output into institutional memory.
Every assay, instrument run, sequencing batch, model output, operator note, failed experiment, and business decision is part of the evidence system. Most organizations capture only a fraction of it.
SignalForge helps life-sciences teams design the documentation, repository, versioning, and QC structure required before private AI, RAG, or persistent agents can be trusted.
The missing record
The lab produces context that future systems cannot infer.
Wet-lab data is not just a value in a spreadsheet. It is the product of protocol choices, timing, instrument behavior, operator judgment, reagent lots, controls, cell state, biological noise, ambiguous reads, and negative results that may never be written down.
When that context is absent, downstream analytics may look polished while the underlying evidence is weak. The substrate work is the discipline of preserving enough context that a future scientist, model, reviewer, or executive can understand what the result actually means.
What gets captured
A usable evidence layer joins raw output with scientific context.
Instrument feed
Source files, run metadata, timestamps, device configuration, sample identifiers, transfer status, and ingestion logs.
Omics layer
NGS, single-cell, genomics, transcriptomics, reference builds, sample provenance, pipeline versions, and output tables.
Computational trail
Scripts, environments, parameters, feature construction, model versions, object storage, database records, and reproducible handoff notes.
Scientific interpretation
Hypotheses, priors, exclusion logic, biological constraints, uncertainty, failure modes, and follow-up experiments.
Business context
Portfolio priorities, market signals, proposal history, budget assumptions, vendor dependencies, and decisions made from the evidence.
Build logic
From scattered lab output to AI-ready infrastructure.
Inventory the sources
Identify instruments, ELNs, LIMS, file shares, notebooks, scripts, databases, reports, and human-only knowledge.
Define the metadata spine
Choose the sample, experiment, run, operator, assay, version, and decision fields that make records joinable.
Design the repository
Separate raw object storage, structured tables, documents, permissions, lineage, and retrieval-ready indexes.
Install quality gates
Log ingestion, flag missing context, reconcile identifiers, track transformations, and surface exceptions before analysis.
Match cadence to throughput
Confirm analytics can keep pace with data production so bottlenecks are visible before they become chronic.
Prepare AI access
Determine which records are safe for RAG, fine-tuning, agents, dashboards, or human-only review.
Persistent agents
High-level AI acceleration starts after the company record is usable.
Once wet-lab evidence, computational outputs, omics, metadata, documents, and business context are organized, a private research or business development agent can work against company ground truth. It can cross-reference internal studies, watch for strategic openings, draft proposal angles, surface cost risks, and support budget allocation with a traceable evidence base.
BeforePeople hunt through folders, slides, inboxes, ad hoc scripts, and memory.
AfterTeams query a documented evidence layer with provenance, versioning, and review discipline.
SignalForge deliverables
Concrete outputs, not architecture theater.
Capture mapWhat is recorded today, what is missing, and which sources should be connected first.
Metadata schema briefRecommended fields and identifiers that make experiments, samples, files, analyses, and decisions joinable.
Repository blueprintPractical storage, database, documentation, permission, and retrieval design for the intended workflow.
AI readiness pathWhat can support RAG, agents, modeling, or decision support now, and what must be repaired first.