Capture map
What context must be recorded at the source: controls, lots, timing, assay conditions, failed runs, and interpretation rationale.
Wet lab data substrate
Make lab evidence reusable before asking AI to reason over it.
A life sciences data substrate is the record layer that lets future scientists, analysts, models, agents, reviewers, and executives understand what happened, why it happened, what changed, and whether the result can be trusted.
SignalForge helps teams design the capture, metadata, repository, versioning, QC, permission, and review structure needed before private RAG, agents, modeling, dashboards, or automated decision support can be trusted.
The substrate failure pattern
The lab produces evidence faster than the organization captures context.
- Instrument files move into folders.
- Spreadsheets become semi official records.
- Controls and failed runs are summarized inconsistently.
- Pipeline parameters live in scripts or memory.
- Slides preserve the conclusion but not the evidence chain.
- Decisions are made from polished outputs whose provenance is hard to reconstruct.
That is not an AI problem yet. It is a memory problem. Until the company record is usable, every downstream model or agent inherits the missing context.
What SignalForge helps design
The record layer underneath trustworthy AI.
Metadata spine
The identifiers, fields, source links, and review states that connect experiments, files, compute, and decisions.
Repository blueprint
How instrument output, object storage, database records, versioning, permissions, and processing state should fit together.
QC and review gates
Where evidence becomes reusable, caveated, excluded, escalated, or decision ready.
Substrate first conversation
Bring the records, not the AI wish list.
The most useful first message names the evidence sources you already have, instruments, ELN, LIMS, object storage, spreadsheets, vendor reports, and the decision they are eventually supposed to support. SignalForge will identify which parts of that substrate are reusable today, which need repair, and which downstream AI ideas should wait.