Immutable QC (IQC) is a high-integrity middleware layer that bridges the gap between physical analytical instruments and any digital ledger. We ensure your data is born compliant.
Your laboratory ecosystem (HPLC, Mass Spectrometers, pH meters, and environmental sensors) connects directly to an IQC instance. As measurements occur, IQC cryptographically signs and commits the data directly to the blockchain.
Source-to-Ledger Provenance: Data is captured and locked at the moment of creation.Human-Free Transcription: Eliminates manual entry and the risk of post-hoc data manipulation.Regulatory-Ready Audit Trails: Real-time, cryptographically verifiable documentation for 21 CFR Part 11 and ISO compliance.Trust Orchestration: A single, immutable source of truth for multi-site operations and external stakeholders.
Phase 1: Foundation (Current - Q2 2026)Source-to-Ledger MVP: Deliver an end-to-end pipeline where instrument/measurement events are captured at creation, cryptographically signed, hashed, and committed to a blockchain-backed audit trail (tamper-evident provenance).QC Data Primitives & “QC Packet” Schema: Define and implement the core data primitives for laboratory workflows (measurement metadata, device identity, timestamps, environmental conditions, operator/reviewer sign-off) with strict schemas designed for verification and audit.Evidence-Grounded AI Extraction (Guardrailed): Implement AI-assisted extraction/normalization from QC artifacts (logs, reports, exports) into the QC Packet schema, with validation rules and abstention behavior when evidence is insufficient.Primary Instrument Onboarding (Environmental Sensors): Establish direct-to-ledger connectivity for environmental sensors (pH, temperature, conductivity, turbidity, O3) with repeatable onboarding, calibration metadata capture, and verification checks.Reviewer Workflow & Attestations (Optional Identity Layer): Ship a minimal reviewer UX for QC review/approval and signed attestations, with an optional roadmap path to proof-of-personhood style identity for anti-sybil reviewer integrity.Phase 2: integration & Scale (Q4 2026)Enterprise LIMS/ELN Synchronicity: Ship production-grade connectors and bidirectional sync patterns so IQC can push verified results/provenance into lab systems and pull required context (sample IDs, methods, specs, approvals) from platforms like LabWare, Benchling, and Quartzy.IQC API Suite (Developer Platform): Release a stable API surface (REST + webhooks/eventing where appropriate) for ingesting measurement events, retrieving QC packets, verifying signatures/hashes, and exporting audit-ready reports.Expanded Instrument Coverage: Scale onboarding beyond initial sensors by supporting 50+ instrument models via validated adapters (file-based, direct, or API-driven ingestion), with calibration/maintenance metadata captured as part of the provenance trail.Multi-Site Orchestration & Governance: Deploy centralized management for distributed labs (tenant controls, role-based access, policy enforcement, key management, retention), while maintaining consistent “source-to-ledger” integrity across sites.AI-Assisted QC at Scale: Operationalize the AI pipeline from Phase 1 for high-throughput environments: automated exception triage, inconsistency detection across runs/sites, and reviewer workload reduction—backed by measurable extraction accuracy and abstention rules.Phase 3: Autonomous Evolution (H1 2027)Real-Time Edge Intelligence: Deploy autonomous edge agents that monitor instrument streams and environmental sensors in real time to flag anomalies, drift, calibration issues, and protocol deviations at the moment of analysis (not after results are finalized).Predictive Compliance & Risk Scoring: Move from periodic/post-audit detection to continuous compliance posture: automated risk scoring per instrument, method, site, and dataset, with early warnings when trends suggest an out-of-spec event or documentation gap is likely.Self-Healing Data Integrity: Automate verification and remediation workflows across the audit trail; e.g., detect missing signatures/metadata, trigger re-attestation or re-capture flows, and run periodic integrity checks so tampering or pipeline failures are caught quickly and can be corrected without breaking provenance.Privacy-Preserving Verification (Selective Disclosure): Enable third parties to verify integrity and reviewer attestations without exposing raw sensitive data, using cryptographic proofs/verification patterns appropriate for regulated environments.Closed-Loop Quality Improvement: Use aggregated, permissioned signals across sites to continuously improve anomaly models, reduce false positives, and codify “what good looks like” for QC; turning every verified run into training/evaluation data for better automation.