The science is difficult enough. Research, quality and operations become slower when teams repeatedly reconstruct context from disconnected systems, documents, experiments and expert knowledge.
Literature, experiments, reports and technical records remain separated across teams and systems.
Methods, failed paths, assumptions and interpretation disappear between projects and handoffs.
Operational findings reach research and development after the most useful learning window.
Scientists and reviewers repeatedly reconstruct what supports a conclusion before they can advance it.
Useful pilots struggle to scale without shared context, governance and traceable evidence.
Facthory relates research, development, quality, operations and expert decisions instead of leaving every function with a different view of the evidence.
Connect hypotheses, assumptions, constraints, objectives and unresolved questions.
Connect external evidence, prior art, standards and relevant technical research.
Connect methods, conditions, observations, datasets, results and scientific interpretation.
Connect process evidence, issues, investigations, approved procedures and operational outcomes.
Connect requirements, controlled procedures, guidance and approved interpretations to operational context.
Preserve review, rationale, authority, uncertainty and validated scientific conclusions.
Focus on workflows where research quality, development speed, operational reliability and institutional knowledge depend on the same evidence.
Structure scientific investigations across literature, experiments, hypotheses, data and expert review.
Compare technical alternatives, requirements and development trade-offs with evidence preserved.
Connect quality issues, operational evidence and expert investigation around recurring reliability problems.
Keep validated procedures governed, searchable and connected to the work they control.
Investigate scientific, quality and operational questions across connected enterprise data.
Identify critical scientific and operational knowledge concentrated in individuals or fragmented evidence.
Facthory treats scientific work as a durable investigation, not a sequence of disconnected searches and chat sessions.
Turn broad research goals into assumptions, constraints, hypotheses and decision criteria.
Question
Assumptions
Constraints
Criteria
Facthory combines external research with internal experimental context and analytical tools, while keeping claims connected to the evidence behind them.
Find, compare and synthesize scientific publications around a defined research question.
Preserve methods, conditions, observations, results and expert interpretation around experiments.
Investigate patents, claims and prior art alongside internal research context.
Compare explanations and identify evidence required to discriminate among them.
Use calculations, code and analytical tools when scientific questions require more than text.
Preserve validated findings, failed paths and scientific rationale for future investigations.
Important scientific questions unfold over weeks or months. Facthory keeps evidence, agent work, expert input and conclusions persistent instead of resetting context every session.
Keep evidence, hypotheses, tasks and open questions intact across long research cycles.
Parallelize literature, evidence, data and technical investigation while preserving one shared context.
Turn validated conclusions and corrections into reusable organizational research memory.
Research and operations should not become separate knowledge worlds. Facthory connects quality evidence, procedures, operational context and expert review so validated lessons can move across the organization.
Connect issues to process evidence, history, documents and expert interpretation.
Keep approved procedures connected to operational evidence and responsible review.
Bring operational records and observations into the context of quality and technical decisions.
Keep consequential scientific and quality interpretations under qualified human authority.
Facthory is designed to reduce repeated evidence work and carry validated learning from research into quality, operations and the next investigation.
Reduce repeated searching and accelerate evidence-backed scientific investigations.
Connect investigations to the evidence, history and procedures behind them.
Move validated technical and quality knowledge closer to the work that needs it.
Turn reviewed findings, corrections and failed paths into organizational memory.
Regulated life sciences work requires controls appropriate to the intended use. Facthory supports provenance, human review, permissions, evaluation and lifecycle oversight around AI-assisted work.
Set the workflow, evidence boundary, responsible roles and acceptable use before execution.
Use case
Evidence
Roles
Boundaries
Sensitive research, quality and development data may require tighter control than ordinary SaaS. Facthory supports deployment models that preserve enterprise control over data, identity, infrastructure and models.
Use a dedicated managed environment with enterprise identity and governance.
Deploy inside customer-controlled cloud infrastructure, networking and regional boundaries.
Isolate critical workloads with dedicated infrastructure and controlled model access.
Keep execution local for restrictive research, laboratory or operational environments.
Choose a workflow where repeated evidence assembly, research time, quality investigation or expert dependency already creates a measurable baseline.
Start with a recurring research question where literature, internal evidence and expert reasoning are repeatedly reconstructed.
Start where recurring issues require teams to rebuild process history, evidence, procedures and expert context.
How Facthory turns probabilistic agent reasoning into governed industrial execution through deterministic controls, durable workflows, evidence, human authority and verification.
How Facthory turns reviewed work into versioned, scoped agent skills using tenant isolation, held-out evaluation, human publication, canary releases, rollback and independent runtime authorization.
How Facthory is being designed as a sovereign, resilient enterprise AI platform with control-plane/data-plane separation, BYOC, private deployment, zonal resilience, governed agents, event-driven execution and production-grade release engineering.
Experts can inspect evidence, correct assumptions, redirect agents and approve consequential findings, procedures or actions. Facthory preserves those reviews as part of the operational record.
Depending on the use case, responses and agent actions can include:
Facthory can also restrict agents from acting when evidence is insufficient, when required information is missing, or when an authorized person must approve the result.
Learn about Human Oversight and Approvals or schedule a governance demonstration.
A deployment may use supported commercial models, privately hosted models, open-weight models, or a controlled combination of models for different tasks. For example, one model may handle high-volume extraction while another is reserved for complex engineering or scientific reasoning.
Model selection can be governed by:
Discuss your preferred model and hosting strategy through an AI architecture consultation.
Depending on the deployed use case and associated risk classification, these controls may include:
The EU AI Act applies differently depending on the system, intended purpose, users, and risk level. Facthory does not replace legal assessment, but it can provide the governance infrastructure required to manage enterprise AI systems more systematically.
Read about AI Governance and Compliance or request an EU AI Act workshop.
Organizations can maintain common enterprise domains while allowing site- or department-specific knowledge and practices.
Connect scientific evidence, expert decisions, quality learning and operational context so each investigation starts with more knowledge than the last.
