# Nodes > Nodes builds enterprise AI software led by Nodes Engine. Engine builds a company-owned context graph, uses its intelligence layer to investigate problems and its system of action to carry out approved work. Nodes Connector connects and tests integrations; Nodes AI FDE configures and tests workflows. Give Nodes a business goal, or authorize it to flag a relevant change in agreed internal or external sources. The company chooses sources and checking frequency. Current permissions govern what people and agents may see, recommend and change. Engine connects human judgment, decisions and outcomes with the evidence. Company knowledge and decision history stay available as models and agents change. Full blog corpus: https://www.nodes.inc/llms-full.txt Raw markdown for any article: https://www.nodes.inc/blog//index.md ## Overview - [Nodes](https://www.nodes.inc/): Nodes Engine turns siloed company data into decisions and action. Your company retains its context graph, business rules, Decision Traces and outcomes as models and agents change. - [Products](https://www.nodes.inc/products): Three working products, led by Nodes Engine with context, intelligence and action layers. Nodes Connector connects and verifies integrations. Nodes AI FDE configures, builds and tests the solution. Available together in the desktop app. - [How Nodes Engine works](https://www.nodes.inc/platform): Engine owns the context graph, intelligence and action layers. Connector links authorized systems, files, documents, conversations and external sources. AI FDE configures integrations, workflows and tests. Configured approvals and delegated authority govern work. - [Request a walkthrough](https://www.nodes.inc/contact): Explore a relevant example and a useful first scope. No company dataset is needed; historical Decision Replay is a separate testing path. - [AI Transformation](https://www.nodes.inc/ai-transformation): One accountable domain, company knowledge, operating economics and team adoption. Quality, exception and total-cost checks plus owner approval govern expansion. - [Pricing](https://www.nodes.inc/pricing): License Nodes in a customer VPC or on premises, or subscribe to Nodes Cloud. Service, configuration support and product upgrades are included. Pricing follows agreed workflow scope, with no seat or token meter. Infrastructure and third-party costs follow the agreement. - [Security](https://www.nodes.inc/security): Nodes Cloud, single-tenant customer VPC, and on-premises deployment boundaries, SOC 2 Type II attestation, and Decision Traces. HIPAA and GDPR alignment are separate from SOC 2 attestation and depend on the defined workflow and deployment. - [Evidence and Methodology](https://www.nodes.inc/evidence): Canonical definitions, study populations, time periods, validation methods, and limitations for public Nodes claims. - [Proof](https://www.nodes.inc/proof): Hiring, licensing progress and educational support at the same Fortune 500 insurance carrier. Historical hiring results retain their original populations, periods and limits. The 10,000+ hires are distinct from the 10,765-person research cohort. - [About Nodes](https://www.nodes.inc/company): The Nodes story, people, products, customer example, and working relationship. - [Buyers](https://www.nodes.inc/buyers): Business, finance, and technical guides to ongoing AI work, operating effort, financial evidence, human authority, and control boundaries. ## First decision brief - [First decision brief](https://www.nodes.inc/pilot): Paid analysis of one owned question or workflow. Setup typically takes 1-2 weeks; the 72-hour clock starts after complete agreed data access. - Within 72 hours of complete agreed data access, identify at least $1M in potential business value over an agreed measurement period, with findings, evidence gaps, compared options, estimated action and inaction costs, uncertainties and recommended next steps. Implementation is separately scoped and scheduled. - The established $125,000 fee, agreed $1M opportunity threshold, full finding-based refund and successful-engagement annual-license credit follow the written terms on /pilot. The finding is an estimated opportunity, not realized savings. - Agree the business, cost and team-effort baseline and comparison period. Missing evidence remains visible. No company data is needed for the first conversation. ## Decision Replay offer - One repeated high-stakes decision and at least two years of linked decisions and measured outcomes enter a data-sufficiency review. - The 72-hour clock starts only after Nodes accepts the agreed deidentified dataset delivered under a mutual NDA. - The customer finance team defines the $1M evidence bar and calculation in writing before the read-only Replay runs. - Nodes shows whether the historical record supports that bar. No live decision or production workflow changes. - If the written bar is not supported, the customer owes no production fee and keeps the analysis. ## Blog - [What is Jev? A simple guide to Jev AI](https://www.nodes.inc/blog/what-is-jev-ai): What is Jev AI? Learn how TypeSafe’s decision model works, what it costs, where it helps, and how to try it with a simple customer support example. - [What is a knowledge graph?](https://www.nodes.inc/blog/what-is-a-knowledge-graph): What a knowledge graph is, how it relates to ontologies and context graphs, and how decision traces connect evidence, judgment and later results. - [Blog](https://www.nodes.inc/blog): Field notes on enterprise decision systems, governed AI, architecture, and measurable outcomes. - [Workday Is the Friend Graph](https://www.nodes.inc/blog/workday-is-the-friend-graph): a16z argues that the System of Intelligence is becoming the new locus of enterprise software value. They used sales. The thesis also applies to talent. - [What is context engineering?](https://www.nodes.inc/blog/context-layer-is-the-moat): Context engineering selects the instructions, facts, tool results, and history AI needs for a task. Model quality, retrieval, and evidence quality still matter. - [Can AI compare the cost of acting versus doing nothing?](https://www.nodes.inc/blog/cost-of-inaction-is-the-hard-half): Compare acting, waiting, gathering evidence, and making no change over the same period. Show future costs and benefits as estimates until observed. - [Why six AI hiring vendors got rejected at the same carrier](https://www.nodes.inc/blog/six-vendors-rejected-architecture): One Fortune 500 insurance carrier rejected six AI hiring vendors on architecture. Its private deployment requirements are a reference, not a universal rule. - [How do I connect company data across different systems?](https://www.nodes.inc/blog/fast-integration-reads-as-risk): Connect company data for AI by testing field meaning, identity, permissions, action results, and schema changes. Scope the first workflow and keep conflicts visible. - [Vendor lock-in is an architecture decision](https://www.nodes.inc/blog/vendor-lock-in-is-architecture): Evaluate AI vendor exit by artifact: evidence, relationships, decisions, outcomes, tested capabilities, model rights, and the work needed to keep them usable. - [The human line in AI hiring is not a task list. It is an approval gate.](https://www.nodes.inc/blog/approval-gate-not-task-list): AI recruiting guides ask which tasks should stay human. The question that matters is narrower: who approves the action before it executes. - [From requisition to hire: what the 38-day result does and does not show](https://www.nodes.inc/blog/whole-loop-req-to-producing-hire): One carrier's requisition-to-hire time moved from 127 to 38 days. Keep that measure separate from contract-to-production ramp and modeled value. - [The agentic enterprise has an org chart problem](https://www.nodes.inc/blog/agentic-enterprise-org-chart): Every fleet of agents needs an org chart. The reporting line is not a headcount ratio. It is the approval gate, and most enterprises have not drawn it. - [A CHRO AI strategy is not a longer tool list](https://www.nodes.inc/blog/chro-ai-strategy-2026): AI became the CHRO's top priority in two 2026 surveys. What most CHROs fund under that heading is still a list of separate tools. - [Can a context graph work with RAG?](https://www.nodes.inc/blog/context-graph-vs-retrieval-pipeline): RAG can use vector search, graphs, time, and provenance. Compare the task and implementation before choosing how to connect evidence. - [DeepSeek keeps making the model a commodity](https://www.nodes.inc/blog/deepseek-v4-flash-open-weights): DeepSeek released V4-Flash-0731 with MIT open weights, self-reporting wins over its own flagship on nine agent benchmarks. The enterprise question is where it runs. - [The EU delayed the hard AI Act obligations to 2027. It did not delay the easy one.](https://www.nodes.inc/blog/eu-ai-act-human-oversight-test): The EU AI Act Digital Omnibus delay pushed high-risk hiring and insurance obligations to December 2027. The transparency duty still starts August 2026. - [Anthropic deleted 80% of its system prompt. Your governance cannot live there.](https://www.nodes.inc/blog/governance-is-not-a-system-prompt): Anthropic cut 80% of Claude Code's system prompt with no eval loss. AI guardrails in the system prompt are preferences, and preferences expire as models improve. - [The yield on a GPU is an approved workflow](https://www.nodes.inc/blog/gpu-yield-approved-workflow): Wall Street has targeted more than half a trillion dollars to finance AI compute as a revenue-generating asset. The yield arrives one approved workflow at a time. - [Retention is the enterprise hiring outcome AI still has to prove.](https://www.nodes.inc/blog/insurance-agent-retention-64-to-91): AI hiring and retention claims require termination dates, censoring rules, a credible comparator, and a trace from recommendation to employment outcome. - [Can AI learn from our past decisions and mistakes?](https://www.nodes.inc/blog/intelligence-compounds-data-stays): Company learning can change context and tested workflows without exporting customer records or pooling weights. Improvement still needs evidence and validation. - [Open weights are not owned weights](https://www.nodes.inc/blog/open-weights-are-not-owned-weights): Jensen Huang signed a letter calling open weights national infrastructure. For a data-sensitive enterprise, open weights and owned weights are two different purchases. - [Promised weights are not weights](https://www.nodes.inc/blog/promised-weights-are-not-weights): Qwen3.8-Max went live today as API-only, with open weights promised next week and no license named. Three tests separate an open-weights artifact from an announcement. - [The second signer: why regulated AI needs two humans on one decision](https://www.nodes.inc/blog/second-signer-regulated-ai): Every AI vendor says their system puts humans in the loop. A second signer is the mechanism that makes that claim auditable, queryable, and defensible in a regulated workflow. - [How do we test AI without letting it change anything?](https://www.nodes.inc/blog/shadow-evaluation-before-promotion): Test AI with historical replay, read-only analysis, sandbox actions, and live shadow evaluation. Keep candidate action routes disabled until review. - [You can't read the code. You still have to sign the contract.](https://www.nodes.inc/blog/sign-the-contract-without-reading-the-code): The senior buyer who can't audit model weights still has to defend the purchase. Three inspection surfaces that work without a technical background. - [One candidate, three systems, and the evidence to connect](https://www.nodes.inc/blog/talent-context-graph-example): An illustrative talent context graph connects candidate evidence, human judgment, and later outcomes. Actual source coverage and each use case require validation. - [The work between an AI answer and a business result](https://www.nodes.inc/blog/the-85-percent-already-built): Compare internal AI and Nodes on integration, approved execution, exception handling, and measured learning. Start with the same business responsibility. - [The confidence gap in insurance AI is an evidence gap](https://www.nodes.inc/blog/the-confidence-gap-is-an-evidence-gap): A 2026 insurance AI survey found adoption outrunning oversight. The gap the industry calls confidence is a missing evidence trail. - [The status quo has a price](https://www.nodes.inc/blog/the-status-quo-has-a-price): Price the current hiring workflow using your own ramp and production records. Reference-study estimates can inform a scenario, but cannot establish your savings. - [Evaluate native agents on the responsibility they can carry](https://www.nodes.inc/blog/waiting-for-native-agents-is-the-wrong-bet): Native agents are a real alternative. Test the same business responsibility across platforms and Nodes, including customer effort, exceptions, and later results. - [What is agentic AI?](https://www.nodes.inc/blog/what-agentic-should-mean-to-a-buyer): Agentic AI can choose steps and use tools toward a goal. It may start from a request or trigger; judge it by verified work and controlled authority. - [What is a context graph?](https://www.nodes.inc/blog/what-is-a-context-graph): A context graph connects authorized entities, evidence, judgments, and outcomes. Its usefulness depends on identity, source quality, permissions, and validation. - [What is governed outcome evidence?](https://www.nodes.inc/blog/what-is-a-performance-genome): Governed outcome evidence connects company context, judgments, and measured results. Explore the proposed model, validation limits, and customer ownership. - [What is an approval gate?](https://www.nodes.inc/blog/what-is-an-approval-gate): An approval gate blocks a designated AI action until the required reviewer approves its current plan. Routine steps can run within standing authority. - [Can AI learn how our best employees make decisions?](https://www.nodes.inc/blog/what-is-institutional-knowledge): Institutional knowledge includes evidence, human judgments, procedures, and outcomes. A useful company record preserves when a lesson applies and why. - [Who owns what AI learns about our company?](https://www.nodes.inc/blog/who-owns-your-agents-memory): Who owns AI agent memory depends on data-use terms, retention, and usable exports. Compare the separable company intelligence with the licensed runtime. - [Gartner's 2027 Warning: Why Binary Governance Kills AI Agents](https://www.nodes.inc/blog/gartner-2027-ai-agent-decommissioning-warning): Gartner predicts four in ten enterprises will decommission autonomous AI agents by 2027. The fix is proportional governance, where human approval gates replace binary trust. - [Local-first AI agents need a complete enterprise boundary](https://www.nodes.inc/blog/local-first-agents-and-the-enterprise-context-boundary): Local agents can reduce hosted inference use, but hardware, tools, permissions, and shared context still matter. Compare actual deployment boundaries. - [Agent-Native Code Hosting Accelerates Commits. Enterprises Still Need a Decision Layer.](https://www.nodes.inc/blog/agent-native-code-hosting-and-the-decision-layer): Cursor's Origin embeds Git into agent workflows, but repo speed is not business impact. Consequential production changes still require a cross-system decision layer. - [Tracking AI Spend to Jira Tickets Is Activity Accounting. True ROI Demands a Decision Ledger.](https://www.nodes.inc/blog/activity-attribution-vs-outcome-decision-ledger): Tempo maps AI token fees to Jira tickets. A decision ledger can connect that cost record to human judgment and later outcomes, without proving causality on its own. - [Autonomous Security Patching Breaks When the Agent Owns the Merge](https://www.nodes.inc/blog/autonomous-security-patching-and-the-human-gate): Agentic security patching needs a reviewable boundary between candidate fixes and production authority. Inspect the configured permissions, evidence, and recovery path. - [Autonomous Code Remediation Demands a Human Approval Gate](https://www.nodes.inc/blog/autonomous-code-remediation-human-approval-gate): Autonomous security patching accelerates fixes, but unreviewed production code writes create severe risk. Safe remediation demands a non-bypassable human approval gate. - [How do we measure the ROI of enterprise AI?](https://www.nodes.inc/blog/closing-the-agentic-ai-roi-gap-with-decision-ledgers): Measure enterprise AI ROI from attributable benefit and total cost over the same period. Keep forecasts, observed results, and causal claims separate. - [Salesforce's 2,000-Leader Study Exposes the Agentic AI ROI Bottleneck](https://www.nodes.inc/blog/salesforce-agentic-ai-report-and-the-roi-bottleneck): Agentic AI needs a testable business case. Connect proposed actions to evidence, configured approval controls, verified effects, and later measured outcomes. - [VMware Private AI: Test Containment and Business Authority Together](https://www.nodes.inc/blog/deny-by-default-agents-and-the-decision-gate): Broadcom pairs private agent containment with governed data and human controls. Evaluate network boundaries, permitted actions, decision evidence, and later outcomes together. - [Can AI help us decide whether to hire, train, or move someone internally?](https://www.nodes.inc/blog/workforce-transitions-demand-a-talent-context-graph): Compare hiring, training, and internal movement against one capacity gap, timeframe, cost basis, and evidence standard. Include employee preferences and uncertainty. - [What is a company brain?](https://www.nodes.inc/blog/company-brain): A company brain is shared, usable business context. Build one around a real decision with sources, definitions, permissions, tests, and later results. - [Can I give AI a business goal instead of step-by-step instructions?](https://www.nodes.inc/blog/ai-business-goal-to-workflow): Turn a business goal into a workflow by defining the result, investigating evidence, choosing steps, testing actions, and measuring the later outcome. - [How can AI speed up customer onboarding?](https://www.nodes.inc/blog/ai-customer-onboarding): AI can connect onboarding requirements, documents, and owners to reduce repeat requests. See an illustrative workflow and what to measure after rollout. - [What is decision intelligence?](https://www.nodes.inc/blog/decision-intelligence): Decision intelligence makes a business choice inspectable: define the problem and baseline, compare explanations and options, then measure the result. - [How do we lower AI costs without lowering quality?](https://www.nodes.inc/blog/lower-enterprise-ai-costs-without-losing-quality): Lower enterprise AI cost by measuring accepted results, testing fewer calls and smaller models, and including review, failures, maintenance, and latency. - [Can AI resolve customer issues across different departments?](https://www.nodes.inc/blog/ai-customer-issues-across-departments): AI can connect customer issues across departments using CRM, support, usage, and rollout evidence. Assign an owner and verify resolution. - [Can AI show whether employee training improves performance?](https://www.nodes.inc/blog/does-employee-training-improve-performance): Training completion, demonstrated skill, and job performance are different results. Here is how to check whether employee training helped. - [How can AI automate insurance back-office work?](https://www.nodes.inc/blog/ai-insurance-back-office): A practical way to select and test an insurance back-office AI workflow, with clear boundaries between published Nodes applications and illustrative examples. - [Can AI explain why sales teams are missing their targets?](https://www.nodes.inc/blog/ai-sales-target-diagnosis): AI can help investigate a sales target gap across CRM and operating records. Compare explanations and test the cause before blaming a person. ## Category - [How Nodes works](https://www.nodes.inc/platform): Connect internal information and external developments, compare approaches with local owners, implement approved work, and measure later outcomes. - [Use Cases](https://www.nodes.inc/use-cases): Put your knowledge graph to work in eight scoped applications: RevOps, Sales Ops, insurance licensing, employee development, hiring top performers, customer support, customer onboarding and market readiness. Compare estimated costs, approve work and retain results in decision traces. - [Platform comparisons](https://www.nodes.inc/comparisons): Compare a bounded responsibility against Palantir, Wonderful, Celonis, ServiceNow, UiPath or internal build on implementation effort, operating experience and retained intelligence, with sourced competitor descriptions. - [How to Choose AI Tools for Company Knowledge](https://www.nodes.inc/comparisons/company-knowledge): Choose by the job: answers, governed knowledge, decision history or approved action. Test permissions, freshness, correction, export and total effort. - [Glean Alternatives: Search, Agents and Approved Action](https://www.nodes.inc/comparisons/glean): Compare current Glean and Nodes configurations on the same answer, source correction, permission boundary, approved action and later outcome. - [Copilot Studio Alternatives for a Defined Workflow](https://www.nodes.inc/comparisons/microsoft-copilot-studio): Compare one workflow across Microsoft and outside systems, including identities, actions, support work and current licensing. - [Agentforce Alternatives Inside and Beyond Salesforce](https://www.nodes.inc/comparisons/salesforce-agentforce): Compare one account workflow across Salesforce and external systems, with the same permissions, actions, handoffs, costs and outcome check. - [Nodes vs Palantir AIP](https://www.nodes.inc/comparisons/palantir): Compare AI FDE, Evolve and the Nodes proposition through a scoped responsibility and measured implementation and operating effort. - [Nodes vs Wonderful](https://www.nodes.inc/comparisons/wonderful): Compare shared context, agent building, repeat deployment work and the evidence of useful reuse, without claiming component features are exclusive. - [Nodes vs Celonis](https://www.nodes.inc/comparisons/celonis): Evaluate process intelligence and orchestration alongside human judgment, interventions and delayed business outcomes. - [Nodes vs ServiceNow](https://www.nodes.inc/comparisons/servicenow): Compare the full AI Platform with a Nodes responsibility across existing systems, including required modules and operating ownership. - [Nodes vs UiPath Maestro](https://www.nodes.inc/comparisons/uipath): Compare investigation, business orchestration, recovery and evidence that a completed intervention helped. - [Nodes vs internal build](https://www.nodes.inc/comparisons/internal-build): Credit managed agent infrastructure and existing internal assets, then identify the integration, product and maintenance responsibilities a Nodes scope would need to replace. - [Hiring and HR comparisons](https://www.nodes.inc/comparisons/hiring): Detailed talent and HR service-delivery matrices, separate from the broader platform evaluation. - [Research comparisons](https://www.nodes.inc/compare): Compare hiring-research methods, outcome definitions, and evidence limits. - [Top-Performer Hiring](https://www.nodes.inc/top-performer-hiring): How a company can test hiring signals against its own post-hire outcomes. ## Tools - [Hiring value estimator](https://www.nodes.inc/tools/hiring-roi-calculator): Estimate potential gross hiring value using your inputs and scoped research assumptions. Investment costs, net ROI, and payback are excluded. ## Industries - [Solutions and workflows](https://www.nodes.inc/industries): Illustrative applications for completing requests, preparing for market changes, recovering renewals, and coordinating field service. Sources, actions, and measures are scoped for each engagement. - [Insurance workflows](https://www.nodes.inc/industries/insurance): Nodes supports hiring, licensing progress and personalized educational support at one Fortune 500 insurance carrier; historical hiring evidence retains its own scope. - [Financial-services workflows](https://www.nodes.inc/industries/financial-services): Three live insurer workflows provide the production reference; banking, wealth-management and other scenarios are examples requiring local validation. ## Security - [Security and Deployment](https://www.nodes.inc/security): Cloud, VPC, and on-premises deployment boundaries, access control, audit evidence, and SOC 2 Type II attestation. HIPAA and GDPR alignment are not additional certifications. - [VPC-Resident AI Hiring](https://www.nodes.inc/security/vpc-deployed-ai-hiring): Inspect the selected private deployment's model routes, connected tools, permissions and data boundary. Containment must be established for the actual configuration. ## Research - [Decision Traces](https://www.nodes.inc/research/decision-traces): Research method for linking hiring evidence, decisions, and measured outcomes. - [Resume Keywords and Performance](https://www.nodes.inc/research/keywords-vs-performance): Findings from 3,597 testable keywords within a 10,765-agent study cohort. - [The Cost of One Hiring Filter](https://www.nodes.inc/research/cost-of-one-hiring-filter): A retrospective estimate of $17.7M in annual production at risk across 2,863 producing agents at one carrier. - [Speed to Production](https://www.nodes.inc/research/speed-to-production): The reference carrier's measured relationship between days to the first production milestone and annual production. - [Data Fusion Accuracy](https://www.nodes.inc/research/data-fusion-accuracy): AUC results for individual and fused evidence sources, with interpretation limits. - [Institutional Knowledge](https://www.nodes.inc/research/institutional-knowledge): How Decision Traces preserve the context behind human decisions and outcomes. ## Glossary - [AI teams](https://www.nodes.inc/glossary/ai-teams): Specialists with defined responsibilities and permissions, coordinated around an agreed job or outcome. - [Shared context](https://www.nodes.inc/glossary/shared-context): Relevant company records, rules, decision history, and outcomes reused under permissions across AI teams. - [Workflow orchestration](https://www.nodes.inc/glossary/workflow-orchestration): Coordinating tasks, approvals, handoffs, and execution across connected business systems. - [Approval controls](https://www.nodes.inc/glossary/approval-controls): Customer policy determines which actions need approval and who may authorize them; existing work stays within configured permissions. - [Decision Traces](https://www.nodes.inc/glossary/decision-traces): Queryable records connect evidence, recommendations, human judgment, approved actions and later outcomes so teams can inspect business decisions. - [Outcome-Based Hiring](https://www.nodes.inc/glossary/outcome-based-hiring): Evaluating candidate evidence against a defined post-hire outcome. - [Talent Intelligence Infrastructure](https://www.nodes.inc/glossary/talent-intelligence-infrastructure): The data and decision layer that connects hiring systems with measured outcomes. ## Compare - [Outcome-Based Hiring vs Skills-Based Hiring](https://www.nodes.inc/compare/outcome-based-vs-skills-based-hiring): The difference between using a skills proxy and validating signals against an outcome. - [Predicting Production vs Retention](https://www.nodes.inc/compare/predict-production-vs-retention): Why production and retention are separate outcomes that require separate validation. ## For Buyers - [CHRO Guide](https://www.nodes.inc/buyers/chro): Talent quality, hiring-filter evidence, group-level review, and named human authority. - [CFO Guide](https://www.nodes.inc/buyers/cfo): Workflow economics, total-cost inputs, measured outcomes, and optional historical validation. Start with a product walkthrough. - [CISO and CTO Guide](https://www.nodes.inc/buyers/ciso): Nodes Cloud, customer VPC, and on-premises boundaries, configured permissions, policy-gated actions, and diligence materials. Request a security review.