High-level system
From fragmented information to governed business action.
The LLM does not replace business systems. It sits above them as a decision layer that assembles context, asks tools for evidence, applies controls, and produces a recommendation or workflow action that people can inspect.
Inputs
Warehouse and marts
Semantic metrics
Docs and policy
ML and risk engines
Excel and files
Events and APIs
DecisionOS
LLM decision layerContext, tools, policies, rules, approvals, audit, and feedback
Outputs
Recommend
Explain
Route
Approve
Escalate
Monitor
Connected information
Each data source contributes a different kind of evidence.
The system is strongest when it can combine structured facts, business definitions, operational context, documents, analyst files, model outputs, and real-time signals.
Warehouses and data marts
- Curated fact and dimension tables
- Customer, citizen, account, asset, and case marts
- Historical trends, segments, scores, and operational KPIs
Semantic layer
- Business-approved metric definitions
- Semantic indexes for entities, measures, and dimensions
- Common language between analysts, applications, and LLM workflows
Operational systems
- CRM, ERP, case management, service desk, and workflow tools
- Ownership, status, priority, SLA, and next-step context
- Action handoff back into the systems teams already use
Documents and policies
- Policy manuals, SOPs, contracts, guidance notes, and playbooks
- Retrieved citations with source, version, and confidence metadata
- Conflict detection when guidance is incomplete or inconsistent
Files and spreadsheets
- Excel workbooks, CSV extracts, analyst uploads, and planning sheets
- Schema inference, validation, row-level checks, and mapping suggestions
- Temporary decision context without turning every file into a system
Models and risk engines
- ML scores, optimization outputs, forecasts, rules, and risk ratings
- Model version, confidence, calibration, and monitoring metadata
- Scores inform decisions while controls decide what action is allowed
SQL and analyst questions
- Approved SQL snippets, natural language questions, and governed query templates
- Read-only guardrails for table access, PII, limits, and tenant scope
- Query results become evidence, not unreviewed automation
External APIs and events
- Market data, payment status, risk feeds, case events, and partner APIs
- Freshness checks, provenance, and degraded-state handling
- Event-driven triggers for time-sensitive decision workflows
What the layer decides
Support the moments where teams ask, "What should we do now?"
Recommend the next best action
Choose an action such as approve, reject, escalate, prioritize, investigate, offer, route, notify, or defer.
Explain the reason
Translate metrics, policies, model signals, and case history into a readable rationale for operators and leaders.
Detect missing evidence
Ask for missing documents, incomplete fields, stale data, conflicting rules, or weak confidence before action.
Apply controls
Use deterministic thresholds, policy gates, risk bands, role permissions, and human review for sensitive outcomes.
Generate decision packets
Package facts, citations, calculations, recommended action, approval state, and trace metadata for review.
Learn from outcomes
Capture what happened after the decision so teams can improve rules, models, policies, and operations.
LLM OS capabilities
A tool-using decision layer, not just a chat box.
Different teams have different stacks. The reusable idea is a modular decision operating layer that can connect to the tools and data sources a team already owns.
Context builder
Assembles a decision-ready packet from tables, documents, SQL results, model scores, and workflow state.
entity profilerecent eventsretrieved policiesrisk and value signals
SQL decision tool
Runs approved read-only SQL or guarded generated SQL to answer decision questions with traceable results.
eligibility checkstrend queriessegment comparisonexception analysis
Excel and file intake
Turns spreadsheets and CSVs into temporary evidence with schema checks, mapping suggestions, and validation.
planning workbookcase uploadmanual override sheetad hoc batch review
Policy retrieval
Finds relevant guidance, shows citations, and highlights conflicting or stale policy context.
SOP lookupeligibility rulerisk guidanceexception path
Decision rules
Keeps thresholds, approvals, risk flags, and hard constraints deterministic instead of leaving them to text.
approval gaterisk capmanual review triggerrole check
Workflow actions
Turns accepted recommendations into tasks, notifications, approvals, escalations, or case handoffs.
task creationapproval routingcase handoffnotification draft
Operator console
Gives teams a business-facing workspace for decisions, evidence, review queues, outcomes, and monitoring.
decision reviewapproval queueoutcome captureevidence health
Monitoring and feedback
Tracks decision volume, stale evidence, exception rates, model drift, adoption, and downstream outcomes.
quality checksdrift signaloutcome loopadoption view
Decision examples
The output is a recommendation with rationale and guardrails.
Should this case be escalated?
Escalate to specialist review because policy sensitivity, customer impact, and SLA risk are high.
Which action should the team take next?
Prioritize outreach, attach supporting evidence, and request missing verification before approval.
Can this be approved automatically?
No. Required evidence is stale and the risk score exceeds the automatic-decision threshold.
Scenario lens
One decision layer, many sector-specific operating patterns.
The same core pattern changes by sector: evidence, tools, controls, and recommended actions shift with the operating context.
Evidence inputs
- account history
- risk score
- policy citation
- case notes
- recent events
Tools used
- SQL eligibility check
- policy retrieval
- risk threshold rules
Controls
- PII scope
- approval level
- manual review trigger
Route to specialist review with the evidence packet, risk drivers, and recommended next action.
Sector use cases
The same pattern applies anywhere decisions need evidence and controls.
The product story should stay broad: the system is not tied to one domain. It is a decision support pattern for regulated, operational, or high-volume environments.
Banking and financial services
- Credit exception routing
- Fraud investigation prioritization
- Collections hardship decisions
- Relationship manager next-best action
Government and public sector
- Benefits eligibility support
- Permit and case triage
- Compliance investigation routing
- Policy-aware citizen service guidance
Healthcare and life sciences
- Care coordination support
- Prior authorization review
- Claims exception analysis
- Operational risk and capacity decisions
Insurance
- Claims severity triage
- Underwriting exception review
- Renewal risk recommendations
- Fraud and recovery workflows
Manufacturing and supply chain
- Supplier risk decisions
- Maintenance prioritization
- Inventory exception handling
- Quality incident escalation
Education and workforce
- Student support prioritization
- Grant and scholarship review
- Workforce planning recommendations
- Policy-guided service routing
Trust and governance
LLM reasoning is useful, but the decision must stay controlled.
The system should make decisions easier to understand and operate. Sensitive recommendations need facts, citations, thresholds, approvals, monitoring, and human accountability.
- Deterministic decision fields are separate from generated explanation text.
- Every decision can carry facts, model signals, policy citations, approval state, and trace metadata.
- High-risk recommendations can route to human review instead of automatic action.
- Outputs are designed to be auditable, replayable, and measurable against downstream outcomes.
Human-in-the-loop
The system recommends and explains; a person approves, rejects, edits, or escalates.
Assisted automation
Low-risk actions can proceed when data quality, policy, confidence, and approval rules are satisfied.
Executive intelligence
Leaders see decision volume, bottlenecks, confidence, risk, outcome quality, and adoption.
Reusable framework
A decision-support concept that adapts to the team's real stack.
The value is not one fixed implementation. The value is the architecture: connect evidence, reason over context, apply controls, support action, and learn from outcomes.