Specialized Inference for
High-Consequence Domains
Domain-bound decision support engineered for governance, auditability, and safety-first operations. Not a chat assistant — a decision engine.
What SolaceSentry Is (and Isn't)
We are not building a better chatbot. We are building a reliable inference engine for operations that cannot afford to be wrong.
General Chat Assistant
- Optimizes for conversational plausibility
- Can hallucinate confident but wrong answers
- Opaque decision reasoning
- Unbounded outputs difficult to audit
Specialized Inference Platform
- Optimizes for defensibility & correctness
- Violation-triggered inference refusal
- Evidence-gated outputs
- Structured decision records for audit
Core Capabilities
Eight engineering disciplines that separate a decision engine from a language model.
Violation-Triggered Inference
Refuses to generate outputs when critical constraints or safety rules are violated. No hallucinated compliance.
Evidence Gating
Blocks decisions unless supported by traceable, validated evidence from trusted sources.
Safety Governance
Enforces policy compliance and ethical guidelines deterministically before any output is generated.
Planning & Reasoning
Decomposes complex problems into verifiable steps rather than guessing the final answer.
Two-Channel Outputs
Delivers a machine-readable data object for systems and a separate human-readable narrative.
Deterministic Validation
Validates inputs and outputs against rigid schemas and business logic. No exceptions.
Safe Evolution
Models update without breaking existing governance or compliance rules. EWC-protected continual learning.
Multi-Tenancy
Logical and physical isolation ensures data privacy and regulatory compliance per tenant.
25 Safety Domains
Every domain has unique failure modes that generic AI cannot handle. We built domain-specific evaluation models where missing a violation costs orders of magnitude more than a false alarm.
Healthcare
Healthcare Operations
Patient safety demands zero tolerance for medication errors and contraindication misses.
Healthcare Operations
Patient safety demands zero tolerance for medication errors and contraindication misses.
The Real Problem
Hospital systems process thousands of medication orders daily. A single dosage error or missed drug interaction can cause patient harm or death. Existing AI tools optimize for throughput, not safety.
Why Generic AI Fails
Generic LLMs will confidently generate plausible-sounding medication guidance without cross-referencing patient records, known contraindications, or dosage bounds. They cannot enforce hard safety ceilings.
How SolaceSentry Works Differently
SolaceSentry refuses inference when medication dosages exceed prescribed bounds or contraindications are detected. Every decision includes a structured evidence trail linking to specific patient records and clinical guidelines.
Clinical Decision Support
Diagnostic recommendations must account for rare diseases and avoid anchoring bias.
Clinical Decision Support
Diagnostic recommendations must account for rare diseases and avoid anchoring bias.
The Real Problem
Clinicians face cognitive overload with complex cases. Missed rare diagnoses and anchoring to initial impressions cause diagnostic errors in 10-15% of cases.
Why Generic AI Fails
LLMs anchor to common conditions and miss rare presentations. They generate differential diagnoses without weighting evidence quality or tracking diagnostic confidence.
How SolaceSentry Works Differently
Evidence-gated differential diagnosis with explicit confidence bounds. The system flags when evidence is insufficient and refuses to rank diagnoses without supporting data.
Pharmaceutical Trials
Clinical trial monitoring must enforce protocol compliance and detect adverse events.
Pharmaceutical Trials
Clinical trial monitoring must enforce protocol compliance and detect adverse events.
The Real Problem
Phase II/III trials involve thousands of patients across sites. Protocol deviations and unreported adverse events jeopardize patient safety and regulatory submissions.
Why Generic AI Fails
Generic AI summarizes trial data without enforcing protocol boundaries or flagging statistical anomalies that indicate safety signals.
How SolaceSentry Works Differently
Real-time protocol compliance monitoring with automatic deviation detection. Safety signals trigger immediate inference refusal until reviewed by the safety board.
Laboratory QA
Lab testing must enforce quality control protocols and detect equipment calibration drift.
Laboratory QA
Lab testing must enforce quality control protocols and detect equipment calibration drift.
The Real Problem
Laboratory errors account for 70% of diagnostic mistakes. Equipment drift, reagent degradation, and protocol deviations compound silently.
Why Generic AI Fails
Generic AI cannot detect subtle calibration drift patterns or enforce quality control protocols across interconnected testing workflows.
How SolaceSentry Works Differently
Continuous QC monitoring with statistical process control. Calibration drift triggers violation-state before results are released.
Financial
Revenue Cycle Integrity
Billing code validation must prevent fraud and ensure regulatory compliance.
Revenue Cycle Integrity
Billing code validation must prevent fraud and ensure regulatory compliance.
The Real Problem
Healthcare billing fraud costs $100B+ annually. Upcoding, unbundling, and phantom billing slip through automated systems designed for speed, not accuracy.
Why Generic AI Fails
LLMs can generate billing justifications that sound correct but violate CMS guidelines. They optimize for plausibility, not regulatory compliance.
How SolaceSentry Works Differently
Every billing code is validated against the specific service performed, patient records, and current regulatory requirements. Violations trigger refusal with structured explanation.
Financial Risk
Risk modeling must account for tail events and regulatory capital requirements.
Financial Risk
Risk modeling must account for tail events and regulatory capital requirements.
The Real Problem
Financial institutions must assess credit, market, and operational risk under strict regulatory frameworks (Basel III, Dodd-Frank). Models that miss tail risks cause systemic failures.
Why Generic AI Fails
LLMs generate risk assessments without understanding regulatory capital calculations, stress testing requirements, or tail risk distributions.
How SolaceSentry Works Differently
Risk assessments are constrained by regulatory frameworks. The system refuses to generate risk scores when input data quality is below threshold or stress scenarios are incomplete.
Insurance Underwriting
Underwriting decisions must balance risk pricing with regulatory fairness requirements.
Insurance Underwriting
Underwriting decisions must balance risk pricing with regulatory fairness requirements.
The Real Problem
Underwriters must price risk accurately while complying with anti-discrimination laws and regulatory rate filing requirements across jurisdictions.
Why Generic AI Fails
Generic AI may inadvertently use protected characteristics as pricing factors or generate rates that violate state-specific filing requirements.
How SolaceSentry Works Differently
Fairness constraints are enforced at inference time. Every pricing decision includes a fairness audit trail documenting which factors were used and which were excluded.
Claims Processing
Claims adjudication must detect fraud while ensuring legitimate claims are paid promptly.
Claims Processing
Claims adjudication must detect fraud while ensuring legitimate claims are paid promptly.
The Real Problem
Insurance fraud costs $80B+ annually, but overly aggressive fraud detection delays legitimate claims and harms customers.
Why Generic AI Fails
LLMs either over-flag (causing delays) or under-flag (missing fraud). They cannot balance false-positive costs against fraud loss.
How SolaceSentry Works Differently
Asymmetric loss functions tuned per claim type. Fraud detection sensitivity adjusts based on claim characteristics while maintaining minimum legitimate-claim processing speed.
Fraud Detection
Transaction monitoring must balance fraud prevention with customer experience.
Fraud Detection
Transaction monitoring must balance fraud prevention with customer experience.
The Real Problem
Real-time transaction monitoring must catch fraud within milliseconds while keeping false-positive rates below 1% to avoid customer friction.
Why Generic AI Fails
Pattern-matching AI generates too many false positives. LLMs cannot provide real-time sub-millisecond decisions with deterministic audit trails.
How SolaceSentry Works Differently
Sub-millisecond inference with structured evidence chains. Every flagged transaction includes the specific rules and patterns that triggered the alert.
Legal & Regulatory
Legal Analysis
Contract review and case law research demand citation accuracy and precedent validation.
Legal Analysis
Contract review and case law research demand citation accuracy and precedent validation.
The Real Problem
Legal analysis requires exact citation of statutes, regulations, and case law. A single incorrect citation can undermine an entire legal argument.
Why Generic AI Fails
LLMs hallucinate case citations, fabricate statutes, and generate plausible-sounding but incorrect legal reasoning. They cannot distinguish binding from persuasive authority.
How SolaceSentry Works Differently
Every legal citation is validated against the actual source. The system refuses to generate legal analysis when cited authorities cannot be verified.
Regulatory Compliance
Compliance monitoring requires continuous rule tracking and evidence-based violation detection.
Regulatory Compliance
Compliance monitoring requires continuous rule tracking and evidence-based violation detection.
The Real Problem
Regulations change constantly across jurisdictions. Organizations must track thousands of requirements and demonstrate ongoing compliance.
Why Generic AI Fails
LLMs provide compliance advice based on training data that may be outdated. They cannot track regulatory changes in real time or enforce specific compliance requirements.
How SolaceSentry Works Differently
Compliance rules are encoded as enforceable constraints. The system tracks regulatory changes and flags when existing compliance postures may be affected.
Government Policy
Policy analysis must validate compliance with existing regulations and detect conflicts.
Government Policy
Policy analysis must validate compliance with existing regulations and detect conflicts.
The Real Problem
Government agencies must ensure new policies align with existing law, avoid unintended consequences, and maintain consistency across regulatory frameworks.
Why Generic AI Fails
LLMs generate policy recommendations without systematically checking for conflicts with existing regulations or analyzing cascading effects across agencies.
How SolaceSentry Works Differently
Policy analysis includes systematic conflict detection across regulatory frameworks. Recommendations include structured impact assessments.
Cyber & Security
Cybersecurity SOC
Threat detection must minimize false positives while ensuring zero missed critical alerts.
Cybersecurity SOC
Threat detection must minimize false positives while ensuring zero missed critical alerts.
The Real Problem
SOC analysts face 10,000+ alerts daily. Alert fatigue causes critical threats to be missed. Mean time to detect (MTTD) averages 200+ days for advanced threats.
Why Generic AI Fails
AI alert triage either floods analysts with false positives or misses novel attack patterns. LLMs cannot maintain real-time threat context across alert streams.
How SolaceSentry Works Differently
Asymmetric loss ensures critical threats are never downgraded. Each alert triage decision includes the specific indicators and evidence that informed the severity classification.
Threat Hunting
Proactive threat discovery requires hypothesis-driven analysis and evidence validation.
Threat Hunting
Proactive threat discovery requires hypothesis-driven analysis and evidence validation.
The Real Problem
Threat hunters must form hypotheses about adversary behavior and systematically validate them against telemetry. This requires structured reasoning, not pattern matching.
Why Generic AI Fails
LLMs generate plausible threat narratives without systematically testing hypotheses against available evidence or tracking confidence levels.
How SolaceSentry Works Differently
Hypothesis-driven threat analysis with explicit evidence requirements. The system refuses to confirm threat hypotheses without sufficient corroborating indicators.
Incident Response
Incident management must enforce escalation protocols and coordinate response actions.
Incident Response
Incident management must enforce escalation protocols and coordinate response actions.
The Real Problem
Security incidents require coordinated response across teams with strict escalation timelines. Missed escalations and uncoordinated actions amplify damage.
Why Generic AI Fails
Generic AI generates response playbooks without enforcing escalation deadlines, tracking response progress, or validating containment actions.
How SolaceSentry Works Differently
Escalation protocols are enforced as hard constraints. Response actions are tracked with evidence-based verification of containment effectiveness.
AI Governance
AI system oversight must enforce fairness constraints and detect model drift.
AI Governance
AI system oversight must enforce fairness constraints and detect model drift.
The Real Problem
Organizations deploying AI must ensure models remain fair, accurate, and aligned with governance policies over time.
Why Generic AI Fails
Generic AI cannot monitor other AI systems for drift, bias emergence, or governance policy violations in a structured, auditable way.
How SolaceSentry Works Differently
Continuous model monitoring with fairness constraint enforcement. Drift detection triggers governance reviews before affected decisions are released.
Industrial
Supply Chain
Logistics optimization must respect inventory constraints and delivery guarantees.
Supply Chain
Logistics optimization must respect inventory constraints and delivery guarantees.
The Real Problem
Supply chain decisions involve thousands of interdependent constraints: inventory levels, lead times, capacity limits, and contractual obligations.
Why Generic AI Fails
LLMs generate supply chain recommendations without enforcing hard constraints like minimum inventory levels or contractual delivery windows.
How SolaceSentry Works Differently
Every supply chain decision is validated against constraint boundaries. The system refuses to recommend actions that would violate safety stock levels or delivery guarantees.
Manufacturing QA
Quality control must detect defects while minimizing false rejects and production downtime.
Manufacturing QA
Quality control must detect defects while minimizing false rejects and production downtime.
The Real Problem
Manufacturing quality decisions have direct cost impact. False rejects waste material; missed defects cause recalls and endanger consumers.
Why Generic AI Fails
Generic AI optimizes for defect detection without accounting for false-reject costs or production line throughput impacts.
How SolaceSentry Works Differently
Asymmetric quality gates tuned per product line. Defect classification includes confidence bounds and economic impact assessment.
Energy Grid
Grid management must balance load, generation, and safety constraints in real time.
Energy Grid
Grid management must balance load, generation, and safety constraints in real time.
The Real Problem
Power grid operations involve real-time balancing of generation, transmission, and demand with zero tolerance for cascading failures.
Why Generic AI Fails
LLMs cannot perform real-time constraint optimization across interconnected grid segments while enforcing safety margins.
How SolaceSentry Works Differently
Real-time constraint enforcement with safety margin preservation. Grid actions are validated against N-1 contingency requirements before execution.
Critical Infrastructure
Infrastructure monitoring must detect anomalies while respecting safety and regulatory constraints.
Critical Infrastructure
Infrastructure monitoring must detect anomalies while respecting safety and regulatory constraints.
The Real Problem
Critical infrastructure (water, transport, telecom) requires continuous monitoring with immediate response to anomalies that could affect public safety.
Why Generic AI Fails
Generic anomaly detection generates excessive false alarms or misses subtle degradation patterns that precede catastrophic failures.
How SolaceSentry Works Differently
Multi-sensor anomaly detection with safety-first classification. Degradation patterns trigger graduated responses based on severity and public safety impact.
Transport
Aviation
Flight operations must enforce strict safety regulations and maintenance compliance.
Aviation
Flight operations must enforce strict safety regulations and maintenance compliance.
The Real Problem
Aviation safety depends on absolute compliance with FAA/EASA regulations, maintenance schedules, and operational limitations. Any deviation can be catastrophic.
Why Generic AI Fails
LLMs generate aviation recommendations without cross-referencing specific aircraft type certificates, airworthiness directives, or operational limitations.
How SolaceSentry Works Differently
Every operational recommendation is validated against the specific aircraft's type certificate and current airworthiness status. Maintenance deviations trigger immediate refusal.
Autonomous Oversight
Autonomous system monitoring must detect edge cases and enforce operational boundaries.
Autonomous Oversight
Autonomous system monitoring must detect edge cases and enforce operational boundaries.
The Real Problem
Autonomous vehicles and robotics operate in unpredictable environments. Edge cases that fall outside training distributions cause silent failures.
Why Generic AI Fails
AI supervision of AI systems creates recursive blind spots. Generic models cannot detect when autonomous systems encounter out-of-distribution scenarios.
How SolaceSentry Works Differently
Operational boundary enforcement with explicit out-of-distribution detection. The system triggers human-in-the-loop escalation when confidence drops below operational thresholds.
Safety Engineering
Safety analysis must enforce hazard mitigation requirements and validate risk controls.
Safety Engineering
Safety analysis must enforce hazard mitigation requirements and validate risk controls.
The Real Problem
Safety engineering requires systematic hazard analysis (HAZOP, FMEA, FTA) with traceable links between identified hazards and implemented controls.
Why Generic AI Fails
LLMs generate safety analyses without maintaining traceability between hazards, controls, and verification evidence.
How SolaceSentry Works Differently
End-to-end traceability from hazard identification through control implementation to verification. Missing controls trigger violation states.
People
HR Compliance
Employment decisions must enforce anti-discrimination laws and document justifications.
HR Compliance
Employment decisions must enforce anti-discrimination laws and document justifications.
The Real Problem
HR decisions (hiring, promotion, termination) carry significant legal exposure. Undocumented decision rationale invites discrimination claims.
Why Generic AI Fails
LLMs may perpetuate historical biases present in training data. They cannot enforce protected-class safeguards or generate legally defensible justifications.
How SolaceSentry Works Differently
Every HR recommendation includes a fairness audit and structured justification. Protected-class factors are explicitly excluded from decision logic with verifiable proof.
See It In Action
A healthcare violation scenario. The system detects a medication dosage that exceeds prescribed bounds and refuses to generate output.
// Violation detected — inference refused { "record_id": "rec_01JK8NR2V9M4", "risk_tier": "critical", "classification": "violation", "response_mode": "refuse", "expectation": { "rule": "medication_dosage_within_bounds", "severity": "critical" }, "violation": { "observed": 850, "maximum": 500, "unit": "mg", "overshoot": "70%" } }
Transparent Pricing
Usage-based pricing designed for scale, with optional isolation tiers for mission-critical security.
Shared Inference
Pooled Multi-Tenant
No base fee
CPU-powered inference on shared infrastructure. Ideal for development, testing, and light production workloads.
- All 25 safety domains
- Pay-per-use ($2.50/1M tokens)
- Up to 5 seats
- CPU inference (~30-50ms latency)
- Dashboard & usage analytics
- Standard rate limits (60 rpm)
- Email support
- Best-effort availability
Dedicated
Physically Isolated
+ $1,500/mo base fee
Dedicated GPU with isolated database. Continual learning on your domain data. 5-10x faster inference.
- All 25 safety domains
- Tokens billed at usage ($5.00/1M)
- Up to 50 seats
- Dedicated GPU inference (~5-10ms latency)
- Isolated database + node pool
- Continual learning on your data
- Priority rate limits (300 rpm)
- Custom domain gates
- Slack + email support
- 99.5% uptime SLA
Enterprise Security
Compliance-Ready
+ $3,500/mo base fee
High-performance GPU with isolated VPC, HA database, and expanded audit and governance controls. BAA may be available for qualifying healthcare workloads.
- All 25 safety domains
- Tokens billed at usage ($8.50/1M)
- Unlimited seats
- High-performance GPU inference (~3-5ms latency)
- Isolated HA infrastructure + VPC
- Mapped to internal security control baseline
- BAA may be available for qualifying healthcare use cases
- Audit trail & explainability controls
- Dedicated support engineer
- 99.9% uptime SLA
Frequently Asked Questions
How is this different from a fine-tuned LLM?
Fine-tuned LLMs optimize for generating plausible text. SolaceSentry optimizes for defensibility and correctness. When constraints are violated, a fine-tuned LLM will still generate output -- ours refuses. Every decision includes a structured evidence trail, not just a confident paragraph. The system has 8 hard invariants that cannot be bypassed, including immutable records, grounded narratives, and evidence-gated outputs.
What happens when constraints are violated?
The system triggers a refusal state. Instead of generating a plausibly incorrect answer, it returns a structured error object detailing which specific constraint was violated (e.g., MissingEvidenceError, SafetyPolicyViolation). The violation is recorded in an immutable audit trail with the evidence that triggered it.
Can we define our own safety rules?
Yes. Dedicated tier customers can define custom policy sets using our formal logic syntax. These rules are compiled into the inference gating layer and enforced on every request. Custom rules receive the same hard-invariant protections as built-in rules.
Is the decision record legally admissible?
Our decision records are designed to support legal defense and regulatory audit. They provide a deterministic trace of inputs, rules evaluated, evidence cited, and the final output. While admissibility depends on jurisdiction, they offer significantly higher evidentiary value than standard LLM logs.
How do you handle data privacy in multi-tenant environments?
We use strict logical isolation for shared tiers and physical isolation for dedicated tiers. Tenant contexts are ephemeral and wiped after inference. No customer data is ever used to train shared foundation models. Enterprise Security adds isolated infrastructure and can support contractual controls such as a BAA for qualifying healthcare use cases.
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Constraint-grounded inference for high-consequence domains. Bounded, traceable, and defensible decisions.
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