AI safety, edge intelligence, and privacy systems designed as real products.

Problem

LLM platforms lack transparent, reproducible mechanisms to flag hallucinations, bias, and latency anomalies in real time across high-volume traffic.

Solution

A model-agnostic risk engine that applies deterministic rules to requests and responses, tagging hallucination, bias, and latency risk without retraining underlying models.

Architecture (high-level)

• Rule engine evaluating content patterns, metadata, and latency thresholds.
• Streaming integration into existing logging / observability pipelines.
• Risk dashboards for VPs and platform owners to codify and monitor policies.

Impact / Why it matters

• Gives infra, security, and risk teams a clear, inspectable safety layer around any LLM endpoint.
• Turns 'AI safety' from ad-hoc dashboards into a governed product surface with explicit risk policies.

Problem

Prompt behavior is brittle and difficult to govern across teams, leading to inconsistent quality and safety incidents.

Solution

A pre- and post-processing layer around any LLM endpoint that inspects prompts and outputs, applies safety and quality rules, and deterministically rewrites prompts before execution.

Architecture (high-level)

• Pluggable rulesets for safety, quality, and policy conformance.
• Bidirectional pipeline (pre- and post-processing) around LLM APIs.
• Audit trail of rewrites and rule hits for debugging and governance.

Impact / Why it matters

• Stabilizes prompt behavior across teams without retraining models.
• Gives AI PMs a predictable, testable layer to enforce policy over time.

Problem

Enterprises need repeatable, defensible decisions on whether LLM outputs meet policy, but cannot rely on opaque scoring from another model.

Solution

A local, non-generative evaluation engine that scores content against human-readable policy rules and produces explicit allow/flag/deny outcomes with reasoning.

Architecture (high-level)

• Rule evaluation core operating fully offline and model-agnostic.
• Policy configuration DSL for content, metadata, and context.
• API + on-device deployment patterns for integration into products.

Impact / Why it matters

• Separates policy from model vendors, avoiding lock-in.
• Creates a clear governance checkpoint before outputs reach users.

Problem

Regulated enterprises can’t send sensitive data to public LLM APIs but still need intelligent assistants with auditability and alignment control.

Solution

An edge-deployed LLM architecture that keeps data on-device, implements self-forgetting memory, and uses a deterministic debugger to inspect and correct behavior locally.

Architecture (high-level)

• Local LLM runtime optimized for edge hardware (quantized / ONNX).
• Vault: controlled memory with self-forgetting policies for personal data.
• Debugger: deterministic alignment engine evaluating prompts + outputs against rule sets with local correction.
• Telemetry hooks for offline audit logs and optional batched uplink.

Impact / Why it matters

• Enables privacy-preserving assistants in healthcare, finance, and telecom.
• Gives infra, legal, and security teams an inspectable alignment layer at the edge.

Problem

Developer tools increasingly rely on generative models for code review, which are hard to audit and cannot run in air-gapped environments.

Solution

An offline, rule-based static analysis engine that detects insecure patterns using deterministic rules (e.g., OWASP-style checks) and maps them to remediation steps.

Architecture (high-level)

• AST-based analysis pipeline with domain-specific rules.
• Deterministic remediation planner ordering fixes for safety and minimal diff impact.
• Offline execution for air-gapped and high-security engineering environments.

Impact / Why it matters

• Delivers audit-ready, reproducible code safety signals without generative models.
• Complements or replaces LLM-based code review in security-critical contexts.

Problem

Telemetry and content logs leak PII and sensitive identifiers, creating compliance and breach risk when shared across teams or vendors.

Solution

A rule- and pattern-driven redaction layer that detects and removes PII before data leaves the originating system, with full audit logs.

Architecture (high-level)

• Pattern and rulesets for PII detection across structured and unstructured data.
• Policy-based redaction actions with configurable strategies (mask, hash, drop).
• End-to-end logging to prove what was redacted and why.

Impact / Why it matters

• Gives privacy and security teams a deterministic way to enforce data minimization.
• Reduces breach blast radius and compliance exposure from telemetry pipelines.

Problem

As AI-generated content proliferates, enterprises struggle to prove origins, transformations, and policy conformance for regulators and incident response.

Solution

A deterministic traceability schema and pipeline that captures prompts, models, rulesets, and downstream uses for AI-generated content.

Architecture (high-level)

• Lineage graph capturing source prompts, models, rule engines, and actions.
• Deterministic identifiers for artifacts and transformations.
• APIs and dashboards for audit, incident investigations, and compliance reviews.

Impact / Why it matters

• Makes AI outputs explainable and auditable across complex pipelines.
• Supports regulatory reporting and internal incident triage.

Problem

Prompt management is often ad-hoc, with no consistent way to test, compare, and roll out better prompts across teams and models.

Solution

A governance layer that treats prompts as experiments—versioned, routed, and evaluated with latency and quality metrics.

Architecture (high-level)

• Prompt registry with versioning and metadata.
• Experiment router selecting prompts based on config and traffic splitting.
• Telemetry ingestion and dashboards, including local LLM support via Ollama.

Impact / Why it matters

• Standardizes how prompt performance and risk are measured.
• Gives AI PMs and platform teams disciplined levers to iterate safely.