Structured intelligence
for high-stakes decisions
An AI system that brings consistency and auditability to complex, document-heavy decisions — combining deterministic business rules, large language model judgment, and semantic retrieval in a single traceable pipeline.
Built for decisions that can't afford to be wrong
High-stakes decisions are layered, document-heavy, and consequential. Signal Layer processes that complexity systematically — every determination is traceable, every edge case is handled.
Multi-stage decision pipeline
A structured pipeline evaluates criteria sequentially across independent stages. Each stage produces a discrete decision with its own confidence score and reasoning trace.
Hybrid deterministic + AI judgment
Objective criteria are evaluated in pure code — no model involvement where the rules are unambiguous. The model is invoked only where language understanding genuinely adds value.
Document-grounded decisions
A semantic retrieval layer surfaces the relevant guidance and supporting material at decision time. Every output cites evidence — not just an answer, but the rationale behind it.
Evaluation and benchmarking
An integrated evaluation harness runs the full pipeline against known cases and scores accuracy, confidence calibration, and reasoning quality — automatically, on every change.
Safe by design
Sensitive data never appears in log lines. Prompt injection from external sources is an explicit threat model — documented, tested, and actively mitigated.
Full audit trail
Every run is persisted with its stage decisions, confidence scores, model used, prompt version, and token spend. Reproducibility is a first-class requirement.
A pipeline designed to escalate, not guess
Each stage is an independent decision node. A low-confidence signal at any stage escalates for human review rather than propagating uncertainty forward.
Initial qualification
Program and enrollment verification
Criteria screening
Structured rule evaluation
Contextual assessment
LLM-assisted judgment with RAG grounding
Document review
Narrative and supporting evidence analysis
Final determination
Consolidated recommendation with confidence
iEvery stage returns one of three outcomes: PASS, FAIL, or ESCALATE. An ESCALATE at any point routes to a human reviewer — the system is designed to surface uncertainty, not suppress it.
Production-grade from the first commit
Every layer of the stack is held to the same standard: observable, testable, auditable. Quality gates are enforced automatically on every push.
CI / Type safety
- Zero type errors enforced on every merge
- Automated lint and import ordering
- Pre-commit hooks block bad commits
- Secret scanning on every push
- Full quality gate in GitHub Actions
Test architecture
- Decision-table and property-based tests
- In-memory fixtures — no production side-effects
- Real-LLM integration tests, opt-in gated
- Safety regression suite for known failure classes
- Evaluation and unit tests fully isolated
Eval harness
- Batched async LLM calls — cost-efficient at scale
- Confidence calibration scoring
- Prompt A/B testing with isolated run tags
- Multi-model comparison built in
- Baseline scorecard protected from experimental runs
Infrastructure
- Cloud-hosted, managed identity auth
- Relational persistence for all decisions
- Vector embeddings for semantic retrieval
- Async throughout — no blocking I/O
- Structured observability in progress
Security posture
- Sensitive data excluded from all log lines
- Adversarial prompt injection tested
- OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF mapped to the threat model
- Secrets rotation procedure documented
- Dedicated robustness hardening phase
Model strategy
- Fast models for all classification work
- Larger models reserved for ambiguous judgment
- Temperature=0 enforced for consistency
- Deterministic pre-computation — models don't do math
- Cost-per-call tracked in every eval run
A structured path to production readiness
Nine sequential quality phases, each with explicit acceptance criteria and a written record. Progress is tracked, not estimated.
Type safety + CI gate
Zero type errors, automated lint, continuous integration green on every push.
Test infrastructure
Comprehensive test suite across core modules, real-LLM integration tests, documented findings.
Observability
Structured JSON logs, correlation IDs, live operations dashboard.
Security hardening
Input validation, rate limiting, mitigations aligned to OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF.
Load testing
Throughput benchmarks, latency SLOs, concurrency behaviour.
Robustness
Adversarial prompt-injection mitigations, edge-case coverage, retry hardening.
Curated benchmark
Golden test set, LLM-as-judge evaluation, regression guard.
Cost + UX polish
Token accounting, spend visibility, latency improvements.
Documentation
Security model, architecture write-up, portfolio artefacts, full eval coverage.
Prompt injection via tool output
Integration tests showed a model can be manipulated by adversarial content embedded in external data returned by tools. Classified against industry-standard frameworks — OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF — with an explicit mitigation phase scoped.
Smaller models match larger ones on structured tasks
On challenging boundary cases, fast and large models reached identical decisions while the larger model used significantly more tokens. Where classification logic is pre-computed, scale does not improve outcomes.
~60% token reduction with identical accuracy
A minimal prompt variant matched the full default prompt on live test cases. Validates prompt compression work against real model traffic, not just offline benchmarks.
What this system is built on
The non-negotiables that shape every architectural choice.
Deterministic first
Every rule that can be expressed in code is expressed in code. The model is a last resort for genuine ambiguity, not a shortcut around engineering.
Escalate, don't fabricate
The system routes uncertain cases to humans rather than producing confident wrong answers. ESCALATE is a valid — and often correct — outcome.
Isolation by default
Test runs, experimental evals, and production traffic are strictly isolated. No experimental code contaminates live metrics.
Measure everything
Accuracy, confidence calibration, token cost, latency, and model agreement are all tracked. If a change can't be measured, it doesn't ship.
Reasoning over answers
The model is asked to cite evidence, not just return a decision. A correct answer for the wrong reason is a latent error waiting to surface.
Data hygiene everywhere
Sensitive data is treated as out-of-bounds at the system boundary — not just the database boundary. Logs, prompts, and eval outputs are all audited.