Services · AI Enablement
AI Delivery that Compounds
We design and ship production AI systems using SIGNAL — our six-phase delivery framework — accelerated by Cortex™ delivery intelligence and a library of seven pre-built FORGE components. Most AI projects die between pilot and production. Ours don't.
Why most AI projects stall
The model works in the notebook. Then it meets reality.
Industry benchmarks consistently show 9 to 18 months from project start to first AI model in production. Three causes account for most of that timeline: strategy and governance are sequenced in series rather than concurrently, data infrastructure is rebuilt for every use case instead of reused, and compliance review is left to the end instead of engineered into the delivery. Ensylon is structured around fixing each of those.
Our framework
SIGNAL: Six phases, built for production AI.
A sprint-based delivery methodology with quality gates, risk checkpoints, and model evaluation loops built in from sprint zero. SIGNAL replaces the waterfall-with-an-AI-layer pattern that most service firms still run. The difference is sequencing: we instrument evaluation and governance in the first sprint, not the last. Which means by the time a model is ready to go live, the evidence of whether it should is already in hand.
Phase 1 — S · SCAN
Discovery and data scoping. We map the decision the AI needs to make, the data it has to work with, and the boundary of the regulated environment it will operate inside. VaultConnect is configured here to give the team access to PHI/PII-free working data in days, not months.
Phase 2 — I · IGNITE
First experiment goes live. ChunkIQ handles document segmentation, GraphStore stands up the knowledge graph, RouteIQ selects models across providers, EvalBench establishes baseline metrics. By end of IGNITE, there's a working model and a baseline to measure it against.
Phase 3 — G · GROUND
Failure modes are mapped. EvalBench runs against the baseline; LoopBack captures every human override as ground truth. What we learn here determines whether the use case is worth amplifying — and if not, we say so before the sunk cost grows.
Phase 4 — N · NAVIGATE
Evidence-based go/no-go. AuditLog provides the evidence gate; EvalBench provides the decision data. This is the phase where we recommend continuing, redirecting, or stopping — with the data on record.
Phase 5 — A · AMPLIFY
Scale-up and hardening. All seven FORGE components are now in use. RouteIQ is tuned for production latency and cost. The system is load-tested against the actual throughput it needs to hold up to.
Phase 6 — L · LOCK IN
Operating model hand-off. AuditLog becomes the source of ongoing compliance documentation. LoopBack becomes the continuous feedback loop feeding model improvement. The client's own team takes ownership; we move into a supporting role.
Our pre-built components
FORGE: 7 production-hardened components, ready on day one.
Every SIGNAL sprint draws on pre-built FORGE components instead of rebuilding from scratch. Each one is proven across Ensylon client deployments in regulated environments.
01
ChunkIQ — Adaptive chunking engine
Context-aware document segmentation that preserves clause integrity across insurance policies, clinical notes, and regulatory filings. Proven on claims libraries and 50-state regulatory archives.
02
RouteIQ — Model orchestration layer
Multi-LLM routing across OpenAI, Anthropic, Google, and open-source models, with automatic cost optimization, latency SLAs, and fallback management. Designed to eliminate vendor lock-in.
03
EvalBench — Automated evaluation harness
Quantitative and qualitative evaluation framework running on every model output. Establishes baseline metrics in sprint 0 and tracks performance through iterations. Human-eval rubrics included.
04
AuditLog — Compliance audit trail
Append-only log of every model call — input, output, timestamp, user, and decision outcome. Engineered to satisfy the HIPAA Security Rule, NAIC audit requirements, and SOX-adjacent documentation needs.
05
VaultConnect — Secure data boundary
PHI/PII masking, synthetic data generation, and scoped data environment provisioning. Regulated-data experiments become possible in days, not quarters, without HIPAA exposure.
06
GraphStore — Knowledge graph & vector layer
Neo4j-based dependency mapping combined with vector storage for semantic retrieval. Maintains structural context across multi-document workflows — policies, clinical notes, and financial filings.
07
LoopBack — Human-in-loop feedback system
Structured human review workflow with override tracking, correction capture, and automated feedback routing back to model improvement. Override rates directly inform governance policy.
At a glance
Three service lines within AI Enablement.
Explore our digital product engineering services below, and discover how we can help bring your vision to life.
AI Consulting
Strategic guidance for AI adoption. Readiness assessments, opportunity identification, use case ideation, and technology evaluation. For organizations that need to build the internal conviction before spending on implementation.
Responsible AI Validation
Independent evaluation of existing AI systems. Bias testing, explainability assessment, risk assessment, and alignment against AI governance frameworks and applicable regulation. Delivered as a fixed-scope engagement.
AI Implementation
End-to-end design, build, integration, deployment, and maintenance of AI systems — the core of what we do. Delivered through SIGNAL, accelerated by FORGE and Cortex™, structured around the business outcome.