1. Architecture Audit & Data Inventory
We map the current stack, identify workflow bottlenecks, evaluate data freshness, and score schemas for LLM compatibility.
- Capability gap analysis
- Data readiness map
- Security and access risk register
The Engine
The AI-Native SDLC for governed speed.
A focused delivery system for building AI infrastructure faster without pretending evaluation, security, or operational ownership can wait until later.
RAG Secure retrieval with metadata, permissions, and evals
Agents Graph-based workflows with explicit control gates
Hardening Tracing, rubrics, drift monitoring, and cost controls
Security Least-privilege runtime design for proprietary data
Delivery framework
Six weeks from architecture signal to production-ready controls.
Every phase produces concrete technical artifacts your leadership team can inspect, operate, and use for investment decisions.
2. Functional Prototyping
We build a production-grade pipeline or multi-agent prototype with real models and real integration assumptions.
- Working AI workflow
- Model and retrieval fit report
- Cost and latency baseline
3. Automated Eval & Guardrail Build
We add scoring rubrics, tracing, prompt and retrieval logs, least-privilege controls, and human validation gates.
- Evaluation harness
- Observability dashboard
- Production hardening plan
Operating principles
Faster delivery only matters when the system can be trusted.
Real model calls, not slideware.
We validate against actual model behavior, data shape, latency, and cost so decisions are grounded in system evidence.
Security before autonomy.
Agents receive only the permissions they need, and sensitive actions require deterministic checks or human approval.
Evaluation as infrastructure.
Rubrics, traces, and regression datasets are part of the product surface, not a QA afterthought.
Next step
Start with an architecture audit.
We will map data readiness, workflow fit, AI risk, implementation cost, and the first valuable production path.