Reliable AI Systems, Agent Workflows & Model Integration
Engineering grounded, deterministic AI systems and agentic workflows with strict verification boundaries, prompt firewalls, and observable tool calling.
The Operational Challenge
Organizations encounter high failure rates, hallucinated actions, security vulnerabilities, and unpredictable latency when deploying probabilistic LLMs into mission-critical workflows.
Core Capabilities
- 01Deterministic agentic state machines with bounded execution loops
- 02Structured schema extraction and strict runtime output validation
- 03Tool-use sandboxing, authorization barriers, and prompt injection defense
- 04Retrieval-augmented generation (RAG) with grounded citation tracking
- 05Cost, latency, and token telemetry with provider fallback routing
Engineering Process
Task Boundary & Threat Analysis
Define explicit operational budgets, failure fallbacks, output schemas, and security guardrails for AI interactions.
Orchestration & Verification Engine
Develop multi-step workflows with automated judges, validation layers, and reproducible evaluation harnesses.
Production Deployment & Observability
Deploy with telemetry monitoring token spend, drift, error rates, and deterministic execution logs.
Technical Proof in Production
Project: callpilot
Designed voice and appointment triage pipelines with strict intent verification and fail-closed operational states.
Project: leadrescue
Built rule-bounded AI lead-response pipelines with structured qualification output and human-approved sends.
Frequently Asked Questions
How do you prevent LLM hallucinations from causing damage?
We require all external tool calls to pass independent schema validation, capability checks, and deterministic sanity rules before execution.
Which foundation model providers do you support?
We architect provider-agnostic abstractions supporting Anthropic Claude, OpenAI, Google Gemini, and local open-weights models.
Ready to execute on this capability?
Discuss architecture requirements directly with our engineering team.