RAG, Agents, Evals
Built Like Real Software
Stop building prototypes. Start shipping reliable, cost-effective LLM systems with engineering rigor. We do not just prompt. We engineer systems.
Reduce hallucinations
Systematic eval pipelines to catch errors before users do.
Lower latency
Optimized inference paths for sub-second responses.
Secure workflows
Enterprise-grade data privacy and PII redaction.
Operator-led LLM Engineering
Proven methodologies for production-grade AI.
Security-First
We implement prompt injection defenses and PII masking at the middleware layer.
Evals-Driven
We build quantitative evaluation datasets to measure accuracy, retrieval quality, and tone drift over time.
Cost-Aware
We optimize token usage, caching strategies, and model selection so large models are only used when they are worth it.
How We Work Together
Choose the engagement model that fits your product stage. From audit to full build.
Workflow Audit
Deep dive into your existing LLM architecture. We identify bottlenecks, security flaws, and cost leaks. You get a prioritized roadmap.
Build Sprint
We build a specific feature or MVP from scratch. Perfect for shipping a reliable RAG pipeline or agent workflow quickly.
Fractional Partner
Ongoing engineering leadership. We join your team part-time to steer technical strategy, review PRs, and ensure long-term stability.
The Engineering Process
Reliable systems are not guessed. They follow a strict lifecycle.
Diagnose
We analyze the problem space, define success metrics, and map out data flows.
Design
Architecting the RAG pipeline or agent workflow. Selecting models, vector stores, and caching layers.
Build
Writing clean, modular code. Implementing guardrails, logging, and integration tests.
Stabilize
Rigorous red-teaming and evaluation against golden datasets. Latency optimization and cost reduction.
Results in Production
RAG Pipeline Optimization
Implemented semantic caching, hybrid search, and smaller task-specific models to cut cost and latency without losing accuracy.
Clinical Data Extraction
Built a multi-step workflow with verification loops and strict schema validation to replace fragile manual data entry.
Frequently Asked Questions
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Want this working in production?
Skip the learning curve. Let us engineer a solution that scales.