AI Engineering Practice
AI systems that survive production.
The gap between an AI demo and a production AI system is an engineering problem — data pipelines, deployment infrastructure, semantic caching, and evaluation telemetry. We solve the engineering.
AI Engineering Thesis
"Most AI initiatives fail not because the model was inadequate, but because the engineering wasn't there — broken data pipelines, lack of evaluation frameworks, unmanaged token costs, and zero regression testing. We build the systems that make models reliable."
Capabilities
AI Engineering Capabilities
Secure LLM Gateway & Proxy
Production LLM deployments with semantic caching, access controls, data governance, and token cost quotas. We integrate models directly into your API gateways and auth layers.
High-Accuracy RAG Architectures
Retrieval-augmented generation systems built for accuracy and auditability. Vector databases, embedding pipelines, semantic chunking, and cross-encoder reranking.
AIOps & Automated Remediation
ML-powered anomaly detection, intelligent telemetry alerting, and automated incident triage built on your existing Prometheus and OpenTelemetry metrics.
Model Governance & Audit Trails
Model registries, prompt version control, output validation, and compliance audit trails. Governance infrastructure that makes AI deployments auditable and reproducible.
Deterministic Data Pipelines
ETL and feature pipelines built for reliability and traceability. Data quality checks, lineage tracking, and versioned datasets feeding production models.
Kubernetes ML Infrastructure
Training infrastructure, model serving, and GPU scheduling on Kubernetes. Dynamic GPU slicing, model versioning, and canary deployments.
Prompt CI/CD & Regression Suites
Systematic prompt engineering with Git version control, A/B testing, and automated regression testing. Prompts are code and treated with software engineering discipline.
Continuous Evaluation Telemetry
Model evaluation frameworks, benchmark suites, regression detection, and output quality monitoring. Know when model accuracy drifts before users notice.
AI Architecture Blueprint
Production AI & RAG Specification
Enterprise-grade data ingestion, deterministic vector indexing, governed LLM gateway, and continuous evaluation telemetry.
Data Ingestion & Sanitization
Isolation Boundary
Air-gapped data extraction pipeline with automated PII masking
Enforcement & Controls
Deterministic chunking strategies, document checksumming, metadata tag indexing
Auditability & Observability
Full data lineage tracking from source document to vector embedding
Need this implemented in your environment?
Review Spec With Senior Engineer →Engineering Candor
Not Every Problem Needs AI.
We will tell you honestly when deterministic code or a SQL query outperforms a complex model at a fraction of the cost and maintenance.
Good Fit
- • High-volume document classification & extraction
- • Semantic search across complex internal wikis
- • Multi-signal telemetry anomaly detection
Evaluate Carefully
- • Internal agentic workflows (needs strict evals)
- • Customer-facing chat triage (needs fallbacks)
- • Code generation tools in regulated pipelines
Probably Not
- • Small datasets (< 1,000 structured samples)
- • Deterministic math or business accounting rules
- • Queries easily solved by indexed SQL / Postgres
Process
How an AI Engagement Works
Feasibility Assessment
We evaluate whether AI is the right tool for your problem or if deterministic rules/SQL outperform ML at 10x lower cost.
Data & Model Architecture
Designing ingestion pipelines, vector indices, model gateways, and evaluation benchmarks before deploying code.
Build & CI Evaluation
Implementation with evaluation suites in CI, automated regression tests, semantic caching, and full observability.
Operate & Drift Control
Ongoing telemetry, data drift monitoring, cost accounting, and prompt regression safeguards your team operates.
FAQ
Questions We Get Asked
Which LLM model providers do you work with?
We are provider-agnostic. We deploy architectures on OpenAI, Anthropic, Google Vertex AI, and open-source models (Llama, Mistral, DeepSeek). We design abstractions that avoid vendor lock-in.
How do you enforce enterprise data privacy with LLMs?
Every engagement enforces strict data classification. PII is redacted pre-inference, audit logging captures all interactions, and on-premise/VPC private endpoints ensure data never leaks to model training.
RAG vs Fine-Tuning: Which is the right choice?
In 90% of enterprise use cases, RAG is superior: it is cheaper, auditable, immediately updatable, and hallucination-resistant. We only recommend fine-tuning when specific model behavior or syntax styling must be altered.
Build AI systems that actually work in production.
Whether evaluating feasibility or scaling a deployment, we build the reliable data pipelines, model gateways, and evaluation tests production AI requires.