Services
Six disciplines, one architecture practice.
Each line of work below is a system we design, build and hand off, not a technology we name-drop.
LLM Systems in Production
Serving, evaluation and cost control for models that run at real traffic, not in a notebook.
When you need this
You have an LLM feature in production, or about to ship one, and latency, cost, or quality isn't where it needs to be.
What you get
- Serving architecture (vLLM, TGI, managed endpoints) sized to your traffic
- Evaluation pipelines (Ragas, DeepEval, custom online evals)
- Cost modeling and FinOps for token spend and GPU usage
- Observability (Langfuse, OpenTelemetry) and guardrails
Multi-Agent Systems
Agent architectures that hand off, verify and fail safely, built for tasks a single prompt can't carry.
When you need this
A single-model workflow has hit its ceiling and the task needs planning, tool use, or multiple specialized roles.
What you get
- Orchestration design (LangGraph, CrewAI, or custom)
- Tool use, function calling and structured outputs
- Guardrails, PII redaction and prompt-injection mitigation
- Failure handling and human-in-the-loop checkpoints
RAG & Semantic Search
Retrieval architecture that returns the right context, not just a similarity score.
When you need this
Your model gives generic or wrong answers because it can't reliably find or rank the right source material.
What you get
- Hybrid retrieval, reranking and query rewriting
- Vector database selection and indexing (pgvector, Qdrant, Weaviate, Vertex AI Vector Search)
- Chunking and embedding strategy tuned to your content
- Retrieval evaluation playbook and regression testing
Speech AI
STT/TTS pipelines built for the latency and quality a live conversation actually needs.
When you need this
You're building a voice product and need streaming transcription, natural TTS, or both, at production latency.
What you get
- Streaming STT/TTS pipeline design (Whisper, Deepgram, Google STT, ElevenLabs, Azure TTS)
- Latency budgeting across the full voice loop
- Quality evaluation for transcription and synthesis
- Integration into existing telephony or app infrastructure
Data Engineering & Pipelines
The ETL/ELT and streaming backbone that AI systems, and the rest of the business, depend on.
When you need this
Your models or dashboards are only as good as the data feeding them, and that pipeline is fragile, slow, or manual.
What you get
- ETL/ELT design and migration to cloud-native architecture
- Streaming pipelines (Kafka, Pub/Sub)
- Lakehouse architecture (Databricks, BigQuery)
- SLA, cost and reliability engineering for data platforms
Cloud-Native Architecture
GCP and OCI foundations, containers and CI/CD sized for what you actually run.
When you need this
You need a reference architecture and a delivery pipeline that will still make sense at 10x the load.
What you get
- Reference architecture on GCP and OCI
- Kubernetes, Docker and CI/CD baseline
- Cost governance and infrastructure-as-code (Terraform)
- Production readiness and architecture review