Capabilities
Enterprise AI applications engineered for real operations
We connect models to governed data, business workflows, and measurable outcomes — then build the controls needed to run them safely.
LLM application development
Purpose-built copilots, assistants, document intelligence, search, and workflow applications across web and internal platforms.
RAG pipelines
Ingestion, chunking, embeddings, retrieval, reranking, citations, permissions, and quantitative relevance evaluation.
Autonomous agents
Tool-using agents and multi-step workflows with state, approval gates, observability, and bounded permissions.
Model adaptation
Model selection, prompt engineering, fine-tuning, distillation, and inference optimization based on evidence.
AI compliance & safety
PII controls, model risk documentation, red teaming, audit trails, content safeguards, and human review.
POC-to-production migration
Architecture hardening, evaluation suites, integrations, monitoring, cost controls, and ownership transfer.
How we deliver
A delivery model your PMO can audit
Every engagement runs on the same transparent process, with named accountability from intake through steady state.
- 1
Frame the use case
Define the user decision, baseline, risk tier, acceptance metrics, and data boundaries.
- 2
Prototype and evaluate
Test model and retrieval options against a representative evaluation dataset.
- 3
Engineer the product
Build secure integrations, user experience, controls, telemetry, and deployment automation.
- 4
Operate and improve
Monitor quality, safety, latency, and spend while iterating against real feedback.
Technology
Tools and platforms we staff and support
- OpenAI
- Azure OpenAI
- Amazon Bedrock
- Hugging Face
- LangChain
- LlamaIndex
- Pinecone
- Weaviate
- Milvus
- PyTorch
- TensorFlow
- MLflow
- Databricks
- Snowflake
- Python
Outcomes
What clients get
- A measurable AI product tied to a business workflow, not an isolated demo.
- Grounded responses with citations, access controls, and repeatable evaluation.
- Operational visibility into quality, latency, usage, risk, and cost.
- Documented architecture and runbooks your internal team can own.
FAQ