Data & AI
Building robust data foundations and AI capabilities that power intelligent applications and drive business outcomes.
Data Foundations for AI
Our Data Foundations service creates the end‑to‑end data layer that AI and analytics depend on.
What We Build
- Ingestion pipelines for batch, streaming, CDC, and API integration
- Transformation layers (ETL/ELT) for curated domain data products
- Storage across data lakes, warehouses, and specialized stores (time series, vector, geo)
- Semantic and metrics layers for BI, ML, and GenAI
- Governance: catalog, lineage, quality, security, observability, auditing, FinOps
Our Approach
- Use‑case–driven modeling Design schemas, data products, and KPIs around the decisions and AI workloads that matter most.
- Lakehouse patterns on AWS Implement scalable architectures on S3, Redshift, Databricks, and related services—covering batch, streaming, and data sharing.
- Operational excellence baked in DataOps, validation, monitoring, and cost controls built into every pipeline, not added after the fact.
Deliverables
- Production data platform blueprint and implementation on AWS
- Domain‑aligned data products with SLAs, ownership, and access policies
- Catalog, lineage views, and quality dashboards
- Runbooks for ingestion, transformation, archiving, DR, and cost management
AI Foundations & LLMOps
Our AI Foundations service establishes the core GenAI building blocks that make agents and applications robust, observable, and reusable.
What We Build
- Knowledge Bases & Context Engineering Document normalization, chunking strategies, semantic views, and context windows tuned to your domain.
- Retrieval & Search Layer Embeddings, semantic indexing, hybrid search, re‑rankers, retrieval caches, query rewriting, and enterprise search.
- Core RAG & Agent Primitives RAG pipelines, tool calling, planning, prompt templates, session context, memory, and multi‑agent collaboration.
How We Manage It
- LLMOps & Observability Serving, logging, metrics, traces, evaluation harnesses, and regression tests across prompts, models, and tools.
- FinOps & Performance Model selection, SLM vs LLM trade‑offs, caching, quantization, offline inferencing, and cost–latency optimization.
- Safety & Governance System prompts, policy enforcement, chain‑of‑thought controls, safety tuning, dataset labeling, and source citations.
Deliverables
- AI Foundation architecture covering knowledge bases, retrieval, and agent primitives
- Deployed RAG and search services with monitoring and caching
- LLMOps playbook: evaluations, releases, rollback, and FinOps practices
- Safety and governance framework for prompts, policies, and content controls
AI Agents & Applications
Our AI Agents & Applications service turns your foundations into production experiences that automate real work.
What We Build
- Workflow agents that orchestrate tools, APIs, and systems
- QA and testing agents (e.g., BDD test agents) that accelerate release cycles
- Security and risk assistants (e.g., threat‑modeling copilots) that guide engineers
- Copilots for analytics, operations, and customer‑facing teams
Our Approach
- Start from the workflow Map the human process first, then design agent roles, tools, and guardrails.
- Integrate with your stack Connect to ticketing, CI/CD, monitoring, CMDBs, data platforms, and internal APIs using AWS‑native services where it makes sense.
- Design for human oversight Use advisory, review, and approval modes before full automation; expose clear traces of how the agent reached each action.
Deliverables
- Production‑ready LLM agent(s) integrated into your environment
- Tooling, prompt, and policy definitions with version control and tests
- Dashboards for usage, quality, and impact metrics
- Runbooks for tuning, retraining, and expanding to new workflows
