EXPERTISE / AI & DATA

From model potential.
To production systems.

Design AI around your data, your users, and measurable acceptance criteria. Start with feasibility. Engineer for evaluation, integration, and operational ownership.

Conceptual AI computing core in emerald glass

CONCEPT VISUAL / CONNECTING DATA, MODELS, AND APPLICATIONS

ENGINEERING SCOPE

Retrieval-augmented generation

Make enterprise knowledge useful through a retrieval pipeline that can be inspected and evaluated.

  • Document parsing, chunking, embeddings, and indexing
  • Metadata filtering and access-aware retrieval
  • Reranking, grounding, and source references
  • Retrieval and answer-quality evaluation

Agentic workflows

Build bounded automation around explicit tools, clear permissions, and human review where decisions require it.

  • Structured outputs and validated tool inputs
  • Workflow state, retries, and failure handling
  • Human approval points for consequential actions
  • Tracing, token budgets, and latency constraints

Model evaluation & data quality

Define quality before scaling a model-dependent workflow.

  • Representative evaluation datasets and scoring rubrics
  • Regression checks for prompt or model changes
  • Human review workflows and error classification
  • Dataset validation and provenance tracking

Data platforms & MLOps

Connect model-powered applications to dependable data and operational practices.

  • Batch and event-driven data pipelines
  • Versioned datasets and reproducible environments
  • CI/CD, observability, and operational runbooks
  • Cost, reliability, and model performance monitoring
ILLUSTRATIVE REFERENCE ARCHITECTURE

A system you can reason about.

01 / INGESTDocuments & APIs
Validation & normalization
Metadata & access policies
02 / RETRIEVEEmbeddings & index
Filters & reranking
Relevant source context
03 / GENERATEModel orchestration
Structured outputs
Tool boundaries & citations
04 / EVALUATEQuality & safety checks
Trace inspection
Feedback & regression tests

Reference design only. Final architecture depends on discovery, security requirements, and your existing stack.

TECHNOLOGY OPTIONS

Choose the stack for the problem.

Potential technologies to evaluate during solution design; these are not vendor partnership or certification claims.

PythonTypeScriptPyTorchHugging FacePostgreSQL / pgvectorVector searchModel APIsDockerKubernetesOpenTelemetry

Bring us the hard part.

Define your next move