GenAI Studio

Ship reliable generative AI features with RAG, prompt orchestration, evaluations, and guardrails—production-ready for .NET teams.

From Idea to Impact

Move fast from prototypes to real features: prompt flows, tool calling, and retrieval patterns that evolve as your use cases mature.

Trust Your Answers

Retrieval-augmented generation with source grounding, document lifecycle controls, and observability for recall/precision trade-offs.

Safety by Design

Guardrails for input/output filtering, PII handling, and policy enforcement. Keep humans-in-the-loop where it matters.

How GenAI Studio Works

The studio assembles the building blocks for production-grade generative apps—so your team can focus on quality and outcomes, not glue code.

  1. Ingest & Index — Clean, chunk, and version data; build embeddings with policies for freshness and access.
  2. Retrieve & Orchestrate — Compose prompts and tools; select models; ground responses with relevant context.
  3. Evaluate — Track faithfulness, helpfulness, and latency; run offline and shadow tests before release.
  4. Govern — Apply guardrails, red-teaming, and audit trails; iterate safely with feature flags.
  • RAG patterns (hybrid, re-ranking, query rewriting)
  • Prompt graphs with tool/function calling
  • Metrics for quality, cost, and performance
GenAI Studio orchestration

Capabilities

Everything you need to build grounded, observable, and governable AI features—without writing the same scaffolding twice.

Prompt Orchestration

Reusable prompt components, system templates, and routing for multi-turn tasks and tool use.

Retrieval

Embeddings with hybrid search, filters, and freshness controls. Support for doc versions and lineage.

Evaluations

Quality & safety checks before release. Compare prompts, models, and retrieval settings with offline runs.

Guardrails

Input/output filters, PII detection, policy prompts, and configurable escalation for sensitive flows.

Integration

Clean .NET APIs, background workers for pipelines, and patterns for MVC, SPA, and BFF endpoints.

Observability

Traces for each generation step, cost & latency budgets, and dashboards for production health.

Secure by Default

Run generation behind a Backend-for-Frontend (BFF) so secrets and tokens stay off the client. Apply per-route policies for models, tools, and data scopes.

  • Server-side key handling and rate controls
  • Role- and tenant-aware retrieval filters
  • Audit trails for prompts, context, and outputs
Operational Rigor

Treat prompts and retrieval like code: version, test, and release with confidence. Measure quality alongside cost and latency.

  • Feature flags for safe rollouts
  • Shadow traffic & backtests
  • SLOs for accuracy and responsiveness

Build AI features your users can trust

We’ll help you design, evaluate, and ship GenAI with the right guardrails—aligned to your data, users, and goals.

Contact us