AI Engineering: Production-Grade Generative AI at Enterprise Scale
DS Stream designs, builds, and operates production-grade generative AI systems — from LLM-powered agents and RAG architectures to multi-agent orchestration. We embed governance, evaluation harnesses, and observability so your AI delivers measurable value, not just demos.
Engineering-grade GenAI: agents, RAG, evals, and governance
We deliver production GenAI systems with proper evaluation harnesses, observability, cost controls, and human-in-the-loop guardrails — built to scale beyond pilot.
Why Most Generative AI Projects Stall After the Demo
GenAI demos are easy; production-grade systems are hard. Most projects stall because of weak evaluation, no observability into LLM behavior, prompt drift, cost spirals, and missing governance. Without engineering discipline, hallucinations and incidents block scale.
AI Engineering Capabilities
Reference architecture for RAG, agentic systems, and multi-model orchestration with vendor flexibility.
Automated, repeatable evaluation of LLM outputs across accuracy, safety, and business KPIs.
Full observability into prompts, retrievals, tool calls, latency, and cost — with replay and debugging.
Input/output filtering, PII redaction, prompt injection defenses, and content policies enforced consistently.
Model routing, caching, prompt optimization, and fine-tuning strategies that cut cost per query by 50–80%.
How We Build Your AI Engineering Practice
Use case scoping, architecture design (RAG vs. agent vs. fine-tune), data readiness, and evaluation criteria definition.
Production-ready MVP with evaluation harness, observability, and guardrails. Real user testing.
Multi-use-case rollout, cost optimization, governance integration, knowledge transfer to internal team.
Continuous evaluation, prompt tuning, model upgrades, and new capability rollout on the platform.
Business Impact
60% reduction in time-to-resolution for support queries with AI agents.
50–80% lower cost per query through model routing, caching, and prompt optimization.
Production-grade quality with automated evaluation catching regressions before they reach users.
Full traceability of every AI decision for compliance and debugging.
Who This Is For
Use Cases for AI Engineering
We deliver AI Engineering engagements across industries with deep vertical expertise.
Most Common Questions
AI Engineering is the discipline of making AI systems work reliably in production — evaluation, monitoring, cost, safety — distinct from research on new models.
We are model-agnostic — OpenAI, Anthropic Claude, Google Gemini, open-source (Llama, Mistral), and fine-tuned variants. We help pick the right model per use case.
RAG with grounding, citation requirements, evaluation harnesses that catch regressions, and human-in-the-loop for high-stakes decisions.
Per-query cost varies widely. We optimize aggressively — routing, caching, prompt compression — typically delivering 50–80% reduction vs. naive usage.
When justified — for specialized domains or to reduce cost. We start with prompting/RAG, then fine-tune only when benchmarks show clear ROI.
Ready to Industrialize Your AI Engineering Practice?
Book a free 30-minute review. We will assess your current state, identify the highest-impact wins, and outline a clear path to production-grade AI Engineering delivery.
GenAI Strategy Workshop
Two-day workshop to identify highest-impact GenAI use cases and define success criteria.
Reference Architecture
Design and deploy RAG/agent reference architecture on your cloud with eval harnesses.
First Production Use Case
End-to-end delivery of first production GenAI use case with measurable business KPIs.