AI right stack.

Help choosing and assembling the AI infrastructure your team can actually operate — model providers, vector stores, observability, evaluation, and deployment — picked deliberately rather than pieced together from blog posts.

Overview

What it means in practice.

The AI tooling landscape changes every quarter and most of it is mid. We help teams cut through the noise: pick the LLM provider that fits your latency and cost profile, the vector database that matches your scale, the orchestration framework that suits your complexity, and the evaluation tooling that catches regressions before users do.

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What we deliver

Capabilities & deliverables.

Every engagement gets shaped to fit, but these are the building blocks we rely on.

01

Model Provider Selection

OpenAI, Anthropic, Google, open-weight models — picked by use case, latency, cost, and data-residency requirements. Not just whoever's currently trending.

02

Vector Database Architecture

Pinecone, Weaviate, pgvector, Qdrant — chosen by query volume, filter complexity, and operational maturity of your team.

03

Orchestration Frameworks

LangChain, LangGraph, LlamaIndex, or hand-rolled — picked deliberately. We know when frameworks help and when they get in the way.

04

Evaluation & Observability

LangSmith, Weights & Biases, Helicone, custom dashboards. You can only improve what you measure.

05

Cost Management

Token tracking, model routing, caching strategies, and budget alerts. AI spend predictability so you don't get a surprise five-figure invoice.

06

Deployment Patterns

Cloud, on-prem, hybrid, or edge — picked by privacy, latency, and operational fit. Including private model hosting via vLLM or Ollama when needed.

LangChain LangSmith Pinecone Weaviate vLLM Ollama Bedrock Vertex AI
Why it works

The SD Technolabs approach.

Two decades of engineering practice, sharpened by the realities of production AI.

01

Vendor-neutral guidance

We have no commission relationships with AI vendors. Recommendations come from operational experience, not affiliate incentives.

02

Operability matters

We pick stacks your team can run, debug, and extend. Brilliant tooling that nobody on staff understands becomes a liability fast.

03

Cost-aware architecture

Token costs and infrastructure expenses modeled before architecture decisions. Pretty demos that bankrupt the unit economics get rejected.

04

Migration paths considered

We don't lock you to one vendor. Architecture patterns assume model providers will change in eighteen months — and plan for it.

Ready to start something good?

Let's discuss how this fits your business. We reply within one working day.

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