AI Tech Stacks AI Tech StacksDesign · Build · Evaluate · Operate
Applied AI engineering & ML platforms

A demo is not a product. We build the system around the model.

AI Tech Stacks takes AI features from prototype to production — retrieval, orchestration, evaluation, and inference infrastructure — and keeps them accurate once real users arrive.

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RAGgrounded on your own data
Evalsquality gates in every release
MLOpsversioned models and prompts
24/7monitored inference

Building across the modern AI stack

What we do

Four practices that get AI into production

The hard part of applied AI is rarely the model call. It is retrieval quality, evaluation, cost, and everything that has to keep working the week after launch.

AI Product Engineering

Ship AI features users actually trust. We design the interaction, the fallbacks, and the failure states — not just the happy-path prompt.

  • AI feature discovery & feasibility spikes
  • Copilots, assistants, and search experiences
  • Agentic and tool-use workflows
  • Human-in-the-loop and review interfaces

RAG & Knowledge Systems

Answers grounded in your own content, with the retrieval quality measured rather than assumed. Most "the model is wrong" problems are retrieval problems.

  • Ingestion, chunking, and embedding strategy
  • Hybrid search, reranking, and citations
  • Vector store selection and tuning
  • Freshness, permissions, and tenant isolation

Evaluation & Model Operations

An AI system that is not measured is not maintained. We build the eval harness first, then treat quality regressions like any other broken build.

  • Golden datasets and LLM-as-judge suites
  • Regression gates wired into CI/CD
  • Prompt, model, and dataset versioning
  • Drift, hallucination, and cost monitoring

AI Platform & Data Foundations

Inference infrastructure, GPU cost control, and the data plumbing every model depends on — because AI runs on whatever your data platform already is.

  • Model serving, autoscaling, and GPU scheduling
  • Feature stores and training/serving parity
  • Lakehouse and streaming pipelines
  • Governance, PII handling, and audit trails
How we work

Evaluate before you scale

AI projects fail late, when quality quietly degrades under real inputs. We front-load measurement so problems surface in week two rather than month six.

Frame

Define the task precisely and decide whether it needs AI at all. Half the wins come from scoping the problem down until a simple approach can win.

Prototype

The simplest thing first — one model call before a pipeline, a pipeline before agents. Complexity has to be earned by the task, not assumed.

Evaluate

Build the golden dataset and scoring harness, then set the numbers a release must beat. This is the step most teams skip and later regret.

Operate

Ship behind guardrails with cost and quality dashboards, then keep re-scoring production traffic as inputs and models change underneath you.

Why AI Tech Stacks

Engineers who have run AI in production, not just in notebooks

We are a small senior team that treats AI systems as software: versioned, tested, observable, and costed. You get the people doing the work, and the reasoning behind every model and architecture choice written down.

  • Model-neutralWe pick models on evals against your data and budget — and we will tell you when a smaller or cheaper one wins.
  • Evaluation-firstNo feature ships without a measurable quality bar, so "it feels worse this week" becomes a number instead of an argument.
  • Cost visible from day oneToken, GPU, and retrieval costs are modelled during design, not discovered on the first full month's bill.
  • Your stack, your reposPipelines, prompts, and infrastructure live in your source control and run in your cloud accounts.

Engagement models

Pick the shape that fits the problem.

  • AI feasibility spike (2–3 weeks)
  • Prototype to production build
  • Evaluation harness & audit
  • Embedded AI squad

What you get

Concrete artifacts, every phase.

  • Architecture decision records
  • Golden datasets and eval suites
  • Deployed pipelines and IaC
  • Cost model and quality dashboards
Architecture

Patterns we build with

Most production AI systems are one of a handful of shapes. Choosing the simplest one that meets the requirement is the single highest-leverage decision in the project.

Retrieval-Augmented Generation

Ground responses in your own documents at query time instead of relying on training data. The default starting point whenever answers must cite something real.

Pipelines & DAGs

Retrieve, rerank, generate, validate — a fixed sequence with predictable control flow. Easy to debug and cheap to run, at the cost of flexibility.

Agentic & tool use

A model plans and calls tools across multiple steps. Genuinely powerful for open-ended tasks, and the pattern most often reached for too early.

Event-driven inference

Queue-triggered scoring for batch enrichment, moderation, and anything where the caller does not need a synchronous answer.

Fine-tuning & distillation

Worth the cost once requirements are stable and prompting has plateaued — often to make a smaller, cheaper model match a larger one.

Classical ML, still

Fraud scoring, forecasting, and ranking are frequently better served by gradient boosting than by an LLM. We say so when that is the case.

Technology

Deep in a focused stack, fluent in the rest

Model providers change every few months, so we build the layers around them to be swappable — an evaluation harness and a clean interface matter more than any single vendor choice. When your context calls for something outside this list, we say so.

Read our engineering notes

Models & providers

ClaudeAmazon BedrockAzure OpenAI Vertex AIOpen weightsEmbeddings Rerankers

Orchestration & serving

LangGraphMCPvLLM RayKubernetesTriton

Data & retrieval

pgvectorOpenSearchPinecone DatabricksKafkaAirflow

Evaluation & observability

MLflowLLM-as-judgeOpenTelemetry PrometheusGrafanaCost tracing
Insights

Engineering notes from our practice

Practical write-ups on the decisions behind production AI systems — architecture, model choice, and the infrastructure they all sit on.

Tell us what you are trying to make the model do.

Send a short description of the task, your data, and where the current approach breaks down. We will reply with an honest read on whether AI is the right tool — and what it would take.

Email welcome@aitechstacks.com Review our services