Agentic AI systems and cloud platforms, engineered for production

Quabyt: agentic AI and multi-cloud, done right.

What clients say

Real feedback from founders and CTOs who trust Quabyt with production work

What we do

Two pillars, one delivery team

We pair deep agentic AI engineering with multi-cloud fluency so the systems we ship are as reliable as the infrastructure underneath them.

Agentic AI & GenAI Systems

Autonomous agents with tool use and human-in-the-loop, plus RAG pipelines and fine-tuned models. Built on LangGraph, CrewAI, OpenAI Agents SDK, and Claude Agent SDK - shipped with eval harnesses, guardrails, and cost controls wired in before launch.

Cloud Engineering on AWS, Azure & GCP

Multi-cloud by choice, not by accident. Well-architected infrastructure with Terraform/OpenTofu, Kubernetes, serverless, and FinOps cost controls.

Data & ML Platforms

Vector stores, model-serving infrastructure, data pipelines, and lakehouses tuned for the throughput and latency your AI and analytics workloads actually need.

Full-Stack Product Engineering

Web, mobile, and backend delivered end-to-end - so your agents, APIs, and user-facing product ship as one coherent system, not a handoff.

DevOps, MLOps & Evals

CI/CD, IaC, eval pipelines, model versioning, and observability. Ship faster with confidence that regressions get caught before your users do.

How we build

The engineering rigor behind every system we ship

This is the part most vendors call 'phase two.' We are a partner who calls it day one.

Design for failure modes, not demos

Before we write the happy path, we map what goes wrong - hallucinations, tool failures, region outages, cost blowouts, edge cases - and design the system around those constraints.

Evals before features

Every AI system gets a versioned eval harness on day one. Accuracy, latency, cost, and safety are measured - not vibes-checked - from the first commit.

Guardrails as first-class components

Input validation, content filters, rate limits, tool-use policies, and human checkpoints are built into the architecture, not bolted on at the end.

Infrastructure as code, security by default

Everything in Terraform/OpenTofu or equivalent. Least-privilege IAM, encryption at rest and in transit, secrets management, and network policies in from the start - not retrofitted.

FinOps in from day one

Right-sizing, reserved capacity planning, tagging discipline, and cost dashboards for cloud workloads. Token accounting, model routing, and caching for AI workloads. Predictable bills, not surprises.

Engagements

What we've shipped recently

Anonymized snapshots of recent client work. Named case studies available on request.

Agentic workflow platform for a hospitality SaaS

Full-stack build over 10 months: backend, frontend, infrastructure, and AI agents for an industry workflow platform. Multi-step processes with human-in-the-loop checkpoints.

AI capability integration for a venture-backed SaaS

Product platform extension with AI features, supporting infrastructure, and engineering practices. AI capabilities shipped behind feature flags into an existing product.

GenAI feature with eval harness for a product team

RAG pipeline with eval harness and observability integrated into an existing product. Rolled out behind feature flags with cost controls and accuracy monitoring in production.

Multi-tenant SaaS platform with auth & deployment

Architecture, multi-tenant authentication, deployment design, and a clean codebase delivered for a YC-backed founding team. Reduced tech debt from day one with infrastructure organized for scale.

Scaling and infrastructure partnership for a venture-backed startup

Long-running engineering partnership across multiple iterations - backend, frontend, infrastructure, and QA practices. Flexible capacity that scaled with roadmap and budget.

Cost-optimized cloud build for a FinTech product

End-to-end cloud build for an accounting product with deliberate cost engineering across compute, storage, and data. Long-term advisory across feature delivery and infrastructure decisions.

Why teams pick us

A technical partner, not a vendor

Clients stay with us because the code is clean, the decisions are documented, and the team shows up.

We own outcomes, not just tickets

Our clients describe us as an extension of their team: thinking ahead, flagging edge cases, and recommending alternatives instead of just implementing whatever lands in the queue.

Engineering judgment over hype

We will tell you when AI is not the right tool, when RAG beats fine-tuning, and when a boring cloud-native solution is better than the latest framework.

Flexible capacity, startup-friendly economics

We flex up and down with your roadmap and runway. Time & Materials or Fixed Price engagement models, whatever fits the stage you are at.

How a typical engagement runs

Step 1: Discovery & Architecture Review

We map the problem, failure modes, and constraints. You leave with a working architecture, a measurement strategy, and a realistic delivery plan.

Step 2: Design & Measurement Plan

System design, API contracts, infrastructure plan, and the measurement strategy: eval harness for AI behaviour; SLOs and test plans for cloud workloads.

Step 3: Build & Integrate

Iterative delivery with short feedback loops. Agents, APIs, cloud infrastructure, and UI come together as one coherent system.

Step 4: Hardening & Safeguards

Security baselines, IAM, secrets, and observability wired in. Plus content filters, rate limits, and tool-use policies for any AI components. All in before anything sees real traffic.

Step 5: Deploy & Launch

Infrastructure as code, CI/CD, staged rollouts, feature flags, and a runbook. The system ships with everything needed to operate it safely.

Step 6: Evolve & Improve

Ongoing support, eval-driven iteration, and continuous cost and performance optimization. We stay as long as it is useful.

Quabyt engagement process

Stack

Technologies we work with

Deep experience across the agentic AI stack, three major clouds, and the full product engineering surface.

Agentic AI & LLM Stack

LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, LangChain; paired with Claude, GPT, Gemini, and open-weight models. Fine-tuning, evaluation, and responsible deployment patterns.

AWS, Azure & GCP

Multi-cloud fluency across Bedrock, Azure AI Foundry, Gemini Enterprise Agent Platform, EKS/AKS/GKE, serverless, and managed data services.

Infrastructure as Code

Terraform, OpenTofu, Pulumi, CDK, Bicep. Version-controlled, reproducible, reviewable infrastructure.

Containers & Kubernetes

EKS, AKS, GKE, and self-managed clusters. Helm, ArgoCD, service meshes, and platform engineering for multi-team orgs.

Serverless & Event-Driven

AWS Lambda, Azure Functions, Cloud Run. EventBridge, Pub/Sub, Service Bus, and Step Functions for resilient asynchronous workflows.

Observability & Evals

OpenTelemetry, Prometheus, Grafana, Datadog for cloud and application observability. Braintrust and Langsmith for AI evals and tracing. One pane of glass.

Backend & APIs

Python (FastAPI, Django, Flask), .NET, Node.js, Go, and Java/Spring. REST, GraphQL, and event-driven architectures.

Data & Databases

PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, plus vector stores (pgvector, Pinecone, Weaviate). Apache Iceberg lakehouses, dbt & Spark for pipelines.

Frontend & Mobile

React, Next.js, Astro, Vue, Svelte for web; React Native and Flutter for mobile. Modern, fast, accessible UIs with AI features built in where they earn their place.

FAQs

Frequently asked questions

How we work, what we charge, and how the partnership runs day-to-day.

What industries do you primarily serve?

We have particular experience in FinTech, Hospitality, Construction, Healthcare, Insurance, High Tech, and Manufacturing. Our core agentic AI and cloud skills transfer across most B2B and B2C software domains.

What is your typical engagement model?

We offer Time & Materials for iterative and evolving scopes, and Fixed Price for well-defined deliverables. Most clients start with a short discovery or architecture review before scoping the larger engagement.

How do you ensure quality and security?

We layer eval harnesses for AI behavior, automated tests (unit, integration, E2E), code reviews, CI/CD with security scanning, and production observability. Security practices follow least-privilege IAM, secrets management, and encryption by default.

Which cloud platforms do you specialize in?

We have hands-on experience across AWS, Microsoft Azure, and Google Cloud Platform. We recommend the platform that fits your workload, existing footprint, and cost profile - we are not tied to any single vendor.

How do you approach cloud cost optimization?

We design for cost from day one - right-sizing, reserved capacity planning, spot strategies where workloads tolerate them, and tagging discipline so you can see where money goes. For AI workloads we add token accounting, model routing, and caching. We track unit economics, not just monthly spend.

Which agentic AI frameworks do you use?

We work with LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, and LangChain. Framework choice depends on the agent patterns needed - single-agent tool use, multi-agent orchestration, or custom planner/executor loops.

How is intellectual property handled?

The IP for the custom software we build for you belongs entirely to you. This is defined clearly in our contract, including any AI prompts, evals, and model configurations developed during the engagement.

What do you need to provide an estimate?

A short discovery call is usually the best starting point. Useful context: your goals, target users, any existing systems, data volumes, and the outcomes that define success. We can also propose a paid discovery or architecture review if the problem space is still fuzzy.

Have an agentic AI or cloud project in mind?

Tell us what you're trying to build. We'll tell you honestly whether we can help, how we'd approach it, and what it takes to get it into production.