Agentic AI systems and cloud platforms, engineered for production
We build Agentic AI systems & ship multi-cloud applications & infrastructure on AWS, Azure & GCP - with evals, guardrails, IaC, observability, and cost controls in from day one. No demo-ware. No lock-in. 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
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.
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.
Product platform extension with AI features, supporting infrastructure, and engineering practices. AI capabilities shipped behind feature flags into an existing product.
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.
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.
Long-running engineering partnership across multiple iterations - backend, frontend, infrastructure, and QA practices. Flexible capacity that scaled with roadmap and budget.
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
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.

Stack
Technologies we work with
Deep experience across the agentic AI stack, three major clouds, and the full product engineering surface.
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.
Multi-cloud fluency across Bedrock, Azure AI Foundry, Gemini Enterprise Agent Platform, EKS/AKS/GKE, serverless, and managed data services.
Terraform, OpenTofu, Pulumi, CDK, Bicep. Version-controlled, reproducible, reviewable infrastructure.
EKS, AKS, GKE, and self-managed clusters. Helm, ArgoCD, service meshes, and platform engineering for multi-team orgs.
AWS Lambda, Azure Functions, Cloud Run. EventBridge, Pub/Sub, Service Bus, and Step Functions for resilient asynchronous workflows.
OpenTelemetry, Prometheus, Grafana, Datadog for cloud and application observability. Braintrust and Langsmith for AI evals and tracing. One pane of glass.
Python (FastAPI, Django, Flask), .NET, Node.js, Go, and Java/Spring. REST, GraphQL, and event-driven architectures.
PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, plus vector stores (pgvector, Pinecone, Weaviate). Apache Iceberg lakehouses, dbt & Spark for pipelines.
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.

