Atlysys · est. 2026 Intelligent Systems.
Infinite Possibilities.

Core services0
Pilot delivery2–3WK
ScopeFixed
Code ownership0%
Built with operational clarity
PythonLangGraphLangChainAWS · CDK Google CloudAzureBedrockOpenAI AnthropicVector StoresAgentic RAGMCP LangFuseCloudWatchLambda · Cloud Run PythonLangGraphLangChainAWS · CDK Google CloudAzureBedrockOpenAI AnthropicVector StoresAgentic RAGMCP LangFuseCloudWatchLambda · Cloud Run
01 · What we build

End-to-end AI systems,
built to operate.

Not demos. Not notebooks. Operational systems your team can run, extend, and monitor — with observability built in from the start.

SVC · 01

AI agents & automation

Multi-agent orchestration that reasons, retrieves, and acts across your tools and data — with full traces and cost-per-run visible to you.

LangGraphMulti-agentMCPWorkflow
SVC · 02

Generative AI solutions

LLM integration, agentic RAG with citation grounding, fine-tuning, and AI product development — concept to production-ready systems.

RAGLLMsFine-tuningVector
SVC · 03

Cloud infrastructure

Serverless & container architectures across AWS, GCP and Azure. IaC with CDK or Terraform, full observability, cost tracking per workflow.

AWSGCPAzureIaC
SVC · 04

SaaS development

End-to-end architecture, backend, frontend, and deployment. We build the full product — not just the AI layer bolted on top of it.

Full-stackAPIsDeploy
SVC · 05

Web applications

Modern, performant web products with resilient APIs, clean UI, and the engineering rigor to support real user load — not landing pages.

ReactPythonREST · GraphQL
SVC · 06

AI consulting

AI readiness audits, workflow analysis, and pilot scoping. We find where AI moves the needle for your business — before you spend a dollar building.

AuditStrategyPilot scoping
02 · Example implementation

What a real
engagement looks like.

A representative workflow from a recent engagement — the type of system we scope, build, and hand off within a fixed-price pilot.

Domain · BFSI · Document Intelligence

Loan Underwriting Document Intelligence Pipeline

A mid-size lending company needed to reduce manual document review time and improve consistency across underwriters. We built a multi-step pipeline that extracts, classifies, and summarises loan documents — with source citations and full audit logs for every decision.

Architecture overview
Input PDF / Docs
Parsing Document
Splitter
LLM Agent Extract
& Classify
RAG Grounded
Retrieval
Output Structured
Report
Stack Python LangGraph Anthropic Claude AWS Lambda S3 LangFuse RAG + Citations
~70% Reduction in review time
100% Source-cited outputs
3 wk Kickoff to deployment
Full Audit trail & cost visibility
03 · How we work

From discovery to deployment
in weeks, not quarters.

A fixed-scope, fixed-timeline engagement. We compress months of discovery and iteration into a focused, measurable delivery loop.

01 · DISCOVER

Understand the workflow

We audit your current process, identify automation opportunities, and scope a precise pilot — no vague statements of work.

02 · BUILD

Ship in 2–3 weeks

Fixed scope. We build a working system with full observability — cost per run, latency, agent traces visible to your team.

03 · DEPLOY

Production from day one

Cloud-hosted, monitored, documented. You own the code. We hand off a system your team can operate and extend without us.

04 · SCALE

Expand what works

Once the pilot proves value, we scope the next layer — more agents, more integrations, more workflows automated end-to-end.

04 · Who we serve

Designed for document-heavy,
high-stakes environments.

Where AI has the highest ROI — and the lowest tolerance for error. We build with source citations, structured audit logs, and access controls in mind.

01

BFSI & Fintech

KYC automation, document review, fraud signal extraction. Traceable pipelines with structured audit logs and explainable outputs.

Primary
02

Healthcare

Clinical document intelligence, diagnostic RAG, prior auth automation. Designed for sensitive data environments with retrieval-grounded outputs.

Primary
03

Legal & Professional

Contract review agents, document intelligence, intake automation. Billable-hour leverage through AI-assisted workflows.

Secondary
04

Manufacturing & Logistics

Predictive maintenance pipelines, supply chain agents, operational data intelligence at scale across plants and lanes.

Secondary
05 · The studio

Built by engineers
who ship.

Atlysys is a founder-led engineering studio. We've spent years shipping production systems, cloud infrastructure, and AI-powered workflows — not advising on them from the sidelines.

/ Principle 01

Operational before elegant

We build systems that work under real load first. Architecture decisions start with observability, cost visibility, and failure modes — not whiteboard abstractions.

/ Principle 02

Retrieval before generation

LLMs should reason over your data, not hallucinate around it. Every output is grounded in retrieved context, with sources traceable back to the originating document.

/ Principle 03

Human-in-the-loop by default

Automation should augment decisions, not replace oversight. We design workflows with review checkpoints, override mechanisms, and transparent reasoning built in.

06 · Technology

A focused stack.
Chosen for production, not demos.

We work with tools we can operate at scale — not whatever is trending this week. Cloud-agnostic, observability-first, and pragmatic.

Core
Python LangChain LangGraph Agentic RAG MCP
Cloud
AWS Google Cloud Azure CDK Terraform Lambda · Cloud Run API Gateway S3 · GCS
Models
OpenAI Anthropic Bedrock Vertex AI Azure OpenAI Open-source LLMs
Observability
LangFuse CloudWatch OpenTelemetry Datadog Sentry
Frontend
React Next.js TypeScript Tailwind
07 · Why Atlysys

We don't ship demos
and call them products.

Six principles we follow on every engagement — the difference between a notebook experiment and a system your team can actually run.

/ 01

Observable from day one

Every system runs on real cloud infrastructure — not a local script. Hosted, monitored, logged, and documented before handoff.

/ 02

Agentic RAG with citations

Answers grounded in your documents, with cited sources and visible reasoning traces. No black-box outputs in regulated environments.

/ 03

Full observability

Cost per agent run, latency breakdowns, complete trace visibility via LangFuse, OpenTelemetry, and your existing dashboards.

/ 04

MCP-native integrations

Agents that work across Slack, Notion, Jira, and your internal tools — not isolated systems your team has to work around.

/ 05

Audit-ready by design

Structured for BFSI and healthcare workflows — audit trails, access controls, and data isolation considered from the architecture stage.

/ 06

You own everything

Full code handoff. No vendor lock-in to Atlysys. Your team can maintain, extend, and operate the system independently.

08 · AI Pilot Program

One working system.
Three weeks. Fixed price.

The lowest-risk way to see what AI automation actually does for your operation — before committing to a larger engagement.

Pilot · USD · Fixed scope
$3,500
Typical engagements: $3,500–$8,000

One deployable AI workflow,
delivered in 3 weeks.

We scope it together in a free 30-minute discovery call, agree the success criteria, and ship a system you can run on your own cloud. Then we hand over the keys.

  • One deployable AI workflow built for your specific use case
  • Cloud-hosted & deployed on AWS, GCP, or Azure — not a local demo
  • Full observability — cost, latency, and agent traces from day one
  • Complete code handoff — you own it entirely, no lock-in
  • Three-week delivery with async progress updates
  • One round of refinements post-delivery, included
Pilot scoped in a free 30-min discovery call first.
Book a discovery call
09 · Common questions

Before the
discovery call.

Technical, direct answers — no marketing copy.

You do. Full code handoff, no lock-in. Every system is delivered with documentation, infrastructure-as-code, and clear runbooks so your team can operate and extend it independently. We retain no rights to the code whatsoever.
AWS, Google Cloud, and Azure — or your existing cloud account. We're cloud-agnostic and deploy into your own account so you control the infrastructure and costs directly. We can also work with multi-cloud setups where that's already your standard.
Both. The pilot format works well for teams that want to validate before committing to a larger engagement — whether that's a Series A startup moving fast or an enterprise team running a proof-of-concept alongside an existing system.
Yes. We're accustomed to working within existing VPCs, IAM policies, and deployment pipelines. We'll surface any infrastructure constraints in the discovery call and build accordingly — not around them.
Data never leaves your cloud account unless you explicitly require otherwise. We build with access control, data isolation, and minimal persistence as defaults. For document-heavy workflows, we design retrieval pipelines that keep source data in your environment throughout.
We audit the workflow you've described, identify the highest-value automation layer, and send you a written pilot scope within 48 hours. The scope includes the problem, proposed solution architecture, delivery timeline, and success criteria — no vague statements of work.
We'll tell you. Part of the discovery call is an honest assessment of whether AI actually moves the needle for your specific workflow — or whether a simpler automation or process change would be more effective. We'd rather give you that answer in 30 minutes than build something that doesn't hold up.
The pilot includes one round of refinements post-delivery. Beyond that, ongoing retainer support and extended engagements are available. Every system is handed off with documentation sufficient for your team to maintain it independently — we don't engineer dependencies into the engagement.
10 · Get started

Let's find the
workflow worth
automating first.

Thirty minutes. No pitch deck. We'll audit one process and tell you honestly whether AI moves the needle for it — or whether it doesn't.