Single integration
Two systems, one data flow, with monitoring and retries.
The ERP does not know what happens in the store, the CRM cannot see billing, and someone on the team acts as a bridge copying data by hand. That can be connected.
2–6 weeks per integration depending on complexity · 10 years connecting enterprise systems · 0 integrations without monitoring: if it fails, someone finds out
Built on
Ninety seconds: who we are, how we work and what you get at the end.
An email, an order or an alert comes in. The agent queries your systems, decides by your rules, asks for approval when needed and executes. Pick a case and watch it run.
Example: a customer asks what their policy covers. The system answers with the exact page of the contract and opens the case.
context
product = motor_premium
84 pp → 612 chunks
Asked for approval · human
log · 5 steps · who and when
Swipe to follow the flow →
01
So an order draws down stock, generates the invoice and notifies, without anyone touching anything.
02
So sales sees the real account status without asking accounting for a report.
03
When the old system works but exposes nothing, and replacing it is not an option.
04
So what arrives via web, WhatsApp or email lands structured where it needs to be.
05
When your customers or partners need to consume your information in a controlled, documented way.
06
Exposing your systems as standardized tools so any AI agent can use them without brittle, bespoke integrations.
01
The hard part
Connecting two systems on the happy path is days of work. What decides whether the integration survives is everything else: what happens when the other system is down, when it returns data in an unexpected format, when the same event arrives twice, when a full day has to be reprocessed without duplicating records.
That is why everything we build carries idempotency, retries with backoff, a dead-letter queue for what cannot be processed, and monitoring with alerts. An integration that fails silently is worse than none, because the business keeps operating on incomplete data without knowing it.
Two systems, one data flow, with monitoring and retries.
Three or four systems synchronized with business rules between them.
Documented interface for third parties, with authentication, rate limits and versioning.
Your systems exposed as tools for AI agents.
Applied where there is volume and stable rules, not where there is expectation.
The system around the model, not just the model.
LangGraph orchestration, durable state and human approval on steps with consequences.
iOS and Android, with inference on the correct side.
Key takeaways
Connecting two systems on the happy path is days of work; what decides whether the integration survives is what happens when the other system is down or the same event arrives twice.
That is why Quarl builds with idempotency, retries with backoff, a dead-letter queue for what could not be processed, and monitoring with alerts.
An integration that breaks without warning leaves the business running on incomplete data for days.
Model Context Protocol servers expose systems as standardized tools, so any AI agent consumes them without brittle bespoke integrations.
An AI system is worth what its sources are worth. These are the standard connectors; anything with an API or a database connects the same way, and what has no API is handled by file.
SAP
Enterprise ERP
Oracle
ERP and database
NetSuite
Cloud ERP
Salesforce
CRM and service
HubSpot
CRM and marketing
PostgreSQL
Database and pgvector
Microsoft SQL
Database
Snowflake
Data warehouse
BigQuery
Google data warehouse
Databricks
Data platform
Redshift
AWS data warehouse
Synapse
Azure data warehouse
Supabase
Managed Postgres
Workday
Payroll and HR
QuickBooks
Accounting
Sage
Accounting and ERP
Xero
Cloud accounting
Shopify
Catalogue and orders
WooCommerce
Catalogue and orders
Magento
Catalogue and orders
Stripe
Payments and subscriptions
Google Drive
Documents and folders
CSV y Excel
Flat files
Nothing in this category
We built and operated the assistant for a loyalty platform serving more than two million active users across ten countries.
We built the full pipeline: document ingestion and normalization, chunking, embedding generation, vector store on Azure AI Search and Pinecone, and retrieval with grounded generation on LangChain.
We held it above 99% availability for three years.
RAG systems →There are ways out and they depend on the case: scheduled file exchange, direct database reads when the vendor allows it, or an intermediate layer exposing what the old system does not. What we do not recommend is automating the graphical interface: it works until the vendor changes a screen and everything breaks without warning. If your system is completely closed, we tell you on the first call.
The integration assumes it from the design: retries with backoff, a queue for what could not be processed, and an alert when something has been pending too long. When the system returns, the backlog is processed without duplicating records. That behavior is what separates a production integration from a script that worked once.
It means processing the same event twice produces the same result as processing it once. It matters because in distributed systems events do get duplicated: a retry, a webhook delivered twice, a reconnection. Without idempotency that means double invoices or inventory drawn down twice. It is one of those things that never shows up in the demo and always shows up in production.
Model Context Protocol is a standard for exposing systems and data as tools a language model can use. Instead of writing a bespoke integration for every agent you build, you expose your systems once and any agent consumes them. If you are planning several agents, starting here saves a lot of repeated work.
Infrastructure is usually low: roughly USD 20 to 80 monthly for typical volumes. The real cost appears when the system on the other side changes its API, and that happens. That is why we document external dependencies and leave monitoring configured, so the change gets detected the same day rather than three weeks later.
Yes, and it is a more frequent request than you would think. OpenAPI documentation generated from the code, with examples and a sandbox, plus versioning if needed. When third parties consume the API, this dramatically reduces support load.
Yes: electronic invoicing, local payment gateways, shipping platforms and regional ERPs. When a system we do not know shows up, the first thing we check is what it exposes and how stable its interface is, and we assess that before quoting so we are not promising on assumptions.
It gets fixed, it gets built, or it is not worth it. And if the diagnostic does not reach three actionable findings, it is not charged.
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