What are data silos and why do they hurt your business?
In a typical company, data about the same customer lives in several places at once: the CRM knows the contact history, the accounting system knows the invoices, the e-commerce platform knows the orders, and a spreadsheet on someone’s laptop holds the “real” status of the case. None of these sources knows about the others.
The result is always the same. An employee manually copies data from one system to another, sometimes several times a day. Copying introduces typos, skipped line items, and mismatches between systems. Nobody has a full picture of a customer or a process, because you’d need to open four applications at once and piece it together yourself.
This costs money - sometimes more than companies suspect. Not just in hours spent clicking between tabs, but in errors that only surface when a customer complains or when the books are closed for the month.
Why are integrations the foundation for AI agents?
Before talking about AI agents, one thing needs to be said plainly: an agent without access to data is blind. It can run on the best language model in the world, but if it can’t see stock levels, a customer’s CRM history, or an invoice status in the accounting system, it will guess or ask a human for manual input - which means it hasn’t automated anything.
That’s why at Basalt AI we treat integrations not as an add-on, but as the layer agents are built on. A quoting agent needs to see pricing in the ERP. A lead-handling agent needs to write leads directly into the CRM, not a side spreadsheet. A warehouse agent needs a live stock count, not a snapshot from last week. A well-designed integration is the difference between an agent that genuinely works 24/7 and a chatbot that just answers questions nicely.
How do we approach integrations?
Audit of existing systems
Before we write a single line of integration code, we map every data flow in the company: which systems are in use, who relies on them, where data is entered manually, and where it already flows automatically. This step also shows where integration actually pays off - and where it would just be an expensive toy, because not every data flow needs to be automated.
Data mapping
The next step is figuring out how the same concepts are named across systems - a “customer” in the CRM isn’t always the same record as a “counterparty” in the accounting system. We map fields, formats, and business rules (for example, what happens when two systems report conflicting order statuses), so the integration untangles the chaos instead of carrying it from one system into another.
Integration layer
Instead of connecting systems point-to-point - which, with five tools, means dozens of individual connections to maintain - we build a central integration layer through which all data flows. Adding another system later doesn’t mean rewriting existing connections, just plugging one new endpoint into the shared layer.
Iterative rollout
We roll out integrations in stages, typically a 2-4 week cycle for the first set of connections. We start with the process that hurts the most and pays back fastest, launch it, measure the effect, and only then move on. That way the company sees results early, and we can correct course before investing time in less important connections.
Monitoring and alerting
Every integration is monitored from day one. When a connection breaks - a third-party API changes its response format, an accounting system has downtime - we know before anyone in the company notices, and we act before the error spreads to other systems.
What systems do we actually integrate?
- ERP: SAP, Microsoft Dynamics, Oracle
- CRM: Salesforce, HubSpot, Pipedrive
- E-commerce: Shopify, WooCommerce, Magento
- Accounting and invoicing: local and international systems, depending on the client’s stack
- Any external API: REST, GraphQL, webhooks - if a system has a documented API, we can connect to it
What this looks like in practice - case study
A good example of our “process first, technology second” approach is our work with Alderson Labs. Instead of jumping straight into integrations and automations, we started with a process audit and calculated the ROI for each process individually - so we’d know which data flows were actually worth automating and which were better left alone. Only after that analysis did we implement the solutions, exactly according to the agreed plan, without scope creep along the way. Read the full breakdown in the Alderson Labs case study.
How much does a system integration cost?
Integrations with your CRM, ERP and external APIs are part of the Pro package of each of our AI agents (from 1,290 to 1,990 PLN/month depending on the agent) - together with priority support and monthly optimization. If you need integration work beyond the standard scope - for example, connecting to a system we don’t yet support, or an integration without an accompanying agent - we price it individually after an audit.
Every integration project is different, because every company runs a different set of systems with different priorities. Get in touch - the first conversation and initial audit are free.
The technical backbone of our integrations
We build the integration layer on infrastructure that has to stay stable around the clock - after all, the AI agents running on it don’t take breaks. To host and secure API connections we rely on edge infrastructure such as Cloudflare, and the language models powering the agents that read and process data from these integrations are built on Claude from Anthropic. It’s the combination of a stable network layer with a model that actually understands business context - not just one that moves data from point A to point B.