01INT
ERP & finance
NetSuite, SAP, Dynamics and Sage: coding suggestions, reconciliations, variance notes.
LLM.co · Private AI & Custom AI DevelopmentCustom AI integrations
AI output is only useful where work already happens. We connect models to your systems of record so drafts, fields and decisions land in the ERP, CRM, EHR or DMS, not in a chat window.
FIG. — Flight director console, NASA Mission ControlLLM.co
The model is rarely the hard part. The hard part is reading from and writing to systems that were never designed for it, under the same access rules your staff already follow.
We build connectors, event hooks and write-backs with service accounts scoped to the job, and log every read and write so your auditors can follow the trail.
| ERP | NetSuite, SAP, Microsoft Dynamics, Sage Intacct |
|---|---|
| CRM & support | Salesforce, HubSpot, Zendesk, ServiceNow |
| Health | Epic, FHIR, HL7 interfaces |
| Docs & data | iManage, SharePoint, Google Drive, Snowflake, Postgres |
| Identity | Okta, Microsoft Entra ID, SAML/OIDC |
What we build
What this looks like in practice.
02INT
CRM & support
Salesforce, HubSpot, Zendesk: account summaries, reply drafts, case routing.
03INT
Clinical & EHR
Epic and FHIR interfaces for summaries and prior-auth packets, under HIPAA controls.
04INT
Documents & data
iManage, SharePoint, Snowflake and Postgres: retrieval sources and structured write-backs.
AI integration services for your systems of record
AI integration services connect models to the software your business already runs. A model that drafts a reply, codes an invoice or summarizes a chart only saves time if that output lands in the right record, in the right field, under the right user. Integration is the work that makes that happen safely.
LLM.co builds integrations as part of its custom AI development practice. The models run as private AI on infrastructure you control, and the connectors read and write your systems under the same access rules your staff follow.
Why enterprise AI integration is the hard part
Most enterprise systems were not designed for an automated reader and writer. APIs have rate limits, data models differ between modules, and permissions are often coarse. A careless integration can post duplicate entries, overwrite a field a person just changed, or pull more data than the task needs.
We start by documenting each system's API, limits and data model, and the narrowest permissions a service account needs. That review often shows the integration is the largest part of the project, which is why we scope it first.
Integration patterns we use
The pattern depends on how the work starts and how much risk a write carries.
- Event-driven: a new invoice, closed case or signed form triggers AI work through a webhook or queue.
- Scheduled batch: nightly or hourly jobs for reconciliation, tagging and backlog review.
- Draft and approve: AI writes a draft or suggestion into the system for a person to accept.
- Direct write-back: structured fields posted automatically, with idempotent writes and rollback.
- Retrieval source: read-only connectors that feed knowledge search with permissions intact.
Risks and controls
Every connector is a typed, tested adapter with retries, backoff and idempotent writes, so a network error never creates a duplicate record. Each one uses its own scoped service account. Every read and write is logged with who, what and why and shipped to your SIEM.
Where a system has no API, we look for a supported export or database view first. Screen automation is a last resort because it breaks when the vendor changes the interface. Your ERP, CRM, EHR or DMS stays the system of record throughout.
How to judge AI integration services
Good AI integration services make the integration boring to operate. Writes are traceable, failures retry without side effects, and a vendor API change produces an alert instead of bad data. Ask any provider how they handle duplicate writes, credential rotation and rate limits, and whether you will own the connector code. Cost drivers follow the same lines: the number of systems, API quality, data mapping work and approval design.
What you receive
You receive the connector source code, event and queue configuration, service account and permission documentation, tests, and deployment code for your environment. Runbooks cover credential rotation, rate limit tuning and what to do when a vendor changes its API. The code is yours to extend to new systems.
How it works
Four steps, each one reviewed.
01
Read the contract
Document each system's API, limits, data model and the permissions a service account needs.
02
Build connectors
Typed, tested adapters with retries, backoff and idempotent writes.
03
Wire events
Trigger AI work from the events that matter, such as a new invoice, a closed case or a signed form.
04
Log everything
Every read and write recorded with who, what and why, shipped to your SIEM.
Questions
Common questions.
What do AI integration services include?
Documenting each system's API, limits and permissions, building typed connectors with retries and idempotent writes, wiring the events that trigger AI work, and logging every read and write to your SIEM. You receive the connector code, tests, permission documentation and runbooks, delivered to your repositories.
Do you replace our existing systems?
No. We add AI to the systems you already run. Your ERP, CRM, EHR or document management system stays the system of record. AI output appears as drafts, suggestions or structured fields inside those tools, so staff keep working where they already work.
What if a system has no API?
We look for a supported export or database view first. Screen automation is a last resort because it breaks when the vendor changes the interface. If screen automation is the only option, we isolate it, monitor it closely and document the risk so you can decide whether to proceed.
Which systems do you integrate with?
Common targets include NetSuite, SAP, Microsoft Dynamics and Sage Intacct for finance; Salesforce, HubSpot, Zendesk and ServiceNow for CRM and support; Epic and FHIR or HL7 interfaces for health; and iManage, SharePoint, Google Drive, Snowflake and Postgres for documents and data. Identity runs through Okta, Entra ID or SAML/OIDC.
How long does an AI integration project take?
A two-week discovery sprint documents each system and its permissions. A focused first system with its integrations usually reaches production in eight to twelve weeks. The number of systems, the quality of their APIs and your change-approval process are the main factors.
Is it secure to connect AI to our ERP or EHR?
It can be when the controls are designed in. Each connector uses a scoped service account, models run as private AI on infrastructure you control, and every read and write is logged. The design is built to support your HIPAA, SOC 2 or similar control program, and your auditors can follow each action.
What affects the cost of AI integration?
Discovery is a fixed fee and the build is quoted per phase. Cost depends on how many systems are involved, whether they have modern APIs, how much data mapping is needed, and whether writes go back automatically or through an approval step. Systems with no API add the most work.
Can this run as private AI on our own infrastructure?
Yes. Everything we build runs on open-weight models served on hardware you own, in your own cloud account, or on an air-gapped network. Nothing calls a third-party model API unless you decide it may, and the connectors run inside the same boundary.
Related
More custom ai development.
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