Can you integrate with legacy systems that have no API?

Usually yes, through the database, a file drop, or an intermediate service, though each option carries a different support burden. The honest answer depends on the system, and establishing it is a first-week question rather than an assumption.

How do we avoid a fragile integration?

Define the failure path before the happy path. A system with a specified behaviour for unavailable, slow and unsure is resilient; one that only works when everything works is a demonstration.

Who supports it afterwards?

Whoever owns the target system, which is why they are involved from the start. An integration handed to a team that never saw it being built is an incident waiting for a quiet week.

Will this slow our existing system down?

Only if it is designed to run inline when it does not need to. Much AI output is better produced asynchronously and presented when ready, which removes the latency question from the critical path entirely.

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AI Integration Services

You don't need to rebuild everything. We plug AI into your existing systems so you get results without disruption.

Integration Services

API Integration

Connect AI services to your applications via clean, well-documented APIs. Works with any tech stack.

Legacy System Modernization

Add AI capabilities to older systems without a full rewrite. We build bridges, not walls.

Workflow Automation

Automate repetitive tasks with intelligent pipelines. Reduce manual work and human error.

Data Pipeline Setup

Build the data infrastructure AI needs - collection, cleaning, storage, and real-time processing.

How We Integrate

Audit existing systems

Understand your current architecture, data flows, and constraints.

Design integration points

Identify where AI adds value with minimal disruption.

Build & test incrementally

Roll out in stages with thorough testing at each step.

Monitor & optimize

Continuous monitoring ensures everything runs smoothly post-launch.

Typical timeline

4-12 weeks depending on system complexity.

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What does it take to integrate AI into systems already in production?

Integrating AI into a running system is mostly not a modelling problem. It is a question of where inference runs, what latency the workflow tolerates, which system the output lands in, what happens when the service is unavailable, and who is paged when it breaks. A model that answers correctly in eight seconds is useless at a workstation and fine in a nightly batch, and nothing about the model tells you which case you are in.

The work therefore starts from the target system rather than the model: its interfaces, its release process, its security review, and the person who owns it. Most integrations that fail did so because that person was not involved until the end.

Adoption is the other half. If a human has to copy a result from one screen into another, usage decays to zero regardless of accuracy, so the output has to arrive where the work already happens.

Who this is for

Companies with a chosen model or vendor and no path into their stack.
Brownfield estates where the target system predates the API era.
Teams whose pilot worked in isolation and stalled at deployment.
Businesses needing AI output inside an ERP, MES, CRM or case system.

What we need from you

Access to the target system and a test environment.
Whoever owns that system, involved from the first week.
The real latency and volume requirement, taken from the workflow.
Your security review process, started early rather than late.

What you get back

A working integration into the system people already use.
A defined fallback for when the model is unavailable or unsure.
Monitoring and alerting tied to the workflow, not just uptime.
A runbook naming who does what when it misbehaves.
Load and latency evidence against the real requirement.

What a typical engagement looks like

Inference into an ERP or MES

Wiring model output into the system of record so an operator sees it in place, with the fallback path defined before go-live rather than discovered during an outage.

Replacing a manual handoff

Where a person currently moves data between two systems, the integration removes the handoff entirely, which is usually where the measurable saving actually comes from.

Vendor model, your environment

Deploying a third-party model inside your boundary for residency or latency reasons, including the security review and the operational handover.

Regulatory and data constraints

Where inference runs is a compliance decision as much as an engineering one. Data residency, the acceptable vendor list and the GDPR position on transfers all constrain the architecture, and retrofitting residency onto a system built against a foreign-hosted API generally means rebuilding it.

Security review is routinely the longest pole and is entirely predictable. Starting it in week one rather than at go-live is the single change that most reliably shortens an integration timeline.

Frequently asked questions

Agents across existing systems

Where a workflow spans several systems and breaks on exceptions, a goal-driven agent copes better than a fixed integration script. It also needs different guardrails.

How Hermes Agents optimise business operations

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