Custom AI Agents for Any Business System

Built-in AI works well inside its own product. A custom agent works across all your systems, follows your own business rules and lets you choose the LLM. Many companies use both: the built-in features for single-app tasks and custom agents for work that spans several systems.

Yes. This is where agents add the most value. An agent can read from your CRM, check your ERP and write to your accounting system in one process, then pass exceptions to a person for review.

Options include direct database access, scheduled file exchange or a thin integration layer built for the system. We review the system in discovery and recommend the safest route.

A multi-agent system uses several specialist agents, with a supervisor agent that assigns tasks, checks results and escalates conflicts. You need one when a process needs different skills, such as reading contracts, checking accounting and approving exceptions, that one agent cannot handle reliably alone.

Agents can compare every record across systems, not a sample. They flag duplicates, mismatched amounts or missing steps that are hard to see manually, and show the records behind each flag so your team can verify quickly.

Cost depends on the number of systems connected, the knowledge required, the autonomy level and the volume handled. We give a fixed scope and quote after a short discovery call. Running costs, such as LLM usage, are shown separately so there are no surprises.

A focused agent is normally scoped and built in weeks, not many months. Timelines grow with the number of integrations and approval steps. We usually launch with a limited scope first and widen it once results are confirmed.

We build model-agnostic agents. You can use leading commercial LLMs or other models supported on the Synconics AI Platform, and use different ones for different tasks. Because the model is a replaceable part, you avoid lock-in to one provider.

We reduce the risk in layers: grounding the agent in your approved records, limiting what actions it may take, testing against real cases before launch and adding approval steps for sensitive work. Uncertain cases are routed to a person instead of guessed.

It does not need to be perfect, but the data the agent relies on should be reasonably accurate. During discovery we check the sources involved and point out gaps. Sometimes a small clean-up is the first step, and the Master Data Steward agent can help maintain it afterwards.

Data handling depends on the model and deployment you choose. We design agents so that only the data a task needs is shared, access follows user roles, and model settings are chosen to meet your privacy needs. Share your compliance requirements, such as GDPR, early in discovery.

Ownership terms are agreed in the project contract. We make scope and ownership clear before work starts, so you know what you receive and how your team can extend it.

Agents need tuning after go-live as real usage shows new cases. We monitor results against the agreed scorecard, refine prompts and rules, and handle model or system changes. Our teams in Canada and India provide complementary working hours.