Custom AI Automation Services

AI automation services take an operational process your team currently runs by hand, or runs through brittle scripts, and hand it to software that can read unstructured inputs, decide between paths, act across your systems, and escalate what it should not decide alone. The scope is the process itself: intake, the steps in between, the writes back to your systems, and the exceptions.

That is broader than a single tool. Real automation covers the integrations, validation rules, approval gates, audit trail, and dashboard your operations leads use to see whether it is working.

Traditional RPA follows a fixed script: click here, copy that field, paste it there, and breaks the moment a button moves or an input format changes. AI automation reads intent instead of screen coordinates, handles inputs that were never uniform to begin with, and adapts when a system changes, so the long tail of edge cases stops landing in a human queue.

The trade-off is governance. Software that can decide needs approval queues, validators, and audit logs built in before production, so your finance team still approves the refund and the automation just gets it ready.

They answer different questions:

• AI automation starts from a business process: which workflow is expensive, what its inputs and exceptions are, and what changes when it runs without manual handling.

AI agent development starts from the system: designing the agent architecture, its tools, memory, and orchestration. If you know which process is hurting, start here.

If you need the underlying agent architecture designed and built, start there. Plenty of engagements need both, and we will tell you which you actually need during scoping.

The ones with volume, clear inputs, clear outputs, and a measurable cost per case. Invoice processing, order-to-cash, claims triage, ticket routing, vendor reconciliation, patient intake, and returns processing all fit that pattern. Processes that are genuinely judgment-heavy, low-volume, or undocumented are usually worth mapping before automating.

We start every engagement by ranking your candidate processes on volume, exception rate, and cost, because automating the second-best process well beats automating the most visible one badly.

Per industry averages:

• A pilot automating a single process typically runs from around $25,000 to $45,000

• A production-grade automation of one business process usually lands between roughly $80,000 and $180,000

• A multi-process platform on shared infrastructure runs higher again, depending on how many systems it integrates, the compliance scope, and the volume it has to carry

Custom work doesn't fit a fixed price list, so rather than quote a misleading number, we scope your process and give a tailored estimate. You can also describe the process in our scope document generator and get an approximate price alongside a proposed architecture in minutes, without a call.

A working proof of concept lands in 4 to 6 weeks for a single process with two or three system integrations. The first 14 days produce a scope document and a thin end-to-end automation that handles the happy path; weeks three to six add the exception handling, validators, and observability your operations team needs before it will trust the output.

We don't ship demos that only work on the slide deck. Every proof of concept runs against your real data in a staging environment before we discuss production rollout.

Three layers. Retrieval grounds every decision in your own records rather than general knowledge. Validators check each output against schemas, business rules, and confidence thresholds before the process continues. Low-confidence cases escalate to a human reviewer with the full reasoning trail attached.

The result is an error rate per process you can tune. A finance workflow gets tighter confidence thresholds than a triage workflow, and the system tracks override rates so you see drift before it becomes a problem.

Every integration runs with scoped credentials, so no shared service accounts and no read-write tokens reaching systems the process doesn't need. We add prompt injection filters, output validators, schema enforcement, per-tool rate limits, and immutable decision logs, so a misbehaving step gets caught at the boundary rather than after it has updated production records.

For high-stakes processes, we layer in human approval gates and a kill switch that disables the automation in one click if something looks wrong.

Yes. We build to SOC 2 controls by default, with encryption at rest and in transit, role-based access, audit logging, and data residency. For HIPAA workflows, we run BAAs with cloud providers, isolate PHI to compliant regions, and tag every automated decision so audits trace back to the source data.

GDPR-compliant deployments use EU-region inference, consent-aware retrieval, and automated data subject request handling. We've shipped this stack for clients in healthcare (DispatchHealth, Doctify), legaltech (LegalFly), and financial services.

Finance (order-to-cash, invoice processing, financial forecasting, vendor reconciliation), healthcare operations (medical coding, claims triage, patient intake), supply chain (demand forecasting, procurement, logistics exceptions), customer support (ticket triage, knowledge retrieval, response drafting), and retail (returns processing, catalog enrichment).

The common pattern across all of them: a multi-step process handled today by a mix of people and brittle scripts, with clear inputs, clear outputs, and a cost per case you can measure.

Yes. 80% of our clients stay with us 7+ years, and our average engineering tenure is 5 years, so the team that automated your process is still there in year three when upstream systems change, formats drift, and rules need retuning. Post-launch, we run guardrail maintenance, model upgrades, evaluation suites, and observability reviews on a monthly cadence.

We also offer structured handover if you ever decide to bring the work fully in-house, since you own the code either way.