AI Services
AI Process Automation for Business Operations
Most businesses have three to five processes that consume a disproportionate amount of staff time. They are usually not complex. They are repetitive. Someone reads something, makes a simple decision, and takes an action. Do this a hundred times a day, and you have a meaningful operational cost that does not scale with your business.
AI process automation is the right tool for this category of work. Not for complex judgment calls, for the high-volume, pattern-driven tasks that follow recognizable logic most of the time.
8-14 weeks
Scoping to production
Phased
Deployment approach
Measurable
Success metrics built in
Technology
Tools and platforms we work with
We select the right stack for your project. These are the platforms and technologies relevant to this service.
OpenAI
Claude
Python
LangChain
Zapier
Make
n8n
Salesforce
HubSpot
SAP
PostgreSQL
AWS
What we deliver
Core capabilities
High-volume task automation
Automate repetitive read-decide-act processes that consume disproportionate staff time at scale.
Judgment-based classification
Handle unstructured input like emails and documents where rule-based tools cannot make the decision.
Exception routing
Every automation includes a defined path for uncertain cases to a human review queue with full context.
Production reporting
Track volume processed, exception rate, and time saved per unit directly in the system.
How we work
Our process
1
Process mapping
Document the process as it actually runs, including decision points and exception cases.
2
Scoping
Define what gets automated, what stays manual, and what goes to review. Produce cost and timeline estimate.
3
Build
Develop the standard flow first, then handle exception cases before go-live.
4
Measure
Deploy with built-in reporting on volume, exceptions, and time saved.
Spending too much time on repetitive processes?
Tell us which process consumes the most staff hours. We will assess whether it is a good automation candidate.
Deep dive
How this service works in detail
What processes are good candidates
A process is a good automation candidate when: the volume is high enough that the manual cost is real, the decision logic follows patterns that can be documented, the inputs are consistent enough to be parsed reliably, and the cost of an occasional wrong decision is manageable (or there is a review step for uncertain cases).
Common examples: invoice matching and approval routing, customer inquiry classification and routing, lead qualification and CRM data entry, inventory alert generation, report compilation from multiple data sources, document data extraction and filing, onboarding workflow triggers.
What makes AI different from standard automation tools
Standard automation tools (Zapier, Make, n8n) handle structured data well. If the input is always in the same format and the decision is always the same given the same input, they work well and cost less than custom AI development.
AI automation handles unstructured input (text, documents, emails) and makes classification decisions that rule-based tools cannot. The right approach depends on what your process actually looks like. We assess this during scoping and recommend the simplest tool that solves the problem. Sometimes that is a no-code tool. Sometimes it requires custom AI development.
How we work
Process mapping (one to two weeks): we document the process as it actually runs, not as it is supposed to run. We identify decision points, exception cases, and the data sources involved. This phase often surfaces process improvements that have nothing to do with AI.
Scoping (one week): based on the process map, we define what gets automated, what stays manual, and what goes to a review queue. We produce a cost and timeline estimate.
Build (four to twelve weeks): we develop the automation, starting with the standard flow and then handling exception cases. We do not go live until exception handling is built and tested.
FAQ
Frequently asked questions
How do you handle exceptions, cases the automation cannot resolve confidently?
Every automation we build has a defined exception path. Uncertain cases go to a review queue with the relevant context displayed so a human can make the decision quickly. The exception rate is monitored and fed back into prompt refinement.
Can automation be deployed in phases?
Yes, and we recommend it. Start with the highest-volume part of the process where the logic is clearest. Get that running reliably before expanding to lower-volume or more complex cases.
Do you work with our existing tools or replace them?
We build on top of your existing tools wherever possible. The automation layer connects to them via API or file exchange. We do not require platform migration.
What is a realistic timeline from first conversation to production?
For a single, well-defined process: eight to fourteen weeks from scoping to production deployment. This includes discovery, build, testing, and a production stabilization period.
How do we measure whether the automation is working?
We define success metrics before building: volume processed per day, exception rate, time saved per unit. We build reporting into the system so you can see these numbers in production.
Ready to scope your project?
Tell us about your systems, workflows, or site. We will respond with a realistic plan and timeline.
Request a consultation