Tepia builds governed enterprise AI automation that acts across CRM, ERP and ticketing with human approvals, guardrails, evaluation and audit logs.
Tepia builds AI automations and agents that take real actions across your CRM, ERP, ticketing and email systems, with human in the loop approvals, allow lists, evaluation and audit logs so the automation stays governed.
Every Tepia build runs through six phases, Discovery, Design, Development and Testing, Training, Launch and Support, with a milestone at each stage.
Read: Custom AI Agent Development
Tepia wraps every automation in human in the loop approvals, allow lists and guardrails so it acts only where it is permitted to.
Read: Internal Tools Development
Tepia delivers a first automation in 3 to 9 months, with a governed rollout where humans approve everything before the system acts alone.
Read: AI Integration Services
Tepia builds accuracy testing and an audit log of every action, so the automation stays governed, measurable and reviewable.
Read: Systems Integration ServicesIndustry figures Tepia plans around when scoping ai automation work.
About 70 percent of the activities in most jobs have some automation potential, according to McKinsey research.
About 45 percent of paid activities could be automated with current technology, according to McKinsey estimates.
Mobile devices generate roughly 60 percent of global website traffic, according to Statista's mobile traffic reports.
Most AI projects stop at answering questions. Enterprise AI workflow automation goes further: the system takes real actions, updating a CRM record, issuing a refund, routing a ticket, drafting and sending an email, on your behalf. That is where the value is, and also where the risk is, because an automation that can act can also act wrongly at scale. Tepia builds automations that act, and wraps them in the controls that make acting safe.
The difference between a demo and a production automation is governance. Tepia designs every automation with human in the loop approvals on the actions that matter, allow lists that bound what the system is permitted to touch, guardrails that stop out of policy behavior, and an audit log that records every action and the reasoning behind it. Nothing acts on production data without a control in front of it.
Tepia brings thirteen years of disciplined engineering and real AI delivery, including retrieval augmented generation, vector databases and the OpenAI and Anthropic model APIs, applied inside enterprises rather than in a sandbox. This is governed automation, not a chatbot with ambitions.
The fastest payback usually comes from one high volume workflow in one department, not a company wide program. Tepia uses the table below to help teams pick a first automation with a clear owner, a measurable outcome and a bounded blast radius. Each row names the system the automation acts on and where a human approval typically sits.
| Department | Automation | System it touches |
|---|---|---|
| Support | Triage, draft replies, resolve or escalate tickets with agent review | Zendesk or ServiceTitan style ticketing, email, knowledge base |
| Finance | Match invoices, flag exceptions, prepare entries for approval | QuickBooks or ERP, document store |
| Operations | Route work, update records, generate status summaries | ERP, internal databases, Slack or Teams |
| Sales | Enrich leads, log activity, draft follow ups for rep approval | Salesforce or HubSpot, email, calendar |
| HR | Screen requests, answer policy questions, prepare onboarding tasks | HRIS, ticketing, document store |
In every case Tepia decides where the automation may act on its own and where a person approves first, based on how costly a wrong action would be. A draft email a rep sends is low risk. A refund or a ledger entry gets a human in the loop. Tepia sets those lines with you during Discovery rather than assuming them.
Not every automation should be an AI agent. When a workflow is rules based and stable, deterministic automation or classic RPA is cheaper, more predictable and easier to audit, and Tepia will recommend it over a model. When a workflow needs judgment, reads unstructured text, or spans systems in ways rules cannot capture, an agent earns its place. Tepia chooses per workflow rather than applying one tool to everything.
Where an agent is right, guardrails are what make it safe to run. Tepia constrains agents with allow lists of the exact tools and systems they may call, validation on every action before it executes, and human approval on anything irreversible or high value. Data governance is designed in: the automation sees only the data it needs, sensitive fields are masked or excluded, and access follows least privilege.
Assurance is not optional at enterprise scale, so Tepia builds evaluation and accuracy testing into the project. That means a test set of real cases, measured accuracy before launch, and ongoing monitoring so drift is caught. Every action lands in an audit log with the inputs and the reasoning, exportable for your compliance team. The systems integration page covers the governed connections these automations run on.
Tepia runs AI automation through its six phase process. Discovery is a workflow mapping exercise: a system investigation of the process you want to automate, the systems it touches and the data it needs, user interviews with the people who do the work today, and a third party integration review of your CRM, ERP, ticketing and email. Deliverables are an Investigation Summary, an Interview Summary and User Stories that define exactly what the automation should and should not do.
Design specifies the automation: the decision points, where humans approve, the allow lists and guardrails, the evaluation set and the audit model. Development and Testing builds it on Alpha and Beta schedules, with accuracy testing against the real case set as a formal gate before anything runs on production data.
Training is a full phase, because the people whose work is being automated need to trust and supervise it. Tepia runs in person and remote sessions on how to review, approve and override the automation, using real user story scenarios. Launch rolls the automation out gradually, often starting with humans approving everything and loosening only as accuracy is proven, with Tepia support reps on hand. A Tepia project manager is assigned throughout, and leadership is US based. See tepia.co/process.
Handing an automation the ability to act on production systems is a trust decision, so the team and the controls matter more than the model. Tepia is US led with individually sourced global talent, not a body shop. Project management, design leadership and engineering leadership are US based, engineers are hand picked and stay on the system, and the team works in US overlapping hours, which matters when an automation needs supervision during business hours.
Tepia’s engineering leadership designs the governance directly, from allow lists to audit logs to the human approval points, and answers security questions rather than routing them to sales. That discipline comes from thirteen years of building regulated and enterprise software, including HIPAA aware and PCI scoped systems where acting wrongly has real consequences.
If you want a single purpose agent rather than a cross system automation, see custom AI agent development. For adding AI capability to existing products, see AI integration services. Full services are at tepia.co/services.
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