AI Solutions & Automation

AI and automation engineered into how the business runs.

Zero One Creation builds AI agents, workflow automation, and model integrations connected to the tools you already run on, so the work gets done, not just summarized in a dashboard.

See Our Work
Trusted by teams building real products
Fortune Group
Classify-X
EkInch
Aim Campus
Ullas
The problem

More tools. More data. Still done by hand.

Most businesses aren't short on software. They're short on systems that actually talk to each other.

A team spends hours a day processing the same kind of request. Hundreds of documents need reviewing by hand. Support answers the same question for the hundredth time. Data exists, but nobody can turn it into a decision without a spreadsheet and an afternoon.

That's not a tooling problem you fix with one more app. It's a systems problem — connecting the tools you have and deciding where automation, and where AI specifically, actually earns its place.

What we build

Six capabilities, one engineering standard.

From a single automated workflow to AI built into the product itself, every build gets the same rigor.

01

AI agents & automated workflows

Agents that read a request, decide what it needs, and trigger the right workflow, so automation handles judgment calls, not just fixed if/then rules.

02

Business process automation

Repeatable manual work — handoffs, approvals, data entry — turned into reliable workflows your team doesn't have to babysit.

03

AI/ML model integration

Existing models from OpenAI, Anthropic, or Google integrated into your product or internal tools, not bolted on as a separate chatbot.

04

Data & reporting automation

Documents, records, and scattered data turned into structured insight, with reports and dashboards that update themselves.

05

CRM, sales & marketing automation

Lead routing, follow-ups, and campaign triggers that run on real behavior, without manual lists and exports.

06

Custom integrations & APIs

The tools you already use, connected through REST or GraphQL APIs, so data moves automatically and accurately between systems.

Choosing the right approach

AI isn't automatically the right tool.

Both approaches are valid. The right one depends on whether the process is predictable, or whether it needs judgment.

Traditional automation

Best when the process is predictable.

  • Rules are clear and don't change often
  • Inputs are structured — forms, fields, fixed formats
  • The decision is deterministic: if this, then that
  • Example: syncing an order to inventory and invoicing
AI-powered automation

Best when judgment is involved.

  • Inputs are unstructured — emails, documents, chat
  • Language needs to be read and interpreted
  • Classification or summarization is required
  • Example: reading a support ticket and deciding how to route it
How AI agents actually work

Not every process needs an agent. Some genuinely do.

An agent earns its place when a task involves real judgment — reading context and deciding what happens next. When the logic is simple and fixed, a standard automation is faster to build and easier to trust.

1
Observe

Takes in the request, message, or event that triggered it.

2
Reason

Interprets context against the rules and data it has access to.

3
Decide

Chooses the right action, or flags it for a human to decide.

4
Act

Executes the action in the connected system.

5
Report

Logs what happened, so nothing changes silently.

The system

We're not adding a chatbot. We're engineering a loop.

Input becomes a decision, the decision becomes an action, and the result feeds back into how the system improves.

Business input
A request, document, message, or event
AI / decision layer
Reads context and decides what should happen
Automation
Executes the workflow across connected tools
Business systems
CRM, ERP, inventory, support, finance
Human approval where needed
High-stakes or ambiguous actions get reviewed
Action
The task is completed, not just flagged
↳ data and outcomes feed back into the decision layer
How we work

A six-stage engineering process.

Clear milestones, fast feedback, no jargon.

STAGE 01

Discovery & audit

We map what's manual today, where errors happen, and what's actually slowing the team down, before proposing anything.

STAGE 02

Strategy & planning

We define triggers, owners, and edge cases, and decide honestly where AI adds value and where simpler automation is enough.

STAGE 03

Build & configure

We connect tools, build workflows and agents, and set sensible alerts and retries where things can go wrong.

STAGE 04

Test & QA

We test against real data paths, not a demo, to catch failure points before anything touches production.

STAGE 05

Deploy & monitor

We roll out safely and monitor early performance so nothing quietly fails.

STAGE 06

Handover & training

Your team gets documentation and a plain-language playbook for what to do if something breaks.

Technology

The stack chosen for the task, not the other way around.

OpenAI · Anthropic · Google Gemini · Open-source models

The model chosen for the task, not the other way around

We integrate existing frontier models where they fit, and open-source models where cost, privacy, or latency make more sense, rather than defaulting to one provider.

Use cases

Where AI and automation actually fit.

Select an area to see the problem we're solving and what we typically build.

Challenge

Support teams answer the same questions repeatedly, and simple tickets wait behind complex ones in the same queue.

What we build

AI-assisted triage and responses for common requests, with automated escalation and routing for everything else.

Ticket classificationAutomated responsesSmart routingHuman handoff
What changes

Support teams spend their time on the tickets that actually need a person.

Proof

Proof beats promises.

You don't need "AI-powered" on a slide. You need workflows that actually run without you.

Classify-X

A white-label EdTech platform for institutions to manage students, courses, and analytics with automated workflows replacing manual admin.

5× efficiency boost in student operations across 10+ institutions.

Why Zero One Creation

Judgment first, AI where it earns it.

We recommend AI when it earns its cost, not by default.

If a rule-based automation solves the problem more reliably and cheaply than an AI agent, we'll say so and build that instead.

Automation is treated as a system, not a script.

Monitoring, error handling, and fallbacks are part of the build, so a workflow doesn't silently break a month after launch.

You own everything. No lock-in.

Workflows, code, and infrastructure accounts stay in your name. If you move teams, you're not trapped in someone else's automation.

We explain what's actually happening.

You'll know what an agent or workflow does, why it made a decision, and what to do if it doesn't — not just that it's "AI-powered."

FAQ

Questions worth asking before you commit.

Does Zero One Creation build AI agents?+

Yes. We build task-oriented AI agents that observe a request, reason over context, decide on an action, and execute it, for workflows where judgment is genuinely needed. We'll also tell you honestly when a simpler rule-based automation is a better fit than an agent.

Can you integrate existing AI models like OpenAI, Anthropic, or Gemini into our product?+

Yes. We integrate frontier models from OpenAI, Anthropic, and Google, as well as open-source models where cost, privacy, or latency make more sense, directly into your product or internal tools rather than as a separate bolt-on chatbot.

What's the difference between automation and AI-powered automation?+

Traditional automation works best when rules are predictable and inputs are structured — it's faster and cheaper to build and maintain. AI-powered automation earns its cost once inputs are unstructured, language is involved, or a decision needs context. We choose based on the problem, not a default preference for either.

Do you build workflow automation without AI?+

Yes, most of our automation work is rule-based and doesn't need AI at all — connecting tools, syncing data, and automating handoffs using platforms like Zapier, Make, or n8n, or custom integrations where those don't fit.

Which tools and platforms do you work with?+

Zapier, Make, and n8n for workflow automation; Zoho, HubSpot, WhatsApp API, and Google Workspace for business systems; OpenAI, Anthropic, and Google Gemini for AI models; and custom API integrations wherever an off-the-shelf connector doesn't exist.

Can you connect AI and automation to our existing CRM, ERP, or data systems?+

Yes. Automation and AI decisions are connected directly to the systems that run your business — CRM, ERP, inventory, and finance tools — so actions actually update the systems of record, not just a dashboard.

Do we need technical knowledge to work with you?+

No. You explain the process or problem in plain language. We translate it into workflows, agents, or integrations, and keep you updated with simple demos rather than technical jargon.

How long does an AI or automation project take?+

Simple workflow automations can go live in days. Multi-tool systems and AI agent builds typically take 2–6 weeks depending on integrations and complexity. We can also scope a lean first version and iterate from there.

We turn bold ideas into successful products

If your team is still doing work that could run itself, let's fix that.

Your budget for this project?