AI at Zyptr

Don't start with the model. Start with the problem.

We help teams identify where AI can reduce manual work, improve customer experience, accelerate decisions or create new product capabilities.

Most AI projects fail for an unglamorous reason: they start from a capability somebody wanted to use rather than a cost somebody wanted to remove. We work the other way round — and we will tell you when the answer is that you do not need AI for this at all.

Use Cases

Where teams usually start

These are the eight that come up most often. Each one is a business problem before it is a technical one.

AI customer-support agents

Resolve the repetitive half of your ticket volume, and hand the rest to a human with context attached.

Internal knowledge assistants

Let teams ask questions of your own documentation, policies and history instead of hunting through drives.

Document intelligence and extraction

Turn invoices, contracts, forms and scans into structured data your systems can act on.

Sales and operations automation

Remove the manual steps between a lead, a quote, an approval and a fulfilled order.

AI copilots inside existing products

Add assistance where your users already work, so the feature raises retention rather than sitting unused.

Workflow agents and system orchestration

Agents that carry a task across several systems and escalate when they should not decide alone.

RAG and enterprise knowledge systems

Retrieval grounded in your data, with citations, permissions and evaluation built in from the start.

Predictive analytics and machine learning

Forecast demand, score risk and flag anomalies using the data you already collect.

How We Get There

Audit, pilot, production

Three stages, each with a decision point at the end. You are never asked to commit to the whole thing on the strength of stage one.

Step 01

Audit

Map every workflow where AI could plausibly pay off, assessed against the data you actually have. The output is a ranked list, including what we recommend against.

Product & AI Audit
Step 02

Pilot

Take the top candidate and make it work on real data, used by real people, with success defined before we start.

30-Day Build
Step 03

Production

Harden what the pilot proved: evaluation sets, monitoring, permissions, cost controls and the integration work that makes it part of the business.

AI & Automation
FAQ

AI FAQ

Let's Work Together

Find your AI opportunities

Tell us where your team loses the most time. We will tell you whether AI is the right instrument for it — and what it would take.