Question 1

How do we know whether AI is the right answer at all?

Accepted Answer

That is the first question we try to answer, and sometimes the answer is no. We look at the workflow: how often it runs, how much human time it consumes, how tolerant it is of being wrong, and whether the data needed to do it well actually exists. If a rules engine or a fixed integration would do the job more cheaply and predictably, we will tell you that rather than sell you a model.

Question 2

What kinds of AI projects does Zyptr take on?

Accepted Answer

Production systems across the stack: support and internal knowledge agents, document intelligence and extraction, RAG over enterprise data, workflow agents that carry a task across several systems, custom ML models for classification, forecasting and anomaly detection, and copilots built into existing products. Scope ranges from a single focused model to a full AI platform.

Question 3

Do you use commercial or open-source models?

Accepted Answer

Both, and choosing is part of the work. Commercial models from Anthropic, OpenAI and Google are strong on general reasoning and fast to prototype with. Open-weight models are often the better answer for data-sensitive use cases, tight latency budgets or on-premises deployment. We are model-agnostic and pick per problem, and we design so the choice can be revisited later without a rewrite.

Question 4

What is RAG and why does it matter for enterprise AI?

Accepted Answer

RAG (Retrieval-Augmented Generation) solves the fundamental limitation of a language model: it does not know your data. A RAG system pairs retrieval over your documents, databases and knowledge base with a generative model that answers from what it retrieved. Done properly it also carries citations, respects existing permissions, and is evaluated against a real question set rather than assumed to work.

Question 5

How do you know whether the AI is actually working?

Accepted Answer

We agree the measure before we build: deflection rate, extraction accuracy, time saved per case, forecast error — whatever the business outcome actually is. Then we build an evaluation set from your real cases and track against it, including after launch, because model behaviour and your data both drift.

Question 6

How do you handle data privacy and security?

Accepted Answer

AI systems can be deployed entirely inside your infrastructure so no data leaves your environment. We implement encryption in transit and at rest, anonymise data before training where relevant, respect existing access controls in retrieval, and log what was asked and answered for audit. For regulated industries we have worked to GDPR, HIPAA and DPDP requirements.

Question 7

How long does it take, and what does it take to start?

Accepted Answer

A focused pilot on a single workflow is typically a matter of weeks — that is exactly what a 30-Day Build is for. Production systems generally run longer depending on data readiness, integration surface and compliance. If you are not yet sure which workflow to start with, a Product & AI Audit produces the opportunity map first.

Question 8

Do you maintain AI systems after launch?

Accepted Answer

Yes. AI systems need more ongoing attention than conventional software: models drift as real-world data shifts, prompts and retrieval quality regress silently, and costs move with usage. We offer retainer-based monitoring, evaluation and tuning, and we can train your team to own it.

Turn repetitive work into intelligent workflows.

The work itself

AI Opportunity Mapping

Support & Knowledge Agents

Document Intelligence & Extraction

RAG & Enterprise Knowledge

Workflow Agents & Orchestration

Predictive Analytics & ML

The use cases that come up most

AI customer-support agents

Internal knowledge assistants

Document intelligence and extraction

Sales and operations automation

AI copilots inside existing products

Workflow agents and system orchestration

RAG and enterprise knowledge systems

Predictive analytics and machine learning

See something working before you commit to the full build.

AI & Automation FAQ

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