AI Engineering

AI and agentic systems that hold up outside the demo

AI features and autonomous agents, built to run in production

We design, build, and ship AI features and autonomous agents on current frontier models — with the evals, guardrails, and engineering discipline that production work demands.

Talk to our team All Services

what we deliver

LLM integration✓

Retrieval-augmented generation✓

Agents and tool use✓

Evaluation and testing✓

Guardrails and safety✓

Overview

How we help

Most AI projects stall at the demo. The prototype impresses, then breaks on real inputs, leaks edge cases, or quietly returns wrong answers no one catches. Mariyano builds the parts that make the difference: retrieval that stays grounded, agents that use tools safely, and evaluation that tells you whether the system actually works before your users find out.

We work across the stack — LLM integration into existing products, retrieval-augmented generation over your own data, and agentic systems that plan, call tools, and complete multi-step tasks. We pick models on merit rather than hype, and we build with the guardrails and observability that let you run them with confidence.

As the product company behind Zipflow, YokoCart, UltraBI, and Fezo, we apply the same standards to client work that we apply to our own. You get AI that integrates cleanly with your systems and a team that has shipped and maintained it in production.

Capabilities

What's included

LLM integration

Bring language models into your product where they add real value — drafting, classification, extraction, summarisation, and natural-language interfaces — wired into your existing data and workflows.

Retrieval-augmented generation

Ground model output in your own documents and data with RAG pipelines built for accuracy and traceability, so answers cite sources and stay current as your content changes.

Agents and tool use

Design autonomous and semi-autonomous agents that plan, call tools and APIs, and complete multi-step tasks, with clear boundaries on what they can do and when a human stays in the loop.

Evaluation and testing

Build eval suites that measure quality, regressions, and failure modes on representative data, so you can change a prompt or model and know whether it helped before shipping.

Guardrails and safety

Add input and output guardrails, permission scoping, rate and cost controls, and logging, so agents behave predictably and sensitive actions stay constrained.

AI strategy and consulting

Assess where AI is worth applying and where it is not, scope a pragmatic roadmap, and advise on build-versus-buy, model choice, and the operating cost of running it.

How we work

Our approach

Frame the problem

We start with the outcome, not the model. We identify where AI genuinely helps, define what good output looks like, and agree on how success will be measured before any building begins.

Prototype against real data

We build a working prototype on your actual data and inputs, choosing models on merit, so the early read on quality and cost reflects reality rather than a curated demo.

Harden for production

We add evals, guardrails, observability, and the integration work that turns a prototype into a dependable feature — handling edge cases, failures, and cost as first-class concerns.

Ship and improve

We deploy into your environment, monitor behaviour and quality in the open, and iterate as models and your needs change, keeping the eval suite as the source of truth.

Use cases

Where it fits

Knowledge assistants over your data

Internal and customer-facing assistants that answer questions from your documents, policies, and records with grounded, source-cited responses rather than guesses.

Document and data processing

Extract, classify, and summarise high volumes of documents, tickets, or records, replacing slow manual review with a pipeline that is measured for accuracy.

Operational agents

Agents that carry out multi-step tasks across your tools — triage, routing, drafting, and routine actions — with permissions and human review on the steps that matter.

AI inside existing products

Add AI features to software you already run, integrated through clean APIs so the new capability fits your data, your interface, and your operating standards.

FAQ

Common questions

How do you keep AI features from producing wrong answers?

We ground responses in your own data with retrieval, constrain what agents are allowed to do with guardrails and permissions, and measure quality with eval suites on representative inputs. The goal is a system you can trust because it is tested, not because it demos well.

Which models do you build on?

We use current frontier models and choose them on merit for each task, weighing quality, latency, and cost. We avoid lock-in where it is sensible to, and we design so you can move to a better model as the field changes.

Can you take a project from idea through to production?

Yes. We cover strategy, prototyping, hardening, and deployment, and we can integrate with your existing systems. For a scoped conversation about your use case, reach the Mariyano team at [email protected].