AI integration · Custom software · Consulting

AI built into how your business runs.

Petelink is a senior engineering team. We put AI and custom software inside real business processes (ERP, CRM, warehouses, back-offices) and keep them running.

A small senior team. You talk to the engineers who build it.

Engineering since
2015
Lead architect experience
15+ yrs
Production apps (CTO)
30+
Fig 01 from process to production

Why Petelink

Most AI projects stall between the demo and production. That gap is our work.

A demo shows what a model can do. Production needs integration with the systems you already run, access control, error handling, cost limits, tests and someone on call. We design for that from day one, so what you see in the first weeks is what you run later.

Services

Four ways we work with you.

  • 01

    AI integration

    Put language models, retrieval and agents into the processes where they pay off: document intake, customer operations, internal search, approvals, reporting.

    • Document and email processing
    • Assistants and agents with tool access
    • Search over your own knowledge
  • 02

    Custom software

    Systems that manage customers, orders and warehouses, and replace slow, outdated back-offices. Built to carry load and to change as your business does.

    • ERP, CRM and WMS
    • Portals that adapt content to client type
    • High-load, fault-tolerant back ends
  • 03

    Data and analytics

    Pipelines and analytics for large volumes of data, with search and reporting your teams can actually use.

    • Batch pipelines and Spark
    • Full-text search (Elasticsearch, Solr)
    • Dashboards and reporting
  • 04

    AI and architecture consulting

    An independent view before you commit budget: where AI fits, what to build or buy, and how to do it securely.

    • AI readiness and use-case review
    • Architecture and security review
    • Roadmap, vendor and model selection

How we work

From goal to production, in four steps.

We do not ask for a requirements specification. We ask about your goals, then agree how success will be measured.

Fig 02 criteria first, then code
  1. Step 01

    Discover

    Short conversations with the people who run the process. We map the process, the data and the systems, and pick the use case with the clearest return.

    You get
    A one-page scope with a success metric
    We avoid
    Starting from a 60-page specification
  2. Step 02

    Prototype

    A working prototype on your own data, scored against criteria agreed in advance. If it does not meet them, we say so early.

    You get
    A tested prototype and a clear go / no-go
    We avoid
    Slide decks instead of working software
  3. Step 03

    Build

    Production engineering: integration with your systems, access control, tests, evaluations and a security review.

    You get
    Software you own, with tests and documentation
    We avoid
    Black boxes only we can maintain
  4. Step 04

    Operate

    We monitor quality, cost and latency, update models and prompts as they change, and maintain the system over time.

    You get
    Monitoring, updates and support on agreed terms
    We avoid
    Handing over and disappearing

Principles

Built to be trusted.

Measured

Every AI feature is tested before it ships.

We build an evaluation set from your real cases and agree a pass threshold with you. Each change to a prompt, model or code is scored against it.

Controlled

AI proposes. Rules decide. People approve.

Where a decision has consequences, deterministic code and a human sign-off sit between the model and the outcome. Access is least-privilege, sensitive data is minimised or redacted, actions are logged.

Honest

We tell you when AI is not the answer.

Sometimes a rule, a query or a better form does the job at a fraction of the cost. We say so, and build the simpler thing.

Fig 03 every change is scored

Stack

Proven tools. Chosen for the job.

We pick the technology that saves you time and money, and we stay model-agnostic.

AI

  • LLM APIs and open models
  • Retrieval (RAG)
  • Agents and tool use
  • Evaluation harnesses

Back end

  • Python
  • Django
  • Flask
  • Scala
  • REST API

Data and search

  • PostgreSQL
  • MySQL
  • Redis
  • NoSQL
  • Spark
  • Pandas
  • Elasticsearch
  • Solr
  • Sphinx

Front end

  • React
  • Vue
  • HTML5 / CSS3
  • SASS

Cloud and operations

  • Google Cloud
  • AWS
  • Linux
  • Ubuntu
  • Debian
  • Git

Team

Senior engineers, direct access.

Vladimir Ageykin

CTO · Lead architect

Software architect with 15+ years of experience delivering 30+ production applications across GenAI, financial systems and enterprise platforms. He has led engineering teams building LLM-based products and data-heavy platforms on Python and cloud infrastructure, and works as a system architect and security specialist.

LinkedIn ↗

The team

Engineering · QA · Product

Senior full-stack engineers, a testing specialist and a product owner. A small team by design: the people who scope the work are the people who build it.

FAQ

Questions, answered.

What does Petelink do?

Petelink is a senior software engineering team that integrates AI and builds custom software for businesses: ERP, CRM and warehouse systems, data platforms and AI-powered automation of business processes. We also advise on AI strategy, architecture and security.

How do you add AI to an existing business process?

We start with the process and the goal, not the model. We map the data and systems involved, build a prototype on your data, score it against criteria agreed in advance, and only then engineer it into production with access control, logging and monitoring.

Can AI work with our existing ERP, CRM or warehouse system?

Yes. We integrate through APIs, events and data exports, so you keep the systems you already run. Where a legacy system has no API, we design a safe way around it or replace only the part that blocks you.

How do you keep our data safe?

We minimise and redact sensitive data before it reaches a model, use least-privilege access, log actions, and keep deterministic rules and human approval between a model and any consequential decision. A security review is part of every build.

How does an engagement start?

With a discovery call. We ask about your goals, agree a scope and a success metric, then propose a prototype or a fixed-scope review. Write to welcome@petelink.dev.

Do you only advise, or also build and run?

Both. We can review and advise, build the system, and operate it afterwards with monitoring and updates.

Tell us the process that hurts.

Email the process, the systems involved and the outcome you want. A senior engineer replies.

Email us the process and the systems involved