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ENGINEERING · AI INTEGRATION

Generative AI development services and AI integration

Generative AI development services put large language model features into a real product, from retrieval and agents to chat, with evaluation, cost limits and data safeguards before launch. For new AI products and established apps.

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Led by Thanos GkanasCo-Founder, engineering

The drawing shows a help assistant and, under it, how it finds passages in your own documents, answers with sources and checks output, with an evaluation gate before release.

We start from the problem the feature should solve, not from the model. Then we build it into your existing backend, test it against real examples and measure what each answer costs. Whether you need AI integration consulting on one decision or hands-on AI integration services for a whole feature, the work ships in your codebase and stays maintainable by your team.

01SOUND FAMILIAR?

Signs you need AI integration help

  • SYMPTOM 01

    Our AI demo was great, but users get wrong answers

    Usually means: There is no evaluation set, so nobody knows how often the feature fails or why.

  • SYMPTOM 02

    The model bill doubled last month

    Usually means: Every request sends too much context to the largest model, with no caching and no limits.

  • SYMPTOM 03

    We want an AI feature but do not know where it fits

    Usually means: The product problem is not defined yet, so any choice of model is a guess.

  • SYMPTOM 04

    Our chatbot says things we would never say

    Usually means: Missing guardrails, no grounding in your own content and no review of risky outputs.

  • SYMPTOM 05

    We are not sure what customer data the provider keeps

    Usually means: Nobody has checked the provider's data terms, the retention settings or what the prompts contain.

  • SYMPTOM 06

    Our model is being retired and nobody planned for it

    Usually means: The feature is tied to one model version, with no tests to prove a replacement works.

Led by Thanos GkanasCo-Founder, engineering

Have an AI idea, or a feature that misbehaves? Tell us about it and we will give you a straight view of what it takes.

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02WHAT WE DO

What our generative AI development services cover

Generative AI integration services are mostly ordinary backend development services done carefully: data, APIs, tests, monitoring and cost control around a component that is sometimes wrong.

  • Feature scoping and model choice

    We define what the feature must do, what a good answer looks like and what it may cost per request, then choose the model and provider that fit. A narrow feature with clear inputs usually beats a general chatbot.

  • Retrieval over your own content

    We connect the model to your documents, help centre or product data, so answers are grounded in what you know and cite the source they came from.

  • AI agents integration

    Agents that call your APIs and tools get a narrow set of permissions, a budget for steps and cost, and a log of every action. Anything irreversible waits for a person to approve it.

  • AI chatbot integration

    Support and in-product assistants that answer from your content, hand off to a person when unsure and stay inside the topics you allow.

  • Evaluation before launch

    We build a test set from real examples and score every change against it, so you know the accuracy before users find out. The same tests run whenever prompts or models change.

  • Cost, safety and model upgrades

    Caching, smaller models for simple steps, usage limits, redaction of personal data and fallbacks when a provider is down. Providers retire models on a schedule, and our alert on OpenAI API deprecations lists the shutdown dates that make tested upgrades necessary.

Illustration of an evaluation run that holds the release until a failing answer is fixed

03IN BRIEF

What makes an AI feature production-ready

An AI feature is production-ready when you know how often it is right and what it costs per use. You also need to know what it does when the model fails and what data it sends where. Most demos answer none of these questions, which is why promising prototypes often stall before launch.

Getting there takes four pieces of work. First, an evaluation set: real examples with agreed good answers, scored automatically on every change. Second, cost controls: caching, smaller models for simple steps and limits per user. Third, failure handling: timeouts, fallbacks and a clear message when the feature cannot help. Fourth, data rules: what goes into prompts, what is redacted and what the provider may keep. With those in place, you can change prompts or switch models with evidence instead of hope. We build all four into every integration, inside your own codebase.

AI integration consulting or an in-house build?

Build in-house when you have engineers with time to learn evaluation, retrieval and cost control, and the feature is central enough to justify that learning. Bring in AI integration consulting when you need the first feature shipped well or a second opinion on an approach. It also fits when you want someone to set up the testing and monitoring your developers will then own.

The two are not exclusive. A common pattern is for consultants to build the first feature with your team, including the evaluation set and the cost dashboard, and then hand it over. Your developers learn the method on real work, and the next features are theirs to build. Whoever does the work, the deciding factor is the same. The model has to be treated as an unreliable component that needs tests, limits and fallbacks, not as a finished product you plug in.

What rules apply when your product calls AI models?

When your product sends data to an AI model, three sets of rules apply. The first is the provider's terms on data retention and training, and the second is privacy law for any personal data in the prompts. The third is AI-specific rules, such as the EU AI Act if you serve users in the European Union.

Start with the provider. Check whether your plan lets them keep or train on your data, how long logs are retained and where processing happens. Then check the prompts themselves: send only what the feature needs and redact personal details where you can. On AI rules, the EU AI Act includes transparency duties for some features, such as telling people when they are dealing with an AI system. Our update on EU AI Act enforcement covers what startups calling model APIs must do. This is engineering guidance, not legal advice, so confirm your obligations with a lawyer.

04STEP BY STEP

How an AI integration project works

  1. 01

    Audit

    We define the feature, collect real examples, check your data and the provider's terms, and agree what a good answer and an acceptable cost look like.

  2. 02

    Build

    We build the feature into your backend with retrieval, tools or chat as needed, plus the evaluation set, cost limits and logging around it.

  3. 03

    Ship

    We launch behind a feature flag, watch quality and cost on real traffic, and hand the tests, dashboards and runbook over to your team.

05ENGAGEMENT OPTIONS

Ways to work with us on AI features

  • AI integration consulting

    Working sessions on a feature idea, an architecture or a choice of provider.

    You get

    • A written recommendation
    • Risks, running costs and an evaluation plan
    • Next steps your team can act on

    Typical shape

    Thanos with your founders or technical lead, working sessions as needed

    Quoted after a short call

    Ask for a quote

  • Feature build

    We build one AI feature end to end, inside your codebase.

    You get

    • The working feature behind a flag
    • An evaluation set and scoring
    • A cost and usage dashboard
    • A handover to your developers

    Typical shape

    A fixed-scope project

    Quoted after a short call

    Ask for a quote

  • Embedded AI engineer

    A senior engineer joins your team to build and run AI features with you.

    You get

    • Ongoing feature work in your sprints
    • Evaluations kept current as models change
    • Mentoring for your developers

    Typical shape

    Part time or full time, reviewed monthly

    Quoted after a short call

    Ask for a quote

06WHO IT IS FOR

Who AI integration services are for

  • SaaS teams adding a first AI feature

    You know the product and the users, and want AI where it removes real work.

  • Products with an AI feature that misbehaves

    It shipped, but the answers, the costs or the behaviour are not where they should be.

  • Founders planning an AI-first product

    The model is central to the idea, and the architecture has to account for it from day one.

Thanos Gkanas

07LED BY

Who builds your AI features

AI integration is led by Thanos Gkanas, our Head of Engineering. He designs the evaluation and the failure handling before any feature reaches your users.

The model call is the easy part. The work is deciding what happens when it is wrong, slow or expensive, and testing for that before users do.

Thanos GkanasCO-FOUNDER · ENGINEERING
Thanos Gkanas
Co-Founder & Head of Engineering
Thanos's profileLinkedIn

08

Related services

AI features often start inside a new product through MVP development, and their hosting and running costs sit with cloud consulting services for startups. If the product itself was built with AI coding tools, read our answer to "What is vibe coding?" and consider a vibe code audit before adding model features on top.

  • MVP development

    Scope the smallest product that proves demand, then build it with senior engineers on code you can grow.

    Thanos

  • Cloud consulting

    Cloud architecture, migration, security and cost control on AWS, Google Cloud or Azure, sized for your next stage.

    Thanos

  • AI code fixes

    Senior review and repair for apps built with AI coding tools: security, data and scale problems found, ranked and fixed.

    Thanos

  • Product and engineering

    Need product decisions on where AI belongs? See product development consulting with both founders.

    Roger and Thanos

09INSIGHTS

Engineering guides from Thanos

  • AI CODE FIXINGGUIDE

    How to measure and reduce technical debt

    How to measure technical debt, the types that hurt startups most, and a step-by-step way to reduce technical debt while you keep shipping features.

    Thanos Gkanas

  • AI CODE FIXINGGUIDE

    Is vibe coding bad? What breaks when AI-written code reaches users

    Vibe coding is fast until real users arrive. What breaks when AI-written code ships, which apps are fine to build this way and the checks that catch problems early.

    Thanos Gkanas

  • STARTUP ENGINEERINGGUIDE

    The MVP development process, step by step

    The MVP development process in six steps, from the riskiest assumption to a launch decision, with MVP testing methods and minimum viable product examples.

    Thanos Gkanas

10FAQ

AI development questions

What do AI integration services include?

AI integration services include scoping the feature, choosing a model and provider, connecting it to your data and APIs, and building retrieval, agents or chat as needed. Good integration also covers evaluation, cost limits, failure handling, data protection and monitoring, so the feature keeps working after launch.

How do you add AI to an existing product?

Start with one task your users already do that is slow or repetitive, and define what a good result looks like. Then build the feature behind a flag, test it against real examples, release it to a small group of users and widen it once quality and cost hold up.

Can you build and integrate AI agents?

Yes. We build AI agents that call your APIs and tools, with narrow permissions, limits on steps and spending, and a log of every action. Anything irreversible, such as sending money or deleting data, waits for human approval, and every agent is tested against realistic tasks before release.

How do you keep LLM costs under control?

We measure the cost of each request, then cut it. That means shorter prompts, caching for repeated answers, smaller models for simple steps and the largest model only where it earns its price. Usage limits per user and per account, plus alerts on spend, stop a bad day from becoming a large bill.

How do you test that an AI feature gives correct answers?

We build an evaluation set from real examples with agreed correct answers, then score the feature against it automatically on every change to prompts, data or models. Some checks are exact matches, others use a rubric or human review. You see accuracy before launch and can spot drift afterwards.

Is our customer data safe when we use an AI provider?

It can be, if you set it up deliberately. Check that your provider plan does not train on your data, choose the shortest log retention available, send only the data each request needs and redact personal details. We review these settings and the provider's terms as part of every integration.

Should we add a chatbot or a narrower AI feature?

Usually start narrower. A focused feature, such as summarizing a ticket or drafting a reply, is easier to test, cheaper to run and simpler to trust. A general chatbot makes sense when users need open-ended help across a large body of content that you control and can ground answers in.

Led by Thanos GkanasCo-Founder, engineering

Talk to Thanos about AI integration

20-minute intro call

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NEXT STEP

Ship AI that works

Book a 20-minute call, or tell us what the feature should do and who will use it. We will tell you what it takes to build, test and run it.

You’ll talk to Thanos

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